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The SemiPar PackageApril 4 2005
Version 10-1
Title Semiparametic Regression
Author Matt Wand ltwandmathsunsweduaugt
Maintainer Matt Wand ltwandmathsunsweduaugt
Depends cluster nlme MASS
Description Functions for semiparametric regression analysis to complement the book Ruppert DWand MP and Carroll RJ (2003) Semiparametric Regression Cambridge University Press
License GPL (version 2 or later)
URL httpwwwmathsunsweduau~wandSPmanupdf
R topics documented
SemiPar-internal 2ageincome 2bpd 3califairpoll 4copper 4electemp 5ethanol 6fittedspm 7fossil 8fuelframe 8janka 9lidar 10linesspm 11milanmort 12monitormercury 13onions 14pigweights 14plotspm 15predictspm 16printspm 17ragweed 18residualsspm 19retireplan 20
1
2 ageincome
salinity 21sausage 22scallop 23sitka 23spm 24summaryspm 26termstructure 27tradeunion 28ustemp 29
Index 30
SemiPar-internal Internal SemiPar Objects
Description
Internal SemiPar functions
Details
These are not to be called by the user
ageincome Ageincome data
Description
Theageincome data frame has 205 pairs observations on Canadian workers from a 1971 Cana-dian Census Public Use Tape (Ullah 1985)
Usage
data(ageincome)
Format
This data frame contains the following columns
age age in years
logincome logarithm of income
Source
Ullah A (1985) Specification analysis of econometric modelsJournal of Quantitative Eco-nomics 2 187-209
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
bpd 3
Examples
library(SemiPar)data(ageincome)attach(ageincome)plot(agelogincome)
bpd Bronchopulmonary dysplasia data
Description
Thebpd data frame has data on 223 human babies
Usage
data(bpd)
Format
This data frame contains the following columns
birthweight birthweight of baby (grammes)
BPD an indicator of presence of bronchopulmonary dysplasia (BPD) 0=absent 1=present
Source
Pagano M and Gauvreau K (1993)Principles of Biostatistics Duxbury Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(bpd)attach(bpd)plot(birthweightBPD)boxplot(split(birthweightBPD)col=green)
4 copper
califairpoll California air polution data
Description
Thecalifairpoll data frame has 345 sets of observations ozone level and meteorologicalvariables in Upland California USA in 1976
Usage
data(califairpoll)
Format
This data frame contains the following columns
ozonelevelOzone concentration (ppm) at Sandburg Air Force Base
daggettpressuregradientPressure gradient at Daggett California
inversionbaseheightInversion base height feet
inversionbasetempInversion base temperature degrees Fahrenheit
Source
Brieman L and Friedman J (1985) Estimating optimal transformations for multiple regressionand correlation (with discussion)Journal of the American Statistical Association 80 580ndash619
Examples
library(SemiPar)data(califairpoll)pairs(califairpoll)
copper Copper data
Description
The copper data frame has 442 sets of observations from a simulation based on a stockpile ofmined material in the former Soviet Union Boreholes have been drilled into the dump The drillcore is cut every 5 metres and assayed for copper and cobalt content in percentage by weight
Usage
data(copper)
electemp 5
Format
This data frame contains the following columns
samplenum sample number
id sample identification number
zone zone code
xcoord x co-ordinate
ycoord y co-ordinate
zcoord z co-ordinate
grade grade measurement
corelength percentage of copper
Source
Clark I and Harper WV (2000)Practical Geostatistics 2000 Columbus Ohio Ecosse NorthAmerica Llc
Examples
library(SemiPar)data(copper)pairs(copper[47])
electemp Electricity usage and temperature data
Description
Theelectemp data frame has 55 observations on monthly electricity usage and average temper-ature for a house in Westchester County New York USA
Usage
data(electemp)
Format
This data frame contains the following columns
usage monthly electricity usage (kilowatt-hours) from a house in Westchester County New YorkUSA
temp average temperature (degrees Fahrenheit) for the corresponding month
Source
Chatterjee S Handcock M and Simonoff JS (1995)A Casebook for a First Course in Statisticsand Data Analysis New York John Wiley amp Sons
6 ethanol
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(electemp)attach(electemp)plot(usagetemp)
ethanol Ethanol data
Description
The ethanol data frame contains 88 sets of measurements for variables from an experiment in whichethanol was burned in a single cylinder automobile test engine
Usage
data(ethanol)
Format
This data frame contains the following columns
NOx the concentration of nitric oxide (NO) and nitrogen dioxide (NO2) in engine exhaust nor-malized by the work done by the engine
C the compression ratio of the engine
E the equivalence ratio at which the engine was run ndash a measure of the richness of the airethanolmix
Source
Brinkman ND (1981) Ethanol fuel ndash a single-cylinder engine study of efficiency and exhaustemissionsSAE transactionsVol 90 No 810345 1410ndash1424
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ethanol)pairs(ethanol)
fittedspm 7
fittedspm Fitted values for semiparametric regression
Description
Extracts fitted values from a semiparametric regression fit object
Usage
fittedspm(object)
Arguments
object a fittedspm object as produced byspm()
other possible arguments
Details
Extracts fitted from a semiparametric regression fit object The fitted are defined to be the set ofvalues obtained when the predictor variable data are substituted into the fitted regression model
Value
The vector of fitted
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)points(agefitted(fit)col=red)
8 fuelframe
fossil Fossil data
Description
Thefossil data frame has 106 observations on fossil shells
Usage
data(fossil)
Format
This data frame contains the following columns
age age in millions of years
strontiumratio ratios of strontium isotopes
Source
Bralower TJ Fullagar PD Paull CK Dwyer GS and Leckie RM (1997) Mid-cretaceousstrontium-isotope stratigraphy of deep-sea sectionsGeological Society of America Bulletin 1091421-1442
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(fossil)attach(fossil)plot(agestrontiumratio)
fuelframe Automobile data from consumer reports
Description
Thefuelframe data frame contains data on 5 variables (columns) for 117 cars (rows)
Usage
data(fuelframe)
janka 9
Format
This data frame contains the following columns
carname character variable giving the name (make) of the car
Weight the weight of the car in pounds
Disp the engine displacement in litres
Mileage gas mileage in milesgallon
Fuel a derived variable concerning fuel efficiency
Type a factor giving the general type of car The levels are Small Sporty Compact Medium Large Van
Source
Consumer Reports April 1990 pp 235-288
References
Chambers JM and Hastie TJ (eds) (1992)Statistical Models in SWadsworth and Brooks Pacific Grove California
Examples
library(SemiPar)data(fuelframe)pairs(fuelframe)par(mfrow=c(22))fuelfit lt- lm(Fuel ~ Weight + Dispfuelframe)plot(fuelfitask=FALSE)par(mfrow=c(11))
janka Janka hardness data
Description
The janka data frame has 36 observations on Australian timber samples
Usage
data(janka)
Format
This data frame contains the following columns
dens a measure of density of the timber
hardness the Janka hardness (structural property) of the timber
10 lidar
Source
Williams EJ (1959)Regression Analysis New York John Wiley amp Sons
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(janka)attach(janka)plot(denshardness)
lidar LIDAR data
Description
The lidar data frame has 221 observations from a light detection and ranging (LIDAR) experi-ment
Usage
data(lidar)
Format
This data frame contains the following columns
range distance travelled before the light is reflected back to its source
logratio logarithm of the ratio of received light from two laser sources
Source
Sigrist M (Ed) (1994)Air Monitoring by Spectroscopic Techniques(Chemical Analysis Seriesvol 197) New York Wiley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(lidar)attach(lidar)plot(rangelogratio)
linesspm 11
linesspm Add a curves to an existing plot
Description
Takes a fittedspm object produced byspm() and adds a curve The function is only appropriatein the case of a single predictor
Usage
linesspm(x)
Arguments
x a fittedspm object as produced byspm()
other graphics parameters described in Appendix B of the SemiPar Usersrsquo Man-ualhttpwwwmathsunsweduau~wandpapershtml
Details
Takes a fittedspm object produced byspm() and adds a curve The function is only appropriatein the case of a single predictor
Value
The function adds a curve to a plot
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fossiltype=n)lines(fit)
12 milanmort
points(fossil)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fossiltype=n)lines(fitcol=greenlwd=5shadecol=mediumpurple1)points(fossilcol=orangepch=16)par(op)
milanmort Milan mortality data
Description
Themilanmort data frame has data on 3652 consecutive days (10 consecutive years 1st Jan-uary 1980 to 30th December 1989) for the city of Milan Italy
Usage
data(milanmort)
Format
This data frame contains the following columns
daynum number of days since 31st December 1979
dayofweek 1=Monday2=Tuesday3=Wednesday4=Thursday 5=Friday6=Saturday7=Sunday
holiday indicator of public holiday 1=public holiday 0=otherwise
meantemp mean daily temperature in degrees Celcius
relhumid relative humidity
totmort total number of deaths
respmort total number of respiratory deaths
SO2 measure of sulphur dioxide level in ambient air
TSP total suspended particles in ambient air
Source
Vigotti MA Rossi G Bisanti L Zanobetti A and Schwartz J (1996) Short term effect ofurban air pollution on respiratory health in Milan Italy 1980-1989Journal of Epidemiology andCommunity Health 50 S71-S75
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
monitormercury 13
Examples
library(SemiPar)data(milanmort)pairs(milanmortpch=)
monitormercury Mercury biomonintoring data
Description
Themonitormercury data frame has 22 observations from sampling locations around a solidwaste incinerator in Warren County New Jersey USA
Usage
data(monitormercury)
Format
This data frame contains the following columns
UTMNorth longitude of sampling location
UTMEast latitude of sampling location
mercuryconcentration mercury concentration in dry sphagnum moss grown at the sampling lo-cation
Source
Opsomer JD Agras J Carpi A and Rodrigues G (1995) An application of locally weightedregression to airborne mercury deposition around an incinerator siteEnvironmetrics 6 205-221
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(monitormercury)pairs(monitormercury)
14 pigweights
onions Onions data
Description
Theonions data frame contains 84 sets of observations from an experiment involving the produc-tion of white Spanish onions in two South Australian locations
Usage
data(onions)
Format
This data frame contains the following columns
dens areal density of plants (plants per square metre)
yield onion yield (grammes per plant)
location indicator of location 0=Purnong Landing 1=Virginia
Source
Ratkowsky D A (1983)Nonlinear Regression Modeling A Unified Practical ApproachNewYork Marcel Dekker
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(onions)attach(onions)pointscols lt- c(redblue)plot(densyieldcol=pointscols[location+1]pch=16)legend(100250c(Purnong LandingVirginia)col=pointscolspch=rep(162))
pigweights Pig weight data
Description
Thepigweights data frame has 9 repeated weight measures on 48 pigs
Usage
data(pigweights)
plotspm 15
Format
This data frame contains the following columns
idnum identification number of pig
numweeks number of weeks since measurements commenced
weight bodyweight of pig idnum after numweeks weeks
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(pigweights)library(lattice)xyplot(weight~numweeksdata=pigweightsgroups=idnumtype=b)
plotspm Semiparametric regression plotting
Description
Takes a fittedspmobject produced byspm() and plots the component smooth functions that makeit up on the scale of the linear predictor
Usage
plotspm(x)
Arguments
x a fittedspm object as produced byspm()
other graphics parameters described in Appendix B of the SemiPar Usersrsquo Man-ualhttpwwwmathsunsweduau~wandpapershtml
Details
Produces plots with each panel corresponding to a component of the semiparametric regressionmodel
Value
The function generates plots
16 predictspm
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fitylim=range(strontiumratio)col=green
lwd=5shadecol=mediumpurple1rugcol=blue)points(agestrontiumratiocol=orangepch=16)par(op)
predictspm Semiparametric regression prediction
Description
Takes a fittedspm object produced byspm() and obtains predictions at new data values
Usage
predictspm(objectnewdatase)
Arguments
object a fittedspm object as produced byspm()
newdata a data frame containing the values of the predictors at which predictions arerequired The columns should have the same name as the predictors
se when this is TRUE standard error estimates are returned for each predictionThe default is FALSE
other arguments
printspm 17
Details
Takes a fittedspm object produced byspm() and obtains predictions at new data values as speci-fied by the lsquonewdatarsquo argument If lsquose=TRUErsquo then standard error estimates are also obtained
Value
If se=FALSE then a vector of predictions at lsquonewdatarsquo is returned If se=TRUE then a list withcomponents named lsquofitrsquo and lsquosersquo is returned The lsquofitrsquo component contains the predictions The lsquosersquocomponent contains standard error estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm plotspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))newdataage lt- dataframe(age=c(90100110120130))preds lt- predict(fitnewdata=newdataagese=TRUE)print(preds)
plot(fitxlim=c(90130))points(unlist(newdataage)preds$fitcol=red)points(unlist(newdataage)preds$fit+2preds$secol=blue)points(unlist(newdataage)preds$fit-2preds$secol=green)
printspm Prints semiparametric regression fit object
Description
Prints a brief description of a semiparametric regression fit object to the screen
Usage
printspm(x)
18 ragweed
Arguments
x a fittedspm object as produced byspm()
other possible arguments
Details
Prints a brief description of a semiparametric regression fit object to the screen
Value
The function prints to the screen
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))print(fit)
ragweed Ragweed data
Description
Theragweed data frame has data on ragweed levels and meteorological variables for 335 days inKalamazoo Michigan USA
Usage
data(ragweed)
residualsspm 19
Format
This data frame contains the following columns
ragweed ragweed level (grains per cubic metre)
year one of 1991 1992 1993 or 1994
dayinseasday number in the current ragweed pollen season
temperature temperature of following day (degrees Fahrenheit)
rain indicator of significant rain the following day 1=at least 3 hours of steady or brief but intenserain 0=otherwise
windspeed wind speed forecast for following day (knots)
Source
Stark P C Ryan L M McDonald J L and Burge H A (1997) Using meteorologic data tomodel and predict daily ragweed pollen levelsAerobiologia 13 177-184
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ragweed)pairs(ragweedpch=)
residualsspm Residuals for semiparametric regression
Description
Extracts residuals from a semiparametric regression fit object
Usage
residualsspm(object)
Arguments
object a fittedspm object as produced byspm()
other possible arguments
Details
Extracts residuals from a semiparametric regression fit object The residuals are defined to be thedifference between the response variable and the fitted values
20 retireplan
Value
The vector of residuals
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(ageresiduals(fit))abline(00)
retireplan Retirement plan data
Description
The retireplan data frame has data on 401(k) retirement plans for employees of 92 firmsmanaged by a company code-named Best Retirement Inc (BRI)
Usage
data(retireplan)
Format
This data frame contains the following columns
contrib contribution to retirement plan at end of first year
group 1=client has group life of group health insurance policy 0=otherwise
turnover employee turnover rate
eligible number of employees eligible to participate in 401(k) plans
vest 1=plan has immediate vesting of employer contributions 0=otherwise
salinity 21
failsafe 1=plan has a fail-safe provision 0=otherwise
match percentage of contributions matched by the employer
salary average annual employee salary in dollars
estimate underwriterrsquos estimate of end-of-year contributions in dollars
susan 1=plan was sold by a sales representative who has been specifically trained to deal exclu-sively with 401(k) plans (code-named Susan Shepard)
Source
Bryant PG and Smith MA (1995)Practical data analysis case studies in business statisticsChicago Irwin
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(retireplan)pairs(retireplan)
salinity Salinity data
Description
The salinity data frame has 28 observations on hydrological measurements from PamlicoSound North Carolina USA
Usage
data(salinity)
Format
This data frame contains the following columns
salinity salinity in Pamlico Sound
laggedsalinity salinity in Pamlico Sound during the previous six weeks
trend trend=1 if the data is the first six-week period of the spring and so forth Used to detectpossible effects of the seasonal warming trend
discharge discharge of fresh water from rivers into the sound
Source
Ruppert D and Carroll RJ (1980) Trimmed least squares estimation in the linear modelJournalof the American Statistical Association 75 828-838
22 sausage
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(salinity)pairs(salinity)
sausage Sausage data
Description
Thesausage data frame has data on 54 lsquohot dogrsquo sausages
Usage
data(sausage)
Format
This data frame contains the following columns
type type of meat
calories number of calories
sodium measure of sodium content
Source
Moore DS and McCabe GP (2003)Introduction to the Practice of Statistics Fourth EditionWH Freeman and Company
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sausage)attach(sausage)pointscols lt- c(redbluegreen)plot(sodiumcaloriescol=pointscols[type]pch=16)legend(200180c(beefporkpoultry)col=pointscolspch=rep(163))
scallop 23
scallop Scallop abundance data
Description
Thescallop data frame has 148 triplets concerning scallop abundance based on a 1990 surveycruise in the Atlantic continental shelf off Long Island New York USA
Usage
data(scallop)
Format
This data frame contains the following columns
latitude degrees latitude (north of the Equator)
longitude degrees longitude (west of Greenwich)
totcatch size of scallop catch at location specified by latitude and longitude
Source
Ecker MD and Heltshe JF (1994) Geostatistical estimates of scallop abundance InCase Studiesin BiometryLange N Ryan L Billard L Brillinger D Conquest L and Greenhouse J (eds)New York John Wiley amp Sons 107-124
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(scallop)pairs(scallop)
sitka Sitka spruce data
Description
Thesitka data frame contains measurements of log-size for 79 Sitka spruce trees grown in nor-mal or ozone-enriched environments Within each year the data are organised in four blocks cor-responding to four controlled environment chambers The first two chambers containing 27 treeseach have an ozone-enriched atmosphere the remaining two containing 12 and 13 trees respec-tively have a normal (control) atmosphere
24 spm
Usage
data(sitka)
Format
This data frame contains the following columns
idnum identification number of tree
order time order ranking within each tree
days time in days since 1st January 1988
logsize tree size measured on a logarithmic scale
ozone indicator ozone treatment 0=control1=ozone
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sitka)attach(sitka)library(lattice)ozonechar lt- rep(controlnrow(sitka))ozonechar[ozone==1] lt- ozonexyplot(logsize~days|ozonechardata=sitkagroups=idnumtype=b)
spm Fit a SemiParametric regression Model
Description
spm is used to fit semiparametric regression models using the mixed model representation of pe-nalized splines (per Ruppert Wand and Carroll 2003)
Usage
spm(formrandom=NULLgroup=NULLfamily=gaussiansparmethod=REMLomitmissing=NULL)
spm 25
Arguments
form a formula describing the model to be fit Note that an intercept is always in-cluded whether given in the formula or not
random random= 1 specifies inclusion of a random intercept according to the groupsspecified by the group argument
group a vector of labels for specifying groups
family for specification of the type of likelihood model assumed in the fitting May begaussianbinomial or poisson
sparmethod method for automatic smoothing parameter selection May be REML (re-stricted maximum likelihood) or ML (maximum likelihood)
omitmissing a logical value indicating whether fields with missing values are to be omitted
Details
See the SemiPar Usersrsquo Manual for details and examples
Value
An list object of classspm containing the fitted model The components are
fit mimics fit object of lme() for family=gaussian and glmmPQL() for family=binomialor family=poisson
info information about the inputs
aux auxiliary information such as variability estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
gam (in package lsquomgcvrsquo)lme (in package lsquonlmersquo)glmmPQL(in package lsquoMASSrsquo)plotspmsummaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)summary(fit)
26 summaryspm
data(califairpoll)attach(califairpoll)fit lt- spm(ozonelevel ~ f(daggettpressuregradient)+
f(inversionbaseheight) +f(inversionbasetemp))
summary(fit)par(mfrow=c(22))plot(fit)
The SemiPar User Manual contains several other examples and details of plotting parameters The current version of the manual is posted on the web-site wwwmathsunsweduau~wandpapershtml
summaryspm Semiparametric regression summary
Description
Takes a fittedspm object produced byspm() and summarises the fit
Usage
summaryspm(object)
Arguments
object a fittedspm object as produced byspm()
other arguments
Details
Produces tables for the linear (parametric) and non-linear (nonparametric) components The lineartable provides coefficient estimates standard errors and p-values The non-linear table providesdegrees of freedom values and other information
Value
The function generates summary tables
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
2 ageincome
salinity 21sausage 22scallop 23sitka 23spm 24summaryspm 26termstructure 27tradeunion 28ustemp 29
Index 30
SemiPar-internal Internal SemiPar Objects
Description
Internal SemiPar functions
Details
These are not to be called by the user
ageincome Ageincome data
Description
Theageincome data frame has 205 pairs observations on Canadian workers from a 1971 Cana-dian Census Public Use Tape (Ullah 1985)
Usage
data(ageincome)
Format
This data frame contains the following columns
age age in years
logincome logarithm of income
Source
Ullah A (1985) Specification analysis of econometric modelsJournal of Quantitative Eco-nomics 2 187-209
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
bpd 3
Examples
library(SemiPar)data(ageincome)attach(ageincome)plot(agelogincome)
bpd Bronchopulmonary dysplasia data
Description
Thebpd data frame has data on 223 human babies
Usage
data(bpd)
Format
This data frame contains the following columns
birthweight birthweight of baby (grammes)
BPD an indicator of presence of bronchopulmonary dysplasia (BPD) 0=absent 1=present
Source
Pagano M and Gauvreau K (1993)Principles of Biostatistics Duxbury Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(bpd)attach(bpd)plot(birthweightBPD)boxplot(split(birthweightBPD)col=green)
4 copper
califairpoll California air polution data
Description
Thecalifairpoll data frame has 345 sets of observations ozone level and meteorologicalvariables in Upland California USA in 1976
Usage
data(califairpoll)
Format
This data frame contains the following columns
ozonelevelOzone concentration (ppm) at Sandburg Air Force Base
daggettpressuregradientPressure gradient at Daggett California
inversionbaseheightInversion base height feet
inversionbasetempInversion base temperature degrees Fahrenheit
Source
Brieman L and Friedman J (1985) Estimating optimal transformations for multiple regressionand correlation (with discussion)Journal of the American Statistical Association 80 580ndash619
Examples
library(SemiPar)data(califairpoll)pairs(califairpoll)
copper Copper data
Description
The copper data frame has 442 sets of observations from a simulation based on a stockpile ofmined material in the former Soviet Union Boreholes have been drilled into the dump The drillcore is cut every 5 metres and assayed for copper and cobalt content in percentage by weight
Usage
data(copper)
electemp 5
Format
This data frame contains the following columns
samplenum sample number
id sample identification number
zone zone code
xcoord x co-ordinate
ycoord y co-ordinate
zcoord z co-ordinate
grade grade measurement
corelength percentage of copper
Source
Clark I and Harper WV (2000)Practical Geostatistics 2000 Columbus Ohio Ecosse NorthAmerica Llc
Examples
library(SemiPar)data(copper)pairs(copper[47])
electemp Electricity usage and temperature data
Description
Theelectemp data frame has 55 observations on monthly electricity usage and average temper-ature for a house in Westchester County New York USA
Usage
data(electemp)
Format
This data frame contains the following columns
usage monthly electricity usage (kilowatt-hours) from a house in Westchester County New YorkUSA
temp average temperature (degrees Fahrenheit) for the corresponding month
Source
Chatterjee S Handcock M and Simonoff JS (1995)A Casebook for a First Course in Statisticsand Data Analysis New York John Wiley amp Sons
6 ethanol
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(electemp)attach(electemp)plot(usagetemp)
ethanol Ethanol data
Description
The ethanol data frame contains 88 sets of measurements for variables from an experiment in whichethanol was burned in a single cylinder automobile test engine
Usage
data(ethanol)
Format
This data frame contains the following columns
NOx the concentration of nitric oxide (NO) and nitrogen dioxide (NO2) in engine exhaust nor-malized by the work done by the engine
C the compression ratio of the engine
E the equivalence ratio at which the engine was run ndash a measure of the richness of the airethanolmix
Source
Brinkman ND (1981) Ethanol fuel ndash a single-cylinder engine study of efficiency and exhaustemissionsSAE transactionsVol 90 No 810345 1410ndash1424
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ethanol)pairs(ethanol)
fittedspm 7
fittedspm Fitted values for semiparametric regression
Description
Extracts fitted values from a semiparametric regression fit object
Usage
fittedspm(object)
Arguments
object a fittedspm object as produced byspm()
other possible arguments
Details
Extracts fitted from a semiparametric regression fit object The fitted are defined to be the set ofvalues obtained when the predictor variable data are substituted into the fitted regression model
Value
The vector of fitted
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)points(agefitted(fit)col=red)
8 fuelframe
fossil Fossil data
Description
Thefossil data frame has 106 observations on fossil shells
Usage
data(fossil)
Format
This data frame contains the following columns
age age in millions of years
strontiumratio ratios of strontium isotopes
Source
Bralower TJ Fullagar PD Paull CK Dwyer GS and Leckie RM (1997) Mid-cretaceousstrontium-isotope stratigraphy of deep-sea sectionsGeological Society of America Bulletin 1091421-1442
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(fossil)attach(fossil)plot(agestrontiumratio)
fuelframe Automobile data from consumer reports
Description
Thefuelframe data frame contains data on 5 variables (columns) for 117 cars (rows)
Usage
data(fuelframe)
janka 9
Format
This data frame contains the following columns
carname character variable giving the name (make) of the car
Weight the weight of the car in pounds
Disp the engine displacement in litres
Mileage gas mileage in milesgallon
Fuel a derived variable concerning fuel efficiency
Type a factor giving the general type of car The levels are Small Sporty Compact Medium Large Van
Source
Consumer Reports April 1990 pp 235-288
References
Chambers JM and Hastie TJ (eds) (1992)Statistical Models in SWadsworth and Brooks Pacific Grove California
Examples
library(SemiPar)data(fuelframe)pairs(fuelframe)par(mfrow=c(22))fuelfit lt- lm(Fuel ~ Weight + Dispfuelframe)plot(fuelfitask=FALSE)par(mfrow=c(11))
janka Janka hardness data
Description
The janka data frame has 36 observations on Australian timber samples
Usage
data(janka)
Format
This data frame contains the following columns
dens a measure of density of the timber
hardness the Janka hardness (structural property) of the timber
10 lidar
Source
Williams EJ (1959)Regression Analysis New York John Wiley amp Sons
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(janka)attach(janka)plot(denshardness)
lidar LIDAR data
Description
The lidar data frame has 221 observations from a light detection and ranging (LIDAR) experi-ment
Usage
data(lidar)
Format
This data frame contains the following columns
range distance travelled before the light is reflected back to its source
logratio logarithm of the ratio of received light from two laser sources
Source
Sigrist M (Ed) (1994)Air Monitoring by Spectroscopic Techniques(Chemical Analysis Seriesvol 197) New York Wiley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(lidar)attach(lidar)plot(rangelogratio)
linesspm 11
linesspm Add a curves to an existing plot
Description
Takes a fittedspm object produced byspm() and adds a curve The function is only appropriatein the case of a single predictor
Usage
linesspm(x)
Arguments
x a fittedspm object as produced byspm()
other graphics parameters described in Appendix B of the SemiPar Usersrsquo Man-ualhttpwwwmathsunsweduau~wandpapershtml
Details
Takes a fittedspm object produced byspm() and adds a curve The function is only appropriatein the case of a single predictor
Value
The function adds a curve to a plot
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fossiltype=n)lines(fit)
12 milanmort
points(fossil)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fossiltype=n)lines(fitcol=greenlwd=5shadecol=mediumpurple1)points(fossilcol=orangepch=16)par(op)
milanmort Milan mortality data
Description
Themilanmort data frame has data on 3652 consecutive days (10 consecutive years 1st Jan-uary 1980 to 30th December 1989) for the city of Milan Italy
Usage
data(milanmort)
Format
This data frame contains the following columns
daynum number of days since 31st December 1979
dayofweek 1=Monday2=Tuesday3=Wednesday4=Thursday 5=Friday6=Saturday7=Sunday
holiday indicator of public holiday 1=public holiday 0=otherwise
meantemp mean daily temperature in degrees Celcius
relhumid relative humidity
totmort total number of deaths
respmort total number of respiratory deaths
SO2 measure of sulphur dioxide level in ambient air
TSP total suspended particles in ambient air
Source
Vigotti MA Rossi G Bisanti L Zanobetti A and Schwartz J (1996) Short term effect ofurban air pollution on respiratory health in Milan Italy 1980-1989Journal of Epidemiology andCommunity Health 50 S71-S75
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
monitormercury 13
Examples
library(SemiPar)data(milanmort)pairs(milanmortpch=)
monitormercury Mercury biomonintoring data
Description
Themonitormercury data frame has 22 observations from sampling locations around a solidwaste incinerator in Warren County New Jersey USA
Usage
data(monitormercury)
Format
This data frame contains the following columns
UTMNorth longitude of sampling location
UTMEast latitude of sampling location
mercuryconcentration mercury concentration in dry sphagnum moss grown at the sampling lo-cation
Source
Opsomer JD Agras J Carpi A and Rodrigues G (1995) An application of locally weightedregression to airborne mercury deposition around an incinerator siteEnvironmetrics 6 205-221
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(monitormercury)pairs(monitormercury)
14 pigweights
onions Onions data
Description
Theonions data frame contains 84 sets of observations from an experiment involving the produc-tion of white Spanish onions in two South Australian locations
Usage
data(onions)
Format
This data frame contains the following columns
dens areal density of plants (plants per square metre)
yield onion yield (grammes per plant)
location indicator of location 0=Purnong Landing 1=Virginia
Source
Ratkowsky D A (1983)Nonlinear Regression Modeling A Unified Practical ApproachNewYork Marcel Dekker
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(onions)attach(onions)pointscols lt- c(redblue)plot(densyieldcol=pointscols[location+1]pch=16)legend(100250c(Purnong LandingVirginia)col=pointscolspch=rep(162))
pigweights Pig weight data
Description
Thepigweights data frame has 9 repeated weight measures on 48 pigs
Usage
data(pigweights)
plotspm 15
Format
This data frame contains the following columns
idnum identification number of pig
numweeks number of weeks since measurements commenced
weight bodyweight of pig idnum after numweeks weeks
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(pigweights)library(lattice)xyplot(weight~numweeksdata=pigweightsgroups=idnumtype=b)
plotspm Semiparametric regression plotting
Description
Takes a fittedspmobject produced byspm() and plots the component smooth functions that makeit up on the scale of the linear predictor
Usage
plotspm(x)
Arguments
x a fittedspm object as produced byspm()
other graphics parameters described in Appendix B of the SemiPar Usersrsquo Man-ualhttpwwwmathsunsweduau~wandpapershtml
Details
Produces plots with each panel corresponding to a component of the semiparametric regressionmodel
Value
The function generates plots
16 predictspm
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fitylim=range(strontiumratio)col=green
lwd=5shadecol=mediumpurple1rugcol=blue)points(agestrontiumratiocol=orangepch=16)par(op)
predictspm Semiparametric regression prediction
Description
Takes a fittedspm object produced byspm() and obtains predictions at new data values
Usage
predictspm(objectnewdatase)
Arguments
object a fittedspm object as produced byspm()
newdata a data frame containing the values of the predictors at which predictions arerequired The columns should have the same name as the predictors
se when this is TRUE standard error estimates are returned for each predictionThe default is FALSE
other arguments
printspm 17
Details
Takes a fittedspm object produced byspm() and obtains predictions at new data values as speci-fied by the lsquonewdatarsquo argument If lsquose=TRUErsquo then standard error estimates are also obtained
Value
If se=FALSE then a vector of predictions at lsquonewdatarsquo is returned If se=TRUE then a list withcomponents named lsquofitrsquo and lsquosersquo is returned The lsquofitrsquo component contains the predictions The lsquosersquocomponent contains standard error estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm plotspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))newdataage lt- dataframe(age=c(90100110120130))preds lt- predict(fitnewdata=newdataagese=TRUE)print(preds)
plot(fitxlim=c(90130))points(unlist(newdataage)preds$fitcol=red)points(unlist(newdataage)preds$fit+2preds$secol=blue)points(unlist(newdataage)preds$fit-2preds$secol=green)
printspm Prints semiparametric regression fit object
Description
Prints a brief description of a semiparametric regression fit object to the screen
Usage
printspm(x)
18 ragweed
Arguments
x a fittedspm object as produced byspm()
other possible arguments
Details
Prints a brief description of a semiparametric regression fit object to the screen
Value
The function prints to the screen
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))print(fit)
ragweed Ragweed data
Description
Theragweed data frame has data on ragweed levels and meteorological variables for 335 days inKalamazoo Michigan USA
Usage
data(ragweed)
residualsspm 19
Format
This data frame contains the following columns
ragweed ragweed level (grains per cubic metre)
year one of 1991 1992 1993 or 1994
dayinseasday number in the current ragweed pollen season
temperature temperature of following day (degrees Fahrenheit)
rain indicator of significant rain the following day 1=at least 3 hours of steady or brief but intenserain 0=otherwise
windspeed wind speed forecast for following day (knots)
Source
Stark P C Ryan L M McDonald J L and Burge H A (1997) Using meteorologic data tomodel and predict daily ragweed pollen levelsAerobiologia 13 177-184
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ragweed)pairs(ragweedpch=)
residualsspm Residuals for semiparametric regression
Description
Extracts residuals from a semiparametric regression fit object
Usage
residualsspm(object)
Arguments
object a fittedspm object as produced byspm()
other possible arguments
Details
Extracts residuals from a semiparametric regression fit object The residuals are defined to be thedifference between the response variable and the fitted values
20 retireplan
Value
The vector of residuals
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(ageresiduals(fit))abline(00)
retireplan Retirement plan data
Description
The retireplan data frame has data on 401(k) retirement plans for employees of 92 firmsmanaged by a company code-named Best Retirement Inc (BRI)
Usage
data(retireplan)
Format
This data frame contains the following columns
contrib contribution to retirement plan at end of first year
group 1=client has group life of group health insurance policy 0=otherwise
turnover employee turnover rate
eligible number of employees eligible to participate in 401(k) plans
vest 1=plan has immediate vesting of employer contributions 0=otherwise
salinity 21
failsafe 1=plan has a fail-safe provision 0=otherwise
match percentage of contributions matched by the employer
salary average annual employee salary in dollars
estimate underwriterrsquos estimate of end-of-year contributions in dollars
susan 1=plan was sold by a sales representative who has been specifically trained to deal exclu-sively with 401(k) plans (code-named Susan Shepard)
Source
Bryant PG and Smith MA (1995)Practical data analysis case studies in business statisticsChicago Irwin
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(retireplan)pairs(retireplan)
salinity Salinity data
Description
The salinity data frame has 28 observations on hydrological measurements from PamlicoSound North Carolina USA
Usage
data(salinity)
Format
This data frame contains the following columns
salinity salinity in Pamlico Sound
laggedsalinity salinity in Pamlico Sound during the previous six weeks
trend trend=1 if the data is the first six-week period of the spring and so forth Used to detectpossible effects of the seasonal warming trend
discharge discharge of fresh water from rivers into the sound
Source
Ruppert D and Carroll RJ (1980) Trimmed least squares estimation in the linear modelJournalof the American Statistical Association 75 828-838
22 sausage
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(salinity)pairs(salinity)
sausage Sausage data
Description
Thesausage data frame has data on 54 lsquohot dogrsquo sausages
Usage
data(sausage)
Format
This data frame contains the following columns
type type of meat
calories number of calories
sodium measure of sodium content
Source
Moore DS and McCabe GP (2003)Introduction to the Practice of Statistics Fourth EditionWH Freeman and Company
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sausage)attach(sausage)pointscols lt- c(redbluegreen)plot(sodiumcaloriescol=pointscols[type]pch=16)legend(200180c(beefporkpoultry)col=pointscolspch=rep(163))
scallop 23
scallop Scallop abundance data
Description
Thescallop data frame has 148 triplets concerning scallop abundance based on a 1990 surveycruise in the Atlantic continental shelf off Long Island New York USA
Usage
data(scallop)
Format
This data frame contains the following columns
latitude degrees latitude (north of the Equator)
longitude degrees longitude (west of Greenwich)
totcatch size of scallop catch at location specified by latitude and longitude
Source
Ecker MD and Heltshe JF (1994) Geostatistical estimates of scallop abundance InCase Studiesin BiometryLange N Ryan L Billard L Brillinger D Conquest L and Greenhouse J (eds)New York John Wiley amp Sons 107-124
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(scallop)pairs(scallop)
sitka Sitka spruce data
Description
Thesitka data frame contains measurements of log-size for 79 Sitka spruce trees grown in nor-mal or ozone-enriched environments Within each year the data are organised in four blocks cor-responding to four controlled environment chambers The first two chambers containing 27 treeseach have an ozone-enriched atmosphere the remaining two containing 12 and 13 trees respec-tively have a normal (control) atmosphere
24 spm
Usage
data(sitka)
Format
This data frame contains the following columns
idnum identification number of tree
order time order ranking within each tree
days time in days since 1st January 1988
logsize tree size measured on a logarithmic scale
ozone indicator ozone treatment 0=control1=ozone
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sitka)attach(sitka)library(lattice)ozonechar lt- rep(controlnrow(sitka))ozonechar[ozone==1] lt- ozonexyplot(logsize~days|ozonechardata=sitkagroups=idnumtype=b)
spm Fit a SemiParametric regression Model
Description
spm is used to fit semiparametric regression models using the mixed model representation of pe-nalized splines (per Ruppert Wand and Carroll 2003)
Usage
spm(formrandom=NULLgroup=NULLfamily=gaussiansparmethod=REMLomitmissing=NULL)
spm 25
Arguments
form a formula describing the model to be fit Note that an intercept is always in-cluded whether given in the formula or not
random random= 1 specifies inclusion of a random intercept according to the groupsspecified by the group argument
group a vector of labels for specifying groups
family for specification of the type of likelihood model assumed in the fitting May begaussianbinomial or poisson
sparmethod method for automatic smoothing parameter selection May be REML (re-stricted maximum likelihood) or ML (maximum likelihood)
omitmissing a logical value indicating whether fields with missing values are to be omitted
Details
See the SemiPar Usersrsquo Manual for details and examples
Value
An list object of classspm containing the fitted model The components are
fit mimics fit object of lme() for family=gaussian and glmmPQL() for family=binomialor family=poisson
info information about the inputs
aux auxiliary information such as variability estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
gam (in package lsquomgcvrsquo)lme (in package lsquonlmersquo)glmmPQL(in package lsquoMASSrsquo)plotspmsummaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)summary(fit)
26 summaryspm
data(califairpoll)attach(califairpoll)fit lt- spm(ozonelevel ~ f(daggettpressuregradient)+
f(inversionbaseheight) +f(inversionbasetemp))
summary(fit)par(mfrow=c(22))plot(fit)
The SemiPar User Manual contains several other examples and details of plotting parameters The current version of the manual is posted on the web-site wwwmathsunsweduau~wandpapershtml
summaryspm Semiparametric regression summary
Description
Takes a fittedspm object produced byspm() and summarises the fit
Usage
summaryspm(object)
Arguments
object a fittedspm object as produced byspm()
other arguments
Details
Produces tables for the linear (parametric) and non-linear (nonparametric) components The lineartable provides coefficient estimates standard errors and p-values The non-linear table providesdegrees of freedom values and other information
Value
The function generates summary tables
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
bpd 3
Examples
library(SemiPar)data(ageincome)attach(ageincome)plot(agelogincome)
bpd Bronchopulmonary dysplasia data
Description
Thebpd data frame has data on 223 human babies
Usage
data(bpd)
Format
This data frame contains the following columns
birthweight birthweight of baby (grammes)
BPD an indicator of presence of bronchopulmonary dysplasia (BPD) 0=absent 1=present
Source
Pagano M and Gauvreau K (1993)Principles of Biostatistics Duxbury Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(bpd)attach(bpd)plot(birthweightBPD)boxplot(split(birthweightBPD)col=green)
4 copper
califairpoll California air polution data
Description
Thecalifairpoll data frame has 345 sets of observations ozone level and meteorologicalvariables in Upland California USA in 1976
Usage
data(califairpoll)
Format
This data frame contains the following columns
ozonelevelOzone concentration (ppm) at Sandburg Air Force Base
daggettpressuregradientPressure gradient at Daggett California
inversionbaseheightInversion base height feet
inversionbasetempInversion base temperature degrees Fahrenheit
Source
Brieman L and Friedman J (1985) Estimating optimal transformations for multiple regressionand correlation (with discussion)Journal of the American Statistical Association 80 580ndash619
Examples
library(SemiPar)data(califairpoll)pairs(califairpoll)
copper Copper data
Description
The copper data frame has 442 sets of observations from a simulation based on a stockpile ofmined material in the former Soviet Union Boreholes have been drilled into the dump The drillcore is cut every 5 metres and assayed for copper and cobalt content in percentage by weight
Usage
data(copper)
electemp 5
Format
This data frame contains the following columns
samplenum sample number
id sample identification number
zone zone code
xcoord x co-ordinate
ycoord y co-ordinate
zcoord z co-ordinate
grade grade measurement
corelength percentage of copper
Source
Clark I and Harper WV (2000)Practical Geostatistics 2000 Columbus Ohio Ecosse NorthAmerica Llc
Examples
library(SemiPar)data(copper)pairs(copper[47])
electemp Electricity usage and temperature data
Description
Theelectemp data frame has 55 observations on monthly electricity usage and average temper-ature for a house in Westchester County New York USA
Usage
data(electemp)
Format
This data frame contains the following columns
usage monthly electricity usage (kilowatt-hours) from a house in Westchester County New YorkUSA
temp average temperature (degrees Fahrenheit) for the corresponding month
Source
Chatterjee S Handcock M and Simonoff JS (1995)A Casebook for a First Course in Statisticsand Data Analysis New York John Wiley amp Sons
6 ethanol
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(electemp)attach(electemp)plot(usagetemp)
ethanol Ethanol data
Description
The ethanol data frame contains 88 sets of measurements for variables from an experiment in whichethanol was burned in a single cylinder automobile test engine
Usage
data(ethanol)
Format
This data frame contains the following columns
NOx the concentration of nitric oxide (NO) and nitrogen dioxide (NO2) in engine exhaust nor-malized by the work done by the engine
C the compression ratio of the engine
E the equivalence ratio at which the engine was run ndash a measure of the richness of the airethanolmix
Source
Brinkman ND (1981) Ethanol fuel ndash a single-cylinder engine study of efficiency and exhaustemissionsSAE transactionsVol 90 No 810345 1410ndash1424
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ethanol)pairs(ethanol)
fittedspm 7
fittedspm Fitted values for semiparametric regression
Description
Extracts fitted values from a semiparametric regression fit object
Usage
fittedspm(object)
Arguments
object a fittedspm object as produced byspm()
other possible arguments
Details
Extracts fitted from a semiparametric regression fit object The fitted are defined to be the set ofvalues obtained when the predictor variable data are substituted into the fitted regression model
Value
The vector of fitted
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)points(agefitted(fit)col=red)
8 fuelframe
fossil Fossil data
Description
Thefossil data frame has 106 observations on fossil shells
Usage
data(fossil)
Format
This data frame contains the following columns
age age in millions of years
strontiumratio ratios of strontium isotopes
Source
Bralower TJ Fullagar PD Paull CK Dwyer GS and Leckie RM (1997) Mid-cretaceousstrontium-isotope stratigraphy of deep-sea sectionsGeological Society of America Bulletin 1091421-1442
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(fossil)attach(fossil)plot(agestrontiumratio)
fuelframe Automobile data from consumer reports
Description
Thefuelframe data frame contains data on 5 variables (columns) for 117 cars (rows)
Usage
data(fuelframe)
janka 9
Format
This data frame contains the following columns
carname character variable giving the name (make) of the car
Weight the weight of the car in pounds
Disp the engine displacement in litres
Mileage gas mileage in milesgallon
Fuel a derived variable concerning fuel efficiency
Type a factor giving the general type of car The levels are Small Sporty Compact Medium Large Van
Source
Consumer Reports April 1990 pp 235-288
References
Chambers JM and Hastie TJ (eds) (1992)Statistical Models in SWadsworth and Brooks Pacific Grove California
Examples
library(SemiPar)data(fuelframe)pairs(fuelframe)par(mfrow=c(22))fuelfit lt- lm(Fuel ~ Weight + Dispfuelframe)plot(fuelfitask=FALSE)par(mfrow=c(11))
janka Janka hardness data
Description
The janka data frame has 36 observations on Australian timber samples
Usage
data(janka)
Format
This data frame contains the following columns
dens a measure of density of the timber
hardness the Janka hardness (structural property) of the timber
10 lidar
Source
Williams EJ (1959)Regression Analysis New York John Wiley amp Sons
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(janka)attach(janka)plot(denshardness)
lidar LIDAR data
Description
The lidar data frame has 221 observations from a light detection and ranging (LIDAR) experi-ment
Usage
data(lidar)
Format
This data frame contains the following columns
range distance travelled before the light is reflected back to its source
logratio logarithm of the ratio of received light from two laser sources
Source
Sigrist M (Ed) (1994)Air Monitoring by Spectroscopic Techniques(Chemical Analysis Seriesvol 197) New York Wiley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(lidar)attach(lidar)plot(rangelogratio)
linesspm 11
linesspm Add a curves to an existing plot
Description
Takes a fittedspm object produced byspm() and adds a curve The function is only appropriatein the case of a single predictor
Usage
linesspm(x)
Arguments
x a fittedspm object as produced byspm()
other graphics parameters described in Appendix B of the SemiPar Usersrsquo Man-ualhttpwwwmathsunsweduau~wandpapershtml
Details
Takes a fittedspm object produced byspm() and adds a curve The function is only appropriatein the case of a single predictor
Value
The function adds a curve to a plot
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fossiltype=n)lines(fit)
12 milanmort
points(fossil)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fossiltype=n)lines(fitcol=greenlwd=5shadecol=mediumpurple1)points(fossilcol=orangepch=16)par(op)
milanmort Milan mortality data
Description
Themilanmort data frame has data on 3652 consecutive days (10 consecutive years 1st Jan-uary 1980 to 30th December 1989) for the city of Milan Italy
Usage
data(milanmort)
Format
This data frame contains the following columns
daynum number of days since 31st December 1979
dayofweek 1=Monday2=Tuesday3=Wednesday4=Thursday 5=Friday6=Saturday7=Sunday
holiday indicator of public holiday 1=public holiday 0=otherwise
meantemp mean daily temperature in degrees Celcius
relhumid relative humidity
totmort total number of deaths
respmort total number of respiratory deaths
SO2 measure of sulphur dioxide level in ambient air
TSP total suspended particles in ambient air
Source
Vigotti MA Rossi G Bisanti L Zanobetti A and Schwartz J (1996) Short term effect ofurban air pollution on respiratory health in Milan Italy 1980-1989Journal of Epidemiology andCommunity Health 50 S71-S75
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
monitormercury 13
Examples
library(SemiPar)data(milanmort)pairs(milanmortpch=)
monitormercury Mercury biomonintoring data
Description
Themonitormercury data frame has 22 observations from sampling locations around a solidwaste incinerator in Warren County New Jersey USA
Usage
data(monitormercury)
Format
This data frame contains the following columns
UTMNorth longitude of sampling location
UTMEast latitude of sampling location
mercuryconcentration mercury concentration in dry sphagnum moss grown at the sampling lo-cation
Source
Opsomer JD Agras J Carpi A and Rodrigues G (1995) An application of locally weightedregression to airborne mercury deposition around an incinerator siteEnvironmetrics 6 205-221
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(monitormercury)pairs(monitormercury)
14 pigweights
onions Onions data
Description
Theonions data frame contains 84 sets of observations from an experiment involving the produc-tion of white Spanish onions in two South Australian locations
Usage
data(onions)
Format
This data frame contains the following columns
dens areal density of plants (plants per square metre)
yield onion yield (grammes per plant)
location indicator of location 0=Purnong Landing 1=Virginia
Source
Ratkowsky D A (1983)Nonlinear Regression Modeling A Unified Practical ApproachNewYork Marcel Dekker
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(onions)attach(onions)pointscols lt- c(redblue)plot(densyieldcol=pointscols[location+1]pch=16)legend(100250c(Purnong LandingVirginia)col=pointscolspch=rep(162))
pigweights Pig weight data
Description
Thepigweights data frame has 9 repeated weight measures on 48 pigs
Usage
data(pigweights)
plotspm 15
Format
This data frame contains the following columns
idnum identification number of pig
numweeks number of weeks since measurements commenced
weight bodyweight of pig idnum after numweeks weeks
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(pigweights)library(lattice)xyplot(weight~numweeksdata=pigweightsgroups=idnumtype=b)
plotspm Semiparametric regression plotting
Description
Takes a fittedspmobject produced byspm() and plots the component smooth functions that makeit up on the scale of the linear predictor
Usage
plotspm(x)
Arguments
x a fittedspm object as produced byspm()
other graphics parameters described in Appendix B of the SemiPar Usersrsquo Man-ualhttpwwwmathsunsweduau~wandpapershtml
Details
Produces plots with each panel corresponding to a component of the semiparametric regressionmodel
Value
The function generates plots
16 predictspm
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fitylim=range(strontiumratio)col=green
lwd=5shadecol=mediumpurple1rugcol=blue)points(agestrontiumratiocol=orangepch=16)par(op)
predictspm Semiparametric regression prediction
Description
Takes a fittedspm object produced byspm() and obtains predictions at new data values
Usage
predictspm(objectnewdatase)
Arguments
object a fittedspm object as produced byspm()
newdata a data frame containing the values of the predictors at which predictions arerequired The columns should have the same name as the predictors
se when this is TRUE standard error estimates are returned for each predictionThe default is FALSE
other arguments
printspm 17
Details
Takes a fittedspm object produced byspm() and obtains predictions at new data values as speci-fied by the lsquonewdatarsquo argument If lsquose=TRUErsquo then standard error estimates are also obtained
Value
If se=FALSE then a vector of predictions at lsquonewdatarsquo is returned If se=TRUE then a list withcomponents named lsquofitrsquo and lsquosersquo is returned The lsquofitrsquo component contains the predictions The lsquosersquocomponent contains standard error estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm plotspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))newdataage lt- dataframe(age=c(90100110120130))preds lt- predict(fitnewdata=newdataagese=TRUE)print(preds)
plot(fitxlim=c(90130))points(unlist(newdataage)preds$fitcol=red)points(unlist(newdataage)preds$fit+2preds$secol=blue)points(unlist(newdataage)preds$fit-2preds$secol=green)
printspm Prints semiparametric regression fit object
Description
Prints a brief description of a semiparametric regression fit object to the screen
Usage
printspm(x)
18 ragweed
Arguments
x a fittedspm object as produced byspm()
other possible arguments
Details
Prints a brief description of a semiparametric regression fit object to the screen
Value
The function prints to the screen
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))print(fit)
ragweed Ragweed data
Description
Theragweed data frame has data on ragweed levels and meteorological variables for 335 days inKalamazoo Michigan USA
Usage
data(ragweed)
residualsspm 19
Format
This data frame contains the following columns
ragweed ragweed level (grains per cubic metre)
year one of 1991 1992 1993 or 1994
dayinseasday number in the current ragweed pollen season
temperature temperature of following day (degrees Fahrenheit)
rain indicator of significant rain the following day 1=at least 3 hours of steady or brief but intenserain 0=otherwise
windspeed wind speed forecast for following day (knots)
Source
Stark P C Ryan L M McDonald J L and Burge H A (1997) Using meteorologic data tomodel and predict daily ragweed pollen levelsAerobiologia 13 177-184
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ragweed)pairs(ragweedpch=)
residualsspm Residuals for semiparametric regression
Description
Extracts residuals from a semiparametric regression fit object
Usage
residualsspm(object)
Arguments
object a fittedspm object as produced byspm()
other possible arguments
Details
Extracts residuals from a semiparametric regression fit object The residuals are defined to be thedifference between the response variable and the fitted values
20 retireplan
Value
The vector of residuals
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(ageresiduals(fit))abline(00)
retireplan Retirement plan data
Description
The retireplan data frame has data on 401(k) retirement plans for employees of 92 firmsmanaged by a company code-named Best Retirement Inc (BRI)
Usage
data(retireplan)
Format
This data frame contains the following columns
contrib contribution to retirement plan at end of first year
group 1=client has group life of group health insurance policy 0=otherwise
turnover employee turnover rate
eligible number of employees eligible to participate in 401(k) plans
vest 1=plan has immediate vesting of employer contributions 0=otherwise
salinity 21
failsafe 1=plan has a fail-safe provision 0=otherwise
match percentage of contributions matched by the employer
salary average annual employee salary in dollars
estimate underwriterrsquos estimate of end-of-year contributions in dollars
susan 1=plan was sold by a sales representative who has been specifically trained to deal exclu-sively with 401(k) plans (code-named Susan Shepard)
Source
Bryant PG and Smith MA (1995)Practical data analysis case studies in business statisticsChicago Irwin
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(retireplan)pairs(retireplan)
salinity Salinity data
Description
The salinity data frame has 28 observations on hydrological measurements from PamlicoSound North Carolina USA
Usage
data(salinity)
Format
This data frame contains the following columns
salinity salinity in Pamlico Sound
laggedsalinity salinity in Pamlico Sound during the previous six weeks
trend trend=1 if the data is the first six-week period of the spring and so forth Used to detectpossible effects of the seasonal warming trend
discharge discharge of fresh water from rivers into the sound
Source
Ruppert D and Carroll RJ (1980) Trimmed least squares estimation in the linear modelJournalof the American Statistical Association 75 828-838
22 sausage
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(salinity)pairs(salinity)
sausage Sausage data
Description
Thesausage data frame has data on 54 lsquohot dogrsquo sausages
Usage
data(sausage)
Format
This data frame contains the following columns
type type of meat
calories number of calories
sodium measure of sodium content
Source
Moore DS and McCabe GP (2003)Introduction to the Practice of Statistics Fourth EditionWH Freeman and Company
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sausage)attach(sausage)pointscols lt- c(redbluegreen)plot(sodiumcaloriescol=pointscols[type]pch=16)legend(200180c(beefporkpoultry)col=pointscolspch=rep(163))
scallop 23
scallop Scallop abundance data
Description
Thescallop data frame has 148 triplets concerning scallop abundance based on a 1990 surveycruise in the Atlantic continental shelf off Long Island New York USA
Usage
data(scallop)
Format
This data frame contains the following columns
latitude degrees latitude (north of the Equator)
longitude degrees longitude (west of Greenwich)
totcatch size of scallop catch at location specified by latitude and longitude
Source
Ecker MD and Heltshe JF (1994) Geostatistical estimates of scallop abundance InCase Studiesin BiometryLange N Ryan L Billard L Brillinger D Conquest L and Greenhouse J (eds)New York John Wiley amp Sons 107-124
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(scallop)pairs(scallop)
sitka Sitka spruce data
Description
Thesitka data frame contains measurements of log-size for 79 Sitka spruce trees grown in nor-mal or ozone-enriched environments Within each year the data are organised in four blocks cor-responding to four controlled environment chambers The first two chambers containing 27 treeseach have an ozone-enriched atmosphere the remaining two containing 12 and 13 trees respec-tively have a normal (control) atmosphere
24 spm
Usage
data(sitka)
Format
This data frame contains the following columns
idnum identification number of tree
order time order ranking within each tree
days time in days since 1st January 1988
logsize tree size measured on a logarithmic scale
ozone indicator ozone treatment 0=control1=ozone
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sitka)attach(sitka)library(lattice)ozonechar lt- rep(controlnrow(sitka))ozonechar[ozone==1] lt- ozonexyplot(logsize~days|ozonechardata=sitkagroups=idnumtype=b)
spm Fit a SemiParametric regression Model
Description
spm is used to fit semiparametric regression models using the mixed model representation of pe-nalized splines (per Ruppert Wand and Carroll 2003)
Usage
spm(formrandom=NULLgroup=NULLfamily=gaussiansparmethod=REMLomitmissing=NULL)
spm 25
Arguments
form a formula describing the model to be fit Note that an intercept is always in-cluded whether given in the formula or not
random random= 1 specifies inclusion of a random intercept according to the groupsspecified by the group argument
group a vector of labels for specifying groups
family for specification of the type of likelihood model assumed in the fitting May begaussianbinomial or poisson
sparmethod method for automatic smoothing parameter selection May be REML (re-stricted maximum likelihood) or ML (maximum likelihood)
omitmissing a logical value indicating whether fields with missing values are to be omitted
Details
See the SemiPar Usersrsquo Manual for details and examples
Value
An list object of classspm containing the fitted model The components are
fit mimics fit object of lme() for family=gaussian and glmmPQL() for family=binomialor family=poisson
info information about the inputs
aux auxiliary information such as variability estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
gam (in package lsquomgcvrsquo)lme (in package lsquonlmersquo)glmmPQL(in package lsquoMASSrsquo)plotspmsummaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)summary(fit)
26 summaryspm
data(califairpoll)attach(califairpoll)fit lt- spm(ozonelevel ~ f(daggettpressuregradient)+
f(inversionbaseheight) +f(inversionbasetemp))
summary(fit)par(mfrow=c(22))plot(fit)
The SemiPar User Manual contains several other examples and details of plotting parameters The current version of the manual is posted on the web-site wwwmathsunsweduau~wandpapershtml
summaryspm Semiparametric regression summary
Description
Takes a fittedspm object produced byspm() and summarises the fit
Usage
summaryspm(object)
Arguments
object a fittedspm object as produced byspm()
other arguments
Details
Produces tables for the linear (parametric) and non-linear (nonparametric) components The lineartable provides coefficient estimates standard errors and p-values The non-linear table providesdegrees of freedom values and other information
Value
The function generates summary tables
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
4 copper
califairpoll California air polution data
Description
Thecalifairpoll data frame has 345 sets of observations ozone level and meteorologicalvariables in Upland California USA in 1976
Usage
data(califairpoll)
Format
This data frame contains the following columns
ozonelevelOzone concentration (ppm) at Sandburg Air Force Base
daggettpressuregradientPressure gradient at Daggett California
inversionbaseheightInversion base height feet
inversionbasetempInversion base temperature degrees Fahrenheit
Source
Brieman L and Friedman J (1985) Estimating optimal transformations for multiple regressionand correlation (with discussion)Journal of the American Statistical Association 80 580ndash619
Examples
library(SemiPar)data(califairpoll)pairs(califairpoll)
copper Copper data
Description
The copper data frame has 442 sets of observations from a simulation based on a stockpile ofmined material in the former Soviet Union Boreholes have been drilled into the dump The drillcore is cut every 5 metres and assayed for copper and cobalt content in percentage by weight
Usage
data(copper)
electemp 5
Format
This data frame contains the following columns
samplenum sample number
id sample identification number
zone zone code
xcoord x co-ordinate
ycoord y co-ordinate
zcoord z co-ordinate
grade grade measurement
corelength percentage of copper
Source
Clark I and Harper WV (2000)Practical Geostatistics 2000 Columbus Ohio Ecosse NorthAmerica Llc
Examples
library(SemiPar)data(copper)pairs(copper[47])
electemp Electricity usage and temperature data
Description
Theelectemp data frame has 55 observations on monthly electricity usage and average temper-ature for a house in Westchester County New York USA
Usage
data(electemp)
Format
This data frame contains the following columns
usage monthly electricity usage (kilowatt-hours) from a house in Westchester County New YorkUSA
temp average temperature (degrees Fahrenheit) for the corresponding month
Source
Chatterjee S Handcock M and Simonoff JS (1995)A Casebook for a First Course in Statisticsand Data Analysis New York John Wiley amp Sons
6 ethanol
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(electemp)attach(electemp)plot(usagetemp)
ethanol Ethanol data
Description
The ethanol data frame contains 88 sets of measurements for variables from an experiment in whichethanol was burned in a single cylinder automobile test engine
Usage
data(ethanol)
Format
This data frame contains the following columns
NOx the concentration of nitric oxide (NO) and nitrogen dioxide (NO2) in engine exhaust nor-malized by the work done by the engine
C the compression ratio of the engine
E the equivalence ratio at which the engine was run ndash a measure of the richness of the airethanolmix
Source
Brinkman ND (1981) Ethanol fuel ndash a single-cylinder engine study of efficiency and exhaustemissionsSAE transactionsVol 90 No 810345 1410ndash1424
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ethanol)pairs(ethanol)
fittedspm 7
fittedspm Fitted values for semiparametric regression
Description
Extracts fitted values from a semiparametric regression fit object
Usage
fittedspm(object)
Arguments
object a fittedspm object as produced byspm()
other possible arguments
Details
Extracts fitted from a semiparametric regression fit object The fitted are defined to be the set ofvalues obtained when the predictor variable data are substituted into the fitted regression model
Value
The vector of fitted
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)points(agefitted(fit)col=red)
8 fuelframe
fossil Fossil data
Description
Thefossil data frame has 106 observations on fossil shells
Usage
data(fossil)
Format
This data frame contains the following columns
age age in millions of years
strontiumratio ratios of strontium isotopes
Source
Bralower TJ Fullagar PD Paull CK Dwyer GS and Leckie RM (1997) Mid-cretaceousstrontium-isotope stratigraphy of deep-sea sectionsGeological Society of America Bulletin 1091421-1442
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(fossil)attach(fossil)plot(agestrontiumratio)
fuelframe Automobile data from consumer reports
Description
Thefuelframe data frame contains data on 5 variables (columns) for 117 cars (rows)
Usage
data(fuelframe)
janka 9
Format
This data frame contains the following columns
carname character variable giving the name (make) of the car
Weight the weight of the car in pounds
Disp the engine displacement in litres
Mileage gas mileage in milesgallon
Fuel a derived variable concerning fuel efficiency
Type a factor giving the general type of car The levels are Small Sporty Compact Medium Large Van
Source
Consumer Reports April 1990 pp 235-288
References
Chambers JM and Hastie TJ (eds) (1992)Statistical Models in SWadsworth and Brooks Pacific Grove California
Examples
library(SemiPar)data(fuelframe)pairs(fuelframe)par(mfrow=c(22))fuelfit lt- lm(Fuel ~ Weight + Dispfuelframe)plot(fuelfitask=FALSE)par(mfrow=c(11))
janka Janka hardness data
Description
The janka data frame has 36 observations on Australian timber samples
Usage
data(janka)
Format
This data frame contains the following columns
dens a measure of density of the timber
hardness the Janka hardness (structural property) of the timber
10 lidar
Source
Williams EJ (1959)Regression Analysis New York John Wiley amp Sons
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(janka)attach(janka)plot(denshardness)
lidar LIDAR data
Description
The lidar data frame has 221 observations from a light detection and ranging (LIDAR) experi-ment
Usage
data(lidar)
Format
This data frame contains the following columns
range distance travelled before the light is reflected back to its source
logratio logarithm of the ratio of received light from two laser sources
Source
Sigrist M (Ed) (1994)Air Monitoring by Spectroscopic Techniques(Chemical Analysis Seriesvol 197) New York Wiley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(lidar)attach(lidar)plot(rangelogratio)
linesspm 11
linesspm Add a curves to an existing plot
Description
Takes a fittedspm object produced byspm() and adds a curve The function is only appropriatein the case of a single predictor
Usage
linesspm(x)
Arguments
x a fittedspm object as produced byspm()
other graphics parameters described in Appendix B of the SemiPar Usersrsquo Man-ualhttpwwwmathsunsweduau~wandpapershtml
Details
Takes a fittedspm object produced byspm() and adds a curve The function is only appropriatein the case of a single predictor
Value
The function adds a curve to a plot
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fossiltype=n)lines(fit)
12 milanmort
points(fossil)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fossiltype=n)lines(fitcol=greenlwd=5shadecol=mediumpurple1)points(fossilcol=orangepch=16)par(op)
milanmort Milan mortality data
Description
Themilanmort data frame has data on 3652 consecutive days (10 consecutive years 1st Jan-uary 1980 to 30th December 1989) for the city of Milan Italy
Usage
data(milanmort)
Format
This data frame contains the following columns
daynum number of days since 31st December 1979
dayofweek 1=Monday2=Tuesday3=Wednesday4=Thursday 5=Friday6=Saturday7=Sunday
holiday indicator of public holiday 1=public holiday 0=otherwise
meantemp mean daily temperature in degrees Celcius
relhumid relative humidity
totmort total number of deaths
respmort total number of respiratory deaths
SO2 measure of sulphur dioxide level in ambient air
TSP total suspended particles in ambient air
Source
Vigotti MA Rossi G Bisanti L Zanobetti A and Schwartz J (1996) Short term effect ofurban air pollution on respiratory health in Milan Italy 1980-1989Journal of Epidemiology andCommunity Health 50 S71-S75
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
monitormercury 13
Examples
library(SemiPar)data(milanmort)pairs(milanmortpch=)
monitormercury Mercury biomonintoring data
Description
Themonitormercury data frame has 22 observations from sampling locations around a solidwaste incinerator in Warren County New Jersey USA
Usage
data(monitormercury)
Format
This data frame contains the following columns
UTMNorth longitude of sampling location
UTMEast latitude of sampling location
mercuryconcentration mercury concentration in dry sphagnum moss grown at the sampling lo-cation
Source
Opsomer JD Agras J Carpi A and Rodrigues G (1995) An application of locally weightedregression to airborne mercury deposition around an incinerator siteEnvironmetrics 6 205-221
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(monitormercury)pairs(monitormercury)
14 pigweights
onions Onions data
Description
Theonions data frame contains 84 sets of observations from an experiment involving the produc-tion of white Spanish onions in two South Australian locations
Usage
data(onions)
Format
This data frame contains the following columns
dens areal density of plants (plants per square metre)
yield onion yield (grammes per plant)
location indicator of location 0=Purnong Landing 1=Virginia
Source
Ratkowsky D A (1983)Nonlinear Regression Modeling A Unified Practical ApproachNewYork Marcel Dekker
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(onions)attach(onions)pointscols lt- c(redblue)plot(densyieldcol=pointscols[location+1]pch=16)legend(100250c(Purnong LandingVirginia)col=pointscolspch=rep(162))
pigweights Pig weight data
Description
Thepigweights data frame has 9 repeated weight measures on 48 pigs
Usage
data(pigweights)
plotspm 15
Format
This data frame contains the following columns
idnum identification number of pig
numweeks number of weeks since measurements commenced
weight bodyweight of pig idnum after numweeks weeks
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(pigweights)library(lattice)xyplot(weight~numweeksdata=pigweightsgroups=idnumtype=b)
plotspm Semiparametric regression plotting
Description
Takes a fittedspmobject produced byspm() and plots the component smooth functions that makeit up on the scale of the linear predictor
Usage
plotspm(x)
Arguments
x a fittedspm object as produced byspm()
other graphics parameters described in Appendix B of the SemiPar Usersrsquo Man-ualhttpwwwmathsunsweduau~wandpapershtml
Details
Produces plots with each panel corresponding to a component of the semiparametric regressionmodel
Value
The function generates plots
16 predictspm
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fitylim=range(strontiumratio)col=green
lwd=5shadecol=mediumpurple1rugcol=blue)points(agestrontiumratiocol=orangepch=16)par(op)
predictspm Semiparametric regression prediction
Description
Takes a fittedspm object produced byspm() and obtains predictions at new data values
Usage
predictspm(objectnewdatase)
Arguments
object a fittedspm object as produced byspm()
newdata a data frame containing the values of the predictors at which predictions arerequired The columns should have the same name as the predictors
se when this is TRUE standard error estimates are returned for each predictionThe default is FALSE
other arguments
printspm 17
Details
Takes a fittedspm object produced byspm() and obtains predictions at new data values as speci-fied by the lsquonewdatarsquo argument If lsquose=TRUErsquo then standard error estimates are also obtained
Value
If se=FALSE then a vector of predictions at lsquonewdatarsquo is returned If se=TRUE then a list withcomponents named lsquofitrsquo and lsquosersquo is returned The lsquofitrsquo component contains the predictions The lsquosersquocomponent contains standard error estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm plotspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))newdataage lt- dataframe(age=c(90100110120130))preds lt- predict(fitnewdata=newdataagese=TRUE)print(preds)
plot(fitxlim=c(90130))points(unlist(newdataage)preds$fitcol=red)points(unlist(newdataage)preds$fit+2preds$secol=blue)points(unlist(newdataage)preds$fit-2preds$secol=green)
printspm Prints semiparametric regression fit object
Description
Prints a brief description of a semiparametric regression fit object to the screen
Usage
printspm(x)
18 ragweed
Arguments
x a fittedspm object as produced byspm()
other possible arguments
Details
Prints a brief description of a semiparametric regression fit object to the screen
Value
The function prints to the screen
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))print(fit)
ragweed Ragweed data
Description
Theragweed data frame has data on ragweed levels and meteorological variables for 335 days inKalamazoo Michigan USA
Usage
data(ragweed)
residualsspm 19
Format
This data frame contains the following columns
ragweed ragweed level (grains per cubic metre)
year one of 1991 1992 1993 or 1994
dayinseasday number in the current ragweed pollen season
temperature temperature of following day (degrees Fahrenheit)
rain indicator of significant rain the following day 1=at least 3 hours of steady or brief but intenserain 0=otherwise
windspeed wind speed forecast for following day (knots)
Source
Stark P C Ryan L M McDonald J L and Burge H A (1997) Using meteorologic data tomodel and predict daily ragweed pollen levelsAerobiologia 13 177-184
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ragweed)pairs(ragweedpch=)
residualsspm Residuals for semiparametric regression
Description
Extracts residuals from a semiparametric regression fit object
Usage
residualsspm(object)
Arguments
object a fittedspm object as produced byspm()
other possible arguments
Details
Extracts residuals from a semiparametric regression fit object The residuals are defined to be thedifference between the response variable and the fitted values
20 retireplan
Value
The vector of residuals
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(ageresiduals(fit))abline(00)
retireplan Retirement plan data
Description
The retireplan data frame has data on 401(k) retirement plans for employees of 92 firmsmanaged by a company code-named Best Retirement Inc (BRI)
Usage
data(retireplan)
Format
This data frame contains the following columns
contrib contribution to retirement plan at end of first year
group 1=client has group life of group health insurance policy 0=otherwise
turnover employee turnover rate
eligible number of employees eligible to participate in 401(k) plans
vest 1=plan has immediate vesting of employer contributions 0=otherwise
salinity 21
failsafe 1=plan has a fail-safe provision 0=otherwise
match percentage of contributions matched by the employer
salary average annual employee salary in dollars
estimate underwriterrsquos estimate of end-of-year contributions in dollars
susan 1=plan was sold by a sales representative who has been specifically trained to deal exclu-sively with 401(k) plans (code-named Susan Shepard)
Source
Bryant PG and Smith MA (1995)Practical data analysis case studies in business statisticsChicago Irwin
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(retireplan)pairs(retireplan)
salinity Salinity data
Description
The salinity data frame has 28 observations on hydrological measurements from PamlicoSound North Carolina USA
Usage
data(salinity)
Format
This data frame contains the following columns
salinity salinity in Pamlico Sound
laggedsalinity salinity in Pamlico Sound during the previous six weeks
trend trend=1 if the data is the first six-week period of the spring and so forth Used to detectpossible effects of the seasonal warming trend
discharge discharge of fresh water from rivers into the sound
Source
Ruppert D and Carroll RJ (1980) Trimmed least squares estimation in the linear modelJournalof the American Statistical Association 75 828-838
22 sausage
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(salinity)pairs(salinity)
sausage Sausage data
Description
Thesausage data frame has data on 54 lsquohot dogrsquo sausages
Usage
data(sausage)
Format
This data frame contains the following columns
type type of meat
calories number of calories
sodium measure of sodium content
Source
Moore DS and McCabe GP (2003)Introduction to the Practice of Statistics Fourth EditionWH Freeman and Company
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sausage)attach(sausage)pointscols lt- c(redbluegreen)plot(sodiumcaloriescol=pointscols[type]pch=16)legend(200180c(beefporkpoultry)col=pointscolspch=rep(163))
scallop 23
scallop Scallop abundance data
Description
Thescallop data frame has 148 triplets concerning scallop abundance based on a 1990 surveycruise in the Atlantic continental shelf off Long Island New York USA
Usage
data(scallop)
Format
This data frame contains the following columns
latitude degrees latitude (north of the Equator)
longitude degrees longitude (west of Greenwich)
totcatch size of scallop catch at location specified by latitude and longitude
Source
Ecker MD and Heltshe JF (1994) Geostatistical estimates of scallop abundance InCase Studiesin BiometryLange N Ryan L Billard L Brillinger D Conquest L and Greenhouse J (eds)New York John Wiley amp Sons 107-124
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(scallop)pairs(scallop)
sitka Sitka spruce data
Description
Thesitka data frame contains measurements of log-size for 79 Sitka spruce trees grown in nor-mal or ozone-enriched environments Within each year the data are organised in four blocks cor-responding to four controlled environment chambers The first two chambers containing 27 treeseach have an ozone-enriched atmosphere the remaining two containing 12 and 13 trees respec-tively have a normal (control) atmosphere
24 spm
Usage
data(sitka)
Format
This data frame contains the following columns
idnum identification number of tree
order time order ranking within each tree
days time in days since 1st January 1988
logsize tree size measured on a logarithmic scale
ozone indicator ozone treatment 0=control1=ozone
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sitka)attach(sitka)library(lattice)ozonechar lt- rep(controlnrow(sitka))ozonechar[ozone==1] lt- ozonexyplot(logsize~days|ozonechardata=sitkagroups=idnumtype=b)
spm Fit a SemiParametric regression Model
Description
spm is used to fit semiparametric regression models using the mixed model representation of pe-nalized splines (per Ruppert Wand and Carroll 2003)
Usage
spm(formrandom=NULLgroup=NULLfamily=gaussiansparmethod=REMLomitmissing=NULL)
spm 25
Arguments
form a formula describing the model to be fit Note that an intercept is always in-cluded whether given in the formula or not
random random= 1 specifies inclusion of a random intercept according to the groupsspecified by the group argument
group a vector of labels for specifying groups
family for specification of the type of likelihood model assumed in the fitting May begaussianbinomial or poisson
sparmethod method for automatic smoothing parameter selection May be REML (re-stricted maximum likelihood) or ML (maximum likelihood)
omitmissing a logical value indicating whether fields with missing values are to be omitted
Details
See the SemiPar Usersrsquo Manual for details and examples
Value
An list object of classspm containing the fitted model The components are
fit mimics fit object of lme() for family=gaussian and glmmPQL() for family=binomialor family=poisson
info information about the inputs
aux auxiliary information such as variability estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
gam (in package lsquomgcvrsquo)lme (in package lsquonlmersquo)glmmPQL(in package lsquoMASSrsquo)plotspmsummaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)summary(fit)
26 summaryspm
data(califairpoll)attach(califairpoll)fit lt- spm(ozonelevel ~ f(daggettpressuregradient)+
f(inversionbaseheight) +f(inversionbasetemp))
summary(fit)par(mfrow=c(22))plot(fit)
The SemiPar User Manual contains several other examples and details of plotting parameters The current version of the manual is posted on the web-site wwwmathsunsweduau~wandpapershtml
summaryspm Semiparametric regression summary
Description
Takes a fittedspm object produced byspm() and summarises the fit
Usage
summaryspm(object)
Arguments
object a fittedspm object as produced byspm()
other arguments
Details
Produces tables for the linear (parametric) and non-linear (nonparametric) components The lineartable provides coefficient estimates standard errors and p-values The non-linear table providesdegrees of freedom values and other information
Value
The function generates summary tables
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
electemp 5
Format
This data frame contains the following columns
samplenum sample number
id sample identification number
zone zone code
xcoord x co-ordinate
ycoord y co-ordinate
zcoord z co-ordinate
grade grade measurement
corelength percentage of copper
Source
Clark I and Harper WV (2000)Practical Geostatistics 2000 Columbus Ohio Ecosse NorthAmerica Llc
Examples
library(SemiPar)data(copper)pairs(copper[47])
electemp Electricity usage and temperature data
Description
Theelectemp data frame has 55 observations on monthly electricity usage and average temper-ature for a house in Westchester County New York USA
Usage
data(electemp)
Format
This data frame contains the following columns
usage monthly electricity usage (kilowatt-hours) from a house in Westchester County New YorkUSA
temp average temperature (degrees Fahrenheit) for the corresponding month
Source
Chatterjee S Handcock M and Simonoff JS (1995)A Casebook for a First Course in Statisticsand Data Analysis New York John Wiley amp Sons
6 ethanol
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(electemp)attach(electemp)plot(usagetemp)
ethanol Ethanol data
Description
The ethanol data frame contains 88 sets of measurements for variables from an experiment in whichethanol was burned in a single cylinder automobile test engine
Usage
data(ethanol)
Format
This data frame contains the following columns
NOx the concentration of nitric oxide (NO) and nitrogen dioxide (NO2) in engine exhaust nor-malized by the work done by the engine
C the compression ratio of the engine
E the equivalence ratio at which the engine was run ndash a measure of the richness of the airethanolmix
Source
Brinkman ND (1981) Ethanol fuel ndash a single-cylinder engine study of efficiency and exhaustemissionsSAE transactionsVol 90 No 810345 1410ndash1424
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ethanol)pairs(ethanol)
fittedspm 7
fittedspm Fitted values for semiparametric regression
Description
Extracts fitted values from a semiparametric regression fit object
Usage
fittedspm(object)
Arguments
object a fittedspm object as produced byspm()
other possible arguments
Details
Extracts fitted from a semiparametric regression fit object The fitted are defined to be the set ofvalues obtained when the predictor variable data are substituted into the fitted regression model
Value
The vector of fitted
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)points(agefitted(fit)col=red)
8 fuelframe
fossil Fossil data
Description
Thefossil data frame has 106 observations on fossil shells
Usage
data(fossil)
Format
This data frame contains the following columns
age age in millions of years
strontiumratio ratios of strontium isotopes
Source
Bralower TJ Fullagar PD Paull CK Dwyer GS and Leckie RM (1997) Mid-cretaceousstrontium-isotope stratigraphy of deep-sea sectionsGeological Society of America Bulletin 1091421-1442
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(fossil)attach(fossil)plot(agestrontiumratio)
fuelframe Automobile data from consumer reports
Description
Thefuelframe data frame contains data on 5 variables (columns) for 117 cars (rows)
Usage
data(fuelframe)
janka 9
Format
This data frame contains the following columns
carname character variable giving the name (make) of the car
Weight the weight of the car in pounds
Disp the engine displacement in litres
Mileage gas mileage in milesgallon
Fuel a derived variable concerning fuel efficiency
Type a factor giving the general type of car The levels are Small Sporty Compact Medium Large Van
Source
Consumer Reports April 1990 pp 235-288
References
Chambers JM and Hastie TJ (eds) (1992)Statistical Models in SWadsworth and Brooks Pacific Grove California
Examples
library(SemiPar)data(fuelframe)pairs(fuelframe)par(mfrow=c(22))fuelfit lt- lm(Fuel ~ Weight + Dispfuelframe)plot(fuelfitask=FALSE)par(mfrow=c(11))
janka Janka hardness data
Description
The janka data frame has 36 observations on Australian timber samples
Usage
data(janka)
Format
This data frame contains the following columns
dens a measure of density of the timber
hardness the Janka hardness (structural property) of the timber
10 lidar
Source
Williams EJ (1959)Regression Analysis New York John Wiley amp Sons
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(janka)attach(janka)plot(denshardness)
lidar LIDAR data
Description
The lidar data frame has 221 observations from a light detection and ranging (LIDAR) experi-ment
Usage
data(lidar)
Format
This data frame contains the following columns
range distance travelled before the light is reflected back to its source
logratio logarithm of the ratio of received light from two laser sources
Source
Sigrist M (Ed) (1994)Air Monitoring by Spectroscopic Techniques(Chemical Analysis Seriesvol 197) New York Wiley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(lidar)attach(lidar)plot(rangelogratio)
linesspm 11
linesspm Add a curves to an existing plot
Description
Takes a fittedspm object produced byspm() and adds a curve The function is only appropriatein the case of a single predictor
Usage
linesspm(x)
Arguments
x a fittedspm object as produced byspm()
other graphics parameters described in Appendix B of the SemiPar Usersrsquo Man-ualhttpwwwmathsunsweduau~wandpapershtml
Details
Takes a fittedspm object produced byspm() and adds a curve The function is only appropriatein the case of a single predictor
Value
The function adds a curve to a plot
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fossiltype=n)lines(fit)
12 milanmort
points(fossil)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fossiltype=n)lines(fitcol=greenlwd=5shadecol=mediumpurple1)points(fossilcol=orangepch=16)par(op)
milanmort Milan mortality data
Description
Themilanmort data frame has data on 3652 consecutive days (10 consecutive years 1st Jan-uary 1980 to 30th December 1989) for the city of Milan Italy
Usage
data(milanmort)
Format
This data frame contains the following columns
daynum number of days since 31st December 1979
dayofweek 1=Monday2=Tuesday3=Wednesday4=Thursday 5=Friday6=Saturday7=Sunday
holiday indicator of public holiday 1=public holiday 0=otherwise
meantemp mean daily temperature in degrees Celcius
relhumid relative humidity
totmort total number of deaths
respmort total number of respiratory deaths
SO2 measure of sulphur dioxide level in ambient air
TSP total suspended particles in ambient air
Source
Vigotti MA Rossi G Bisanti L Zanobetti A and Schwartz J (1996) Short term effect ofurban air pollution on respiratory health in Milan Italy 1980-1989Journal of Epidemiology andCommunity Health 50 S71-S75
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
monitormercury 13
Examples
library(SemiPar)data(milanmort)pairs(milanmortpch=)
monitormercury Mercury biomonintoring data
Description
Themonitormercury data frame has 22 observations from sampling locations around a solidwaste incinerator in Warren County New Jersey USA
Usage
data(monitormercury)
Format
This data frame contains the following columns
UTMNorth longitude of sampling location
UTMEast latitude of sampling location
mercuryconcentration mercury concentration in dry sphagnum moss grown at the sampling lo-cation
Source
Opsomer JD Agras J Carpi A and Rodrigues G (1995) An application of locally weightedregression to airborne mercury deposition around an incinerator siteEnvironmetrics 6 205-221
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(monitormercury)pairs(monitormercury)
14 pigweights
onions Onions data
Description
Theonions data frame contains 84 sets of observations from an experiment involving the produc-tion of white Spanish onions in two South Australian locations
Usage
data(onions)
Format
This data frame contains the following columns
dens areal density of plants (plants per square metre)
yield onion yield (grammes per plant)
location indicator of location 0=Purnong Landing 1=Virginia
Source
Ratkowsky D A (1983)Nonlinear Regression Modeling A Unified Practical ApproachNewYork Marcel Dekker
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(onions)attach(onions)pointscols lt- c(redblue)plot(densyieldcol=pointscols[location+1]pch=16)legend(100250c(Purnong LandingVirginia)col=pointscolspch=rep(162))
pigweights Pig weight data
Description
Thepigweights data frame has 9 repeated weight measures on 48 pigs
Usage
data(pigweights)
plotspm 15
Format
This data frame contains the following columns
idnum identification number of pig
numweeks number of weeks since measurements commenced
weight bodyweight of pig idnum after numweeks weeks
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(pigweights)library(lattice)xyplot(weight~numweeksdata=pigweightsgroups=idnumtype=b)
plotspm Semiparametric regression plotting
Description
Takes a fittedspmobject produced byspm() and plots the component smooth functions that makeit up on the scale of the linear predictor
Usage
plotspm(x)
Arguments
x a fittedspm object as produced byspm()
other graphics parameters described in Appendix B of the SemiPar Usersrsquo Man-ualhttpwwwmathsunsweduau~wandpapershtml
Details
Produces plots with each panel corresponding to a component of the semiparametric regressionmodel
Value
The function generates plots
16 predictspm
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fitylim=range(strontiumratio)col=green
lwd=5shadecol=mediumpurple1rugcol=blue)points(agestrontiumratiocol=orangepch=16)par(op)
predictspm Semiparametric regression prediction
Description
Takes a fittedspm object produced byspm() and obtains predictions at new data values
Usage
predictspm(objectnewdatase)
Arguments
object a fittedspm object as produced byspm()
newdata a data frame containing the values of the predictors at which predictions arerequired The columns should have the same name as the predictors
se when this is TRUE standard error estimates are returned for each predictionThe default is FALSE
other arguments
printspm 17
Details
Takes a fittedspm object produced byspm() and obtains predictions at new data values as speci-fied by the lsquonewdatarsquo argument If lsquose=TRUErsquo then standard error estimates are also obtained
Value
If se=FALSE then a vector of predictions at lsquonewdatarsquo is returned If se=TRUE then a list withcomponents named lsquofitrsquo and lsquosersquo is returned The lsquofitrsquo component contains the predictions The lsquosersquocomponent contains standard error estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm plotspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))newdataage lt- dataframe(age=c(90100110120130))preds lt- predict(fitnewdata=newdataagese=TRUE)print(preds)
plot(fitxlim=c(90130))points(unlist(newdataage)preds$fitcol=red)points(unlist(newdataage)preds$fit+2preds$secol=blue)points(unlist(newdataage)preds$fit-2preds$secol=green)
printspm Prints semiparametric regression fit object
Description
Prints a brief description of a semiparametric regression fit object to the screen
Usage
printspm(x)
18 ragweed
Arguments
x a fittedspm object as produced byspm()
other possible arguments
Details
Prints a brief description of a semiparametric regression fit object to the screen
Value
The function prints to the screen
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))print(fit)
ragweed Ragweed data
Description
Theragweed data frame has data on ragweed levels and meteorological variables for 335 days inKalamazoo Michigan USA
Usage
data(ragweed)
residualsspm 19
Format
This data frame contains the following columns
ragweed ragweed level (grains per cubic metre)
year one of 1991 1992 1993 or 1994
dayinseasday number in the current ragweed pollen season
temperature temperature of following day (degrees Fahrenheit)
rain indicator of significant rain the following day 1=at least 3 hours of steady or brief but intenserain 0=otherwise
windspeed wind speed forecast for following day (knots)
Source
Stark P C Ryan L M McDonald J L and Burge H A (1997) Using meteorologic data tomodel and predict daily ragweed pollen levelsAerobiologia 13 177-184
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ragweed)pairs(ragweedpch=)
residualsspm Residuals for semiparametric regression
Description
Extracts residuals from a semiparametric regression fit object
Usage
residualsspm(object)
Arguments
object a fittedspm object as produced byspm()
other possible arguments
Details
Extracts residuals from a semiparametric regression fit object The residuals are defined to be thedifference between the response variable and the fitted values
20 retireplan
Value
The vector of residuals
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(ageresiduals(fit))abline(00)
retireplan Retirement plan data
Description
The retireplan data frame has data on 401(k) retirement plans for employees of 92 firmsmanaged by a company code-named Best Retirement Inc (BRI)
Usage
data(retireplan)
Format
This data frame contains the following columns
contrib contribution to retirement plan at end of first year
group 1=client has group life of group health insurance policy 0=otherwise
turnover employee turnover rate
eligible number of employees eligible to participate in 401(k) plans
vest 1=plan has immediate vesting of employer contributions 0=otherwise
salinity 21
failsafe 1=plan has a fail-safe provision 0=otherwise
match percentage of contributions matched by the employer
salary average annual employee salary in dollars
estimate underwriterrsquos estimate of end-of-year contributions in dollars
susan 1=plan was sold by a sales representative who has been specifically trained to deal exclu-sively with 401(k) plans (code-named Susan Shepard)
Source
Bryant PG and Smith MA (1995)Practical data analysis case studies in business statisticsChicago Irwin
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(retireplan)pairs(retireplan)
salinity Salinity data
Description
The salinity data frame has 28 observations on hydrological measurements from PamlicoSound North Carolina USA
Usage
data(salinity)
Format
This data frame contains the following columns
salinity salinity in Pamlico Sound
laggedsalinity salinity in Pamlico Sound during the previous six weeks
trend trend=1 if the data is the first six-week period of the spring and so forth Used to detectpossible effects of the seasonal warming trend
discharge discharge of fresh water from rivers into the sound
Source
Ruppert D and Carroll RJ (1980) Trimmed least squares estimation in the linear modelJournalof the American Statistical Association 75 828-838
22 sausage
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(salinity)pairs(salinity)
sausage Sausage data
Description
Thesausage data frame has data on 54 lsquohot dogrsquo sausages
Usage
data(sausage)
Format
This data frame contains the following columns
type type of meat
calories number of calories
sodium measure of sodium content
Source
Moore DS and McCabe GP (2003)Introduction to the Practice of Statistics Fourth EditionWH Freeman and Company
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sausage)attach(sausage)pointscols lt- c(redbluegreen)plot(sodiumcaloriescol=pointscols[type]pch=16)legend(200180c(beefporkpoultry)col=pointscolspch=rep(163))
scallop 23
scallop Scallop abundance data
Description
Thescallop data frame has 148 triplets concerning scallop abundance based on a 1990 surveycruise in the Atlantic continental shelf off Long Island New York USA
Usage
data(scallop)
Format
This data frame contains the following columns
latitude degrees latitude (north of the Equator)
longitude degrees longitude (west of Greenwich)
totcatch size of scallop catch at location specified by latitude and longitude
Source
Ecker MD and Heltshe JF (1994) Geostatistical estimates of scallop abundance InCase Studiesin BiometryLange N Ryan L Billard L Brillinger D Conquest L and Greenhouse J (eds)New York John Wiley amp Sons 107-124
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(scallop)pairs(scallop)
sitka Sitka spruce data
Description
Thesitka data frame contains measurements of log-size for 79 Sitka spruce trees grown in nor-mal or ozone-enriched environments Within each year the data are organised in four blocks cor-responding to four controlled environment chambers The first two chambers containing 27 treeseach have an ozone-enriched atmosphere the remaining two containing 12 and 13 trees respec-tively have a normal (control) atmosphere
24 spm
Usage
data(sitka)
Format
This data frame contains the following columns
idnum identification number of tree
order time order ranking within each tree
days time in days since 1st January 1988
logsize tree size measured on a logarithmic scale
ozone indicator ozone treatment 0=control1=ozone
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sitka)attach(sitka)library(lattice)ozonechar lt- rep(controlnrow(sitka))ozonechar[ozone==1] lt- ozonexyplot(logsize~days|ozonechardata=sitkagroups=idnumtype=b)
spm Fit a SemiParametric regression Model
Description
spm is used to fit semiparametric regression models using the mixed model representation of pe-nalized splines (per Ruppert Wand and Carroll 2003)
Usage
spm(formrandom=NULLgroup=NULLfamily=gaussiansparmethod=REMLomitmissing=NULL)
spm 25
Arguments
form a formula describing the model to be fit Note that an intercept is always in-cluded whether given in the formula or not
random random= 1 specifies inclusion of a random intercept according to the groupsspecified by the group argument
group a vector of labels for specifying groups
family for specification of the type of likelihood model assumed in the fitting May begaussianbinomial or poisson
sparmethod method for automatic smoothing parameter selection May be REML (re-stricted maximum likelihood) or ML (maximum likelihood)
omitmissing a logical value indicating whether fields with missing values are to be omitted
Details
See the SemiPar Usersrsquo Manual for details and examples
Value
An list object of classspm containing the fitted model The components are
fit mimics fit object of lme() for family=gaussian and glmmPQL() for family=binomialor family=poisson
info information about the inputs
aux auxiliary information such as variability estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
gam (in package lsquomgcvrsquo)lme (in package lsquonlmersquo)glmmPQL(in package lsquoMASSrsquo)plotspmsummaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)summary(fit)
26 summaryspm
data(califairpoll)attach(califairpoll)fit lt- spm(ozonelevel ~ f(daggettpressuregradient)+
f(inversionbaseheight) +f(inversionbasetemp))
summary(fit)par(mfrow=c(22))plot(fit)
The SemiPar User Manual contains several other examples and details of plotting parameters The current version of the manual is posted on the web-site wwwmathsunsweduau~wandpapershtml
summaryspm Semiparametric regression summary
Description
Takes a fittedspm object produced byspm() and summarises the fit
Usage
summaryspm(object)
Arguments
object a fittedspm object as produced byspm()
other arguments
Details
Produces tables for the linear (parametric) and non-linear (nonparametric) components The lineartable provides coefficient estimates standard errors and p-values The non-linear table providesdegrees of freedom values and other information
Value
The function generates summary tables
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
6 ethanol
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(electemp)attach(electemp)plot(usagetemp)
ethanol Ethanol data
Description
The ethanol data frame contains 88 sets of measurements for variables from an experiment in whichethanol was burned in a single cylinder automobile test engine
Usage
data(ethanol)
Format
This data frame contains the following columns
NOx the concentration of nitric oxide (NO) and nitrogen dioxide (NO2) in engine exhaust nor-malized by the work done by the engine
C the compression ratio of the engine
E the equivalence ratio at which the engine was run ndash a measure of the richness of the airethanolmix
Source
Brinkman ND (1981) Ethanol fuel ndash a single-cylinder engine study of efficiency and exhaustemissionsSAE transactionsVol 90 No 810345 1410ndash1424
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ethanol)pairs(ethanol)
fittedspm 7
fittedspm Fitted values for semiparametric regression
Description
Extracts fitted values from a semiparametric regression fit object
Usage
fittedspm(object)
Arguments
object a fittedspm object as produced byspm()
other possible arguments
Details
Extracts fitted from a semiparametric regression fit object The fitted are defined to be the set ofvalues obtained when the predictor variable data are substituted into the fitted regression model
Value
The vector of fitted
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)points(agefitted(fit)col=red)
8 fuelframe
fossil Fossil data
Description
Thefossil data frame has 106 observations on fossil shells
Usage
data(fossil)
Format
This data frame contains the following columns
age age in millions of years
strontiumratio ratios of strontium isotopes
Source
Bralower TJ Fullagar PD Paull CK Dwyer GS and Leckie RM (1997) Mid-cretaceousstrontium-isotope stratigraphy of deep-sea sectionsGeological Society of America Bulletin 1091421-1442
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(fossil)attach(fossil)plot(agestrontiumratio)
fuelframe Automobile data from consumer reports
Description
Thefuelframe data frame contains data on 5 variables (columns) for 117 cars (rows)
Usage
data(fuelframe)
janka 9
Format
This data frame contains the following columns
carname character variable giving the name (make) of the car
Weight the weight of the car in pounds
Disp the engine displacement in litres
Mileage gas mileage in milesgallon
Fuel a derived variable concerning fuel efficiency
Type a factor giving the general type of car The levels are Small Sporty Compact Medium Large Van
Source
Consumer Reports April 1990 pp 235-288
References
Chambers JM and Hastie TJ (eds) (1992)Statistical Models in SWadsworth and Brooks Pacific Grove California
Examples
library(SemiPar)data(fuelframe)pairs(fuelframe)par(mfrow=c(22))fuelfit lt- lm(Fuel ~ Weight + Dispfuelframe)plot(fuelfitask=FALSE)par(mfrow=c(11))
janka Janka hardness data
Description
The janka data frame has 36 observations on Australian timber samples
Usage
data(janka)
Format
This data frame contains the following columns
dens a measure of density of the timber
hardness the Janka hardness (structural property) of the timber
10 lidar
Source
Williams EJ (1959)Regression Analysis New York John Wiley amp Sons
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(janka)attach(janka)plot(denshardness)
lidar LIDAR data
Description
The lidar data frame has 221 observations from a light detection and ranging (LIDAR) experi-ment
Usage
data(lidar)
Format
This data frame contains the following columns
range distance travelled before the light is reflected back to its source
logratio logarithm of the ratio of received light from two laser sources
Source
Sigrist M (Ed) (1994)Air Monitoring by Spectroscopic Techniques(Chemical Analysis Seriesvol 197) New York Wiley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(lidar)attach(lidar)plot(rangelogratio)
linesspm 11
linesspm Add a curves to an existing plot
Description
Takes a fittedspm object produced byspm() and adds a curve The function is only appropriatein the case of a single predictor
Usage
linesspm(x)
Arguments
x a fittedspm object as produced byspm()
other graphics parameters described in Appendix B of the SemiPar Usersrsquo Man-ualhttpwwwmathsunsweduau~wandpapershtml
Details
Takes a fittedspm object produced byspm() and adds a curve The function is only appropriatein the case of a single predictor
Value
The function adds a curve to a plot
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fossiltype=n)lines(fit)
12 milanmort
points(fossil)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fossiltype=n)lines(fitcol=greenlwd=5shadecol=mediumpurple1)points(fossilcol=orangepch=16)par(op)
milanmort Milan mortality data
Description
Themilanmort data frame has data on 3652 consecutive days (10 consecutive years 1st Jan-uary 1980 to 30th December 1989) for the city of Milan Italy
Usage
data(milanmort)
Format
This data frame contains the following columns
daynum number of days since 31st December 1979
dayofweek 1=Monday2=Tuesday3=Wednesday4=Thursday 5=Friday6=Saturday7=Sunday
holiday indicator of public holiday 1=public holiday 0=otherwise
meantemp mean daily temperature in degrees Celcius
relhumid relative humidity
totmort total number of deaths
respmort total number of respiratory deaths
SO2 measure of sulphur dioxide level in ambient air
TSP total suspended particles in ambient air
Source
Vigotti MA Rossi G Bisanti L Zanobetti A and Schwartz J (1996) Short term effect ofurban air pollution on respiratory health in Milan Italy 1980-1989Journal of Epidemiology andCommunity Health 50 S71-S75
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
monitormercury 13
Examples
library(SemiPar)data(milanmort)pairs(milanmortpch=)
monitormercury Mercury biomonintoring data
Description
Themonitormercury data frame has 22 observations from sampling locations around a solidwaste incinerator in Warren County New Jersey USA
Usage
data(monitormercury)
Format
This data frame contains the following columns
UTMNorth longitude of sampling location
UTMEast latitude of sampling location
mercuryconcentration mercury concentration in dry sphagnum moss grown at the sampling lo-cation
Source
Opsomer JD Agras J Carpi A and Rodrigues G (1995) An application of locally weightedregression to airborne mercury deposition around an incinerator siteEnvironmetrics 6 205-221
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(monitormercury)pairs(monitormercury)
14 pigweights
onions Onions data
Description
Theonions data frame contains 84 sets of observations from an experiment involving the produc-tion of white Spanish onions in two South Australian locations
Usage
data(onions)
Format
This data frame contains the following columns
dens areal density of plants (plants per square metre)
yield onion yield (grammes per plant)
location indicator of location 0=Purnong Landing 1=Virginia
Source
Ratkowsky D A (1983)Nonlinear Regression Modeling A Unified Practical ApproachNewYork Marcel Dekker
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(onions)attach(onions)pointscols lt- c(redblue)plot(densyieldcol=pointscols[location+1]pch=16)legend(100250c(Purnong LandingVirginia)col=pointscolspch=rep(162))
pigweights Pig weight data
Description
Thepigweights data frame has 9 repeated weight measures on 48 pigs
Usage
data(pigweights)
plotspm 15
Format
This data frame contains the following columns
idnum identification number of pig
numweeks number of weeks since measurements commenced
weight bodyweight of pig idnum after numweeks weeks
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(pigweights)library(lattice)xyplot(weight~numweeksdata=pigweightsgroups=idnumtype=b)
plotspm Semiparametric regression plotting
Description
Takes a fittedspmobject produced byspm() and plots the component smooth functions that makeit up on the scale of the linear predictor
Usage
plotspm(x)
Arguments
x a fittedspm object as produced byspm()
other graphics parameters described in Appendix B of the SemiPar Usersrsquo Man-ualhttpwwwmathsunsweduau~wandpapershtml
Details
Produces plots with each panel corresponding to a component of the semiparametric regressionmodel
Value
The function generates plots
16 predictspm
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fitylim=range(strontiumratio)col=green
lwd=5shadecol=mediumpurple1rugcol=blue)points(agestrontiumratiocol=orangepch=16)par(op)
predictspm Semiparametric regression prediction
Description
Takes a fittedspm object produced byspm() and obtains predictions at new data values
Usage
predictspm(objectnewdatase)
Arguments
object a fittedspm object as produced byspm()
newdata a data frame containing the values of the predictors at which predictions arerequired The columns should have the same name as the predictors
se when this is TRUE standard error estimates are returned for each predictionThe default is FALSE
other arguments
printspm 17
Details
Takes a fittedspm object produced byspm() and obtains predictions at new data values as speci-fied by the lsquonewdatarsquo argument If lsquose=TRUErsquo then standard error estimates are also obtained
Value
If se=FALSE then a vector of predictions at lsquonewdatarsquo is returned If se=TRUE then a list withcomponents named lsquofitrsquo and lsquosersquo is returned The lsquofitrsquo component contains the predictions The lsquosersquocomponent contains standard error estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm plotspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))newdataage lt- dataframe(age=c(90100110120130))preds lt- predict(fitnewdata=newdataagese=TRUE)print(preds)
plot(fitxlim=c(90130))points(unlist(newdataage)preds$fitcol=red)points(unlist(newdataage)preds$fit+2preds$secol=blue)points(unlist(newdataage)preds$fit-2preds$secol=green)
printspm Prints semiparametric regression fit object
Description
Prints a brief description of a semiparametric regression fit object to the screen
Usage
printspm(x)
18 ragweed
Arguments
x a fittedspm object as produced byspm()
other possible arguments
Details
Prints a brief description of a semiparametric regression fit object to the screen
Value
The function prints to the screen
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))print(fit)
ragweed Ragweed data
Description
Theragweed data frame has data on ragweed levels and meteorological variables for 335 days inKalamazoo Michigan USA
Usage
data(ragweed)
residualsspm 19
Format
This data frame contains the following columns
ragweed ragweed level (grains per cubic metre)
year one of 1991 1992 1993 or 1994
dayinseasday number in the current ragweed pollen season
temperature temperature of following day (degrees Fahrenheit)
rain indicator of significant rain the following day 1=at least 3 hours of steady or brief but intenserain 0=otherwise
windspeed wind speed forecast for following day (knots)
Source
Stark P C Ryan L M McDonald J L and Burge H A (1997) Using meteorologic data tomodel and predict daily ragweed pollen levelsAerobiologia 13 177-184
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ragweed)pairs(ragweedpch=)
residualsspm Residuals for semiparametric regression
Description
Extracts residuals from a semiparametric regression fit object
Usage
residualsspm(object)
Arguments
object a fittedspm object as produced byspm()
other possible arguments
Details
Extracts residuals from a semiparametric regression fit object The residuals are defined to be thedifference between the response variable and the fitted values
20 retireplan
Value
The vector of residuals
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(ageresiduals(fit))abline(00)
retireplan Retirement plan data
Description
The retireplan data frame has data on 401(k) retirement plans for employees of 92 firmsmanaged by a company code-named Best Retirement Inc (BRI)
Usage
data(retireplan)
Format
This data frame contains the following columns
contrib contribution to retirement plan at end of first year
group 1=client has group life of group health insurance policy 0=otherwise
turnover employee turnover rate
eligible number of employees eligible to participate in 401(k) plans
vest 1=plan has immediate vesting of employer contributions 0=otherwise
salinity 21
failsafe 1=plan has a fail-safe provision 0=otherwise
match percentage of contributions matched by the employer
salary average annual employee salary in dollars
estimate underwriterrsquos estimate of end-of-year contributions in dollars
susan 1=plan was sold by a sales representative who has been specifically trained to deal exclu-sively with 401(k) plans (code-named Susan Shepard)
Source
Bryant PG and Smith MA (1995)Practical data analysis case studies in business statisticsChicago Irwin
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(retireplan)pairs(retireplan)
salinity Salinity data
Description
The salinity data frame has 28 observations on hydrological measurements from PamlicoSound North Carolina USA
Usage
data(salinity)
Format
This data frame contains the following columns
salinity salinity in Pamlico Sound
laggedsalinity salinity in Pamlico Sound during the previous six weeks
trend trend=1 if the data is the first six-week period of the spring and so forth Used to detectpossible effects of the seasonal warming trend
discharge discharge of fresh water from rivers into the sound
Source
Ruppert D and Carroll RJ (1980) Trimmed least squares estimation in the linear modelJournalof the American Statistical Association 75 828-838
22 sausage
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(salinity)pairs(salinity)
sausage Sausage data
Description
Thesausage data frame has data on 54 lsquohot dogrsquo sausages
Usage
data(sausage)
Format
This data frame contains the following columns
type type of meat
calories number of calories
sodium measure of sodium content
Source
Moore DS and McCabe GP (2003)Introduction to the Practice of Statistics Fourth EditionWH Freeman and Company
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sausage)attach(sausage)pointscols lt- c(redbluegreen)plot(sodiumcaloriescol=pointscols[type]pch=16)legend(200180c(beefporkpoultry)col=pointscolspch=rep(163))
scallop 23
scallop Scallop abundance data
Description
Thescallop data frame has 148 triplets concerning scallop abundance based on a 1990 surveycruise in the Atlantic continental shelf off Long Island New York USA
Usage
data(scallop)
Format
This data frame contains the following columns
latitude degrees latitude (north of the Equator)
longitude degrees longitude (west of Greenwich)
totcatch size of scallop catch at location specified by latitude and longitude
Source
Ecker MD and Heltshe JF (1994) Geostatistical estimates of scallop abundance InCase Studiesin BiometryLange N Ryan L Billard L Brillinger D Conquest L and Greenhouse J (eds)New York John Wiley amp Sons 107-124
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(scallop)pairs(scallop)
sitka Sitka spruce data
Description
Thesitka data frame contains measurements of log-size for 79 Sitka spruce trees grown in nor-mal or ozone-enriched environments Within each year the data are organised in four blocks cor-responding to four controlled environment chambers The first two chambers containing 27 treeseach have an ozone-enriched atmosphere the remaining two containing 12 and 13 trees respec-tively have a normal (control) atmosphere
24 spm
Usage
data(sitka)
Format
This data frame contains the following columns
idnum identification number of tree
order time order ranking within each tree
days time in days since 1st January 1988
logsize tree size measured on a logarithmic scale
ozone indicator ozone treatment 0=control1=ozone
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sitka)attach(sitka)library(lattice)ozonechar lt- rep(controlnrow(sitka))ozonechar[ozone==1] lt- ozonexyplot(logsize~days|ozonechardata=sitkagroups=idnumtype=b)
spm Fit a SemiParametric regression Model
Description
spm is used to fit semiparametric regression models using the mixed model representation of pe-nalized splines (per Ruppert Wand and Carroll 2003)
Usage
spm(formrandom=NULLgroup=NULLfamily=gaussiansparmethod=REMLomitmissing=NULL)
spm 25
Arguments
form a formula describing the model to be fit Note that an intercept is always in-cluded whether given in the formula or not
random random= 1 specifies inclusion of a random intercept according to the groupsspecified by the group argument
group a vector of labels for specifying groups
family for specification of the type of likelihood model assumed in the fitting May begaussianbinomial or poisson
sparmethod method for automatic smoothing parameter selection May be REML (re-stricted maximum likelihood) or ML (maximum likelihood)
omitmissing a logical value indicating whether fields with missing values are to be omitted
Details
See the SemiPar Usersrsquo Manual for details and examples
Value
An list object of classspm containing the fitted model The components are
fit mimics fit object of lme() for family=gaussian and glmmPQL() for family=binomialor family=poisson
info information about the inputs
aux auxiliary information such as variability estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
gam (in package lsquomgcvrsquo)lme (in package lsquonlmersquo)glmmPQL(in package lsquoMASSrsquo)plotspmsummaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)summary(fit)
26 summaryspm
data(califairpoll)attach(califairpoll)fit lt- spm(ozonelevel ~ f(daggettpressuregradient)+
f(inversionbaseheight) +f(inversionbasetemp))
summary(fit)par(mfrow=c(22))plot(fit)
The SemiPar User Manual contains several other examples and details of plotting parameters The current version of the manual is posted on the web-site wwwmathsunsweduau~wandpapershtml
summaryspm Semiparametric regression summary
Description
Takes a fittedspm object produced byspm() and summarises the fit
Usage
summaryspm(object)
Arguments
object a fittedspm object as produced byspm()
other arguments
Details
Produces tables for the linear (parametric) and non-linear (nonparametric) components The lineartable provides coefficient estimates standard errors and p-values The non-linear table providesdegrees of freedom values and other information
Value
The function generates summary tables
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
fittedspm 7
fittedspm Fitted values for semiparametric regression
Description
Extracts fitted values from a semiparametric regression fit object
Usage
fittedspm(object)
Arguments
object a fittedspm object as produced byspm()
other possible arguments
Details
Extracts fitted from a semiparametric regression fit object The fitted are defined to be the set ofvalues obtained when the predictor variable data are substituted into the fitted regression model
Value
The vector of fitted
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)points(agefitted(fit)col=red)
8 fuelframe
fossil Fossil data
Description
Thefossil data frame has 106 observations on fossil shells
Usage
data(fossil)
Format
This data frame contains the following columns
age age in millions of years
strontiumratio ratios of strontium isotopes
Source
Bralower TJ Fullagar PD Paull CK Dwyer GS and Leckie RM (1997) Mid-cretaceousstrontium-isotope stratigraphy of deep-sea sectionsGeological Society of America Bulletin 1091421-1442
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(fossil)attach(fossil)plot(agestrontiumratio)
fuelframe Automobile data from consumer reports
Description
Thefuelframe data frame contains data on 5 variables (columns) for 117 cars (rows)
Usage
data(fuelframe)
janka 9
Format
This data frame contains the following columns
carname character variable giving the name (make) of the car
Weight the weight of the car in pounds
Disp the engine displacement in litres
Mileage gas mileage in milesgallon
Fuel a derived variable concerning fuel efficiency
Type a factor giving the general type of car The levels are Small Sporty Compact Medium Large Van
Source
Consumer Reports April 1990 pp 235-288
References
Chambers JM and Hastie TJ (eds) (1992)Statistical Models in SWadsworth and Brooks Pacific Grove California
Examples
library(SemiPar)data(fuelframe)pairs(fuelframe)par(mfrow=c(22))fuelfit lt- lm(Fuel ~ Weight + Dispfuelframe)plot(fuelfitask=FALSE)par(mfrow=c(11))
janka Janka hardness data
Description
The janka data frame has 36 observations on Australian timber samples
Usage
data(janka)
Format
This data frame contains the following columns
dens a measure of density of the timber
hardness the Janka hardness (structural property) of the timber
10 lidar
Source
Williams EJ (1959)Regression Analysis New York John Wiley amp Sons
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(janka)attach(janka)plot(denshardness)
lidar LIDAR data
Description
The lidar data frame has 221 observations from a light detection and ranging (LIDAR) experi-ment
Usage
data(lidar)
Format
This data frame contains the following columns
range distance travelled before the light is reflected back to its source
logratio logarithm of the ratio of received light from two laser sources
Source
Sigrist M (Ed) (1994)Air Monitoring by Spectroscopic Techniques(Chemical Analysis Seriesvol 197) New York Wiley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(lidar)attach(lidar)plot(rangelogratio)
linesspm 11
linesspm Add a curves to an existing plot
Description
Takes a fittedspm object produced byspm() and adds a curve The function is only appropriatein the case of a single predictor
Usage
linesspm(x)
Arguments
x a fittedspm object as produced byspm()
other graphics parameters described in Appendix B of the SemiPar Usersrsquo Man-ualhttpwwwmathsunsweduau~wandpapershtml
Details
Takes a fittedspm object produced byspm() and adds a curve The function is only appropriatein the case of a single predictor
Value
The function adds a curve to a plot
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fossiltype=n)lines(fit)
12 milanmort
points(fossil)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fossiltype=n)lines(fitcol=greenlwd=5shadecol=mediumpurple1)points(fossilcol=orangepch=16)par(op)
milanmort Milan mortality data
Description
Themilanmort data frame has data on 3652 consecutive days (10 consecutive years 1st Jan-uary 1980 to 30th December 1989) for the city of Milan Italy
Usage
data(milanmort)
Format
This data frame contains the following columns
daynum number of days since 31st December 1979
dayofweek 1=Monday2=Tuesday3=Wednesday4=Thursday 5=Friday6=Saturday7=Sunday
holiday indicator of public holiday 1=public holiday 0=otherwise
meantemp mean daily temperature in degrees Celcius
relhumid relative humidity
totmort total number of deaths
respmort total number of respiratory deaths
SO2 measure of sulphur dioxide level in ambient air
TSP total suspended particles in ambient air
Source
Vigotti MA Rossi G Bisanti L Zanobetti A and Schwartz J (1996) Short term effect ofurban air pollution on respiratory health in Milan Italy 1980-1989Journal of Epidemiology andCommunity Health 50 S71-S75
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
monitormercury 13
Examples
library(SemiPar)data(milanmort)pairs(milanmortpch=)
monitormercury Mercury biomonintoring data
Description
Themonitormercury data frame has 22 observations from sampling locations around a solidwaste incinerator in Warren County New Jersey USA
Usage
data(monitormercury)
Format
This data frame contains the following columns
UTMNorth longitude of sampling location
UTMEast latitude of sampling location
mercuryconcentration mercury concentration in dry sphagnum moss grown at the sampling lo-cation
Source
Opsomer JD Agras J Carpi A and Rodrigues G (1995) An application of locally weightedregression to airborne mercury deposition around an incinerator siteEnvironmetrics 6 205-221
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(monitormercury)pairs(monitormercury)
14 pigweights
onions Onions data
Description
Theonions data frame contains 84 sets of observations from an experiment involving the produc-tion of white Spanish onions in two South Australian locations
Usage
data(onions)
Format
This data frame contains the following columns
dens areal density of plants (plants per square metre)
yield onion yield (grammes per plant)
location indicator of location 0=Purnong Landing 1=Virginia
Source
Ratkowsky D A (1983)Nonlinear Regression Modeling A Unified Practical ApproachNewYork Marcel Dekker
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(onions)attach(onions)pointscols lt- c(redblue)plot(densyieldcol=pointscols[location+1]pch=16)legend(100250c(Purnong LandingVirginia)col=pointscolspch=rep(162))
pigweights Pig weight data
Description
Thepigweights data frame has 9 repeated weight measures on 48 pigs
Usage
data(pigweights)
plotspm 15
Format
This data frame contains the following columns
idnum identification number of pig
numweeks number of weeks since measurements commenced
weight bodyweight of pig idnum after numweeks weeks
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(pigweights)library(lattice)xyplot(weight~numweeksdata=pigweightsgroups=idnumtype=b)
plotspm Semiparametric regression plotting
Description
Takes a fittedspmobject produced byspm() and plots the component smooth functions that makeit up on the scale of the linear predictor
Usage
plotspm(x)
Arguments
x a fittedspm object as produced byspm()
other graphics parameters described in Appendix B of the SemiPar Usersrsquo Man-ualhttpwwwmathsunsweduau~wandpapershtml
Details
Produces plots with each panel corresponding to a component of the semiparametric regressionmodel
Value
The function generates plots
16 predictspm
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fitylim=range(strontiumratio)col=green
lwd=5shadecol=mediumpurple1rugcol=blue)points(agestrontiumratiocol=orangepch=16)par(op)
predictspm Semiparametric regression prediction
Description
Takes a fittedspm object produced byspm() and obtains predictions at new data values
Usage
predictspm(objectnewdatase)
Arguments
object a fittedspm object as produced byspm()
newdata a data frame containing the values of the predictors at which predictions arerequired The columns should have the same name as the predictors
se when this is TRUE standard error estimates are returned for each predictionThe default is FALSE
other arguments
printspm 17
Details
Takes a fittedspm object produced byspm() and obtains predictions at new data values as speci-fied by the lsquonewdatarsquo argument If lsquose=TRUErsquo then standard error estimates are also obtained
Value
If se=FALSE then a vector of predictions at lsquonewdatarsquo is returned If se=TRUE then a list withcomponents named lsquofitrsquo and lsquosersquo is returned The lsquofitrsquo component contains the predictions The lsquosersquocomponent contains standard error estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm plotspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))newdataage lt- dataframe(age=c(90100110120130))preds lt- predict(fitnewdata=newdataagese=TRUE)print(preds)
plot(fitxlim=c(90130))points(unlist(newdataage)preds$fitcol=red)points(unlist(newdataage)preds$fit+2preds$secol=blue)points(unlist(newdataage)preds$fit-2preds$secol=green)
printspm Prints semiparametric regression fit object
Description
Prints a brief description of a semiparametric regression fit object to the screen
Usage
printspm(x)
18 ragweed
Arguments
x a fittedspm object as produced byspm()
other possible arguments
Details
Prints a brief description of a semiparametric regression fit object to the screen
Value
The function prints to the screen
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))print(fit)
ragweed Ragweed data
Description
Theragweed data frame has data on ragweed levels and meteorological variables for 335 days inKalamazoo Michigan USA
Usage
data(ragweed)
residualsspm 19
Format
This data frame contains the following columns
ragweed ragweed level (grains per cubic metre)
year one of 1991 1992 1993 or 1994
dayinseasday number in the current ragweed pollen season
temperature temperature of following day (degrees Fahrenheit)
rain indicator of significant rain the following day 1=at least 3 hours of steady or brief but intenserain 0=otherwise
windspeed wind speed forecast for following day (knots)
Source
Stark P C Ryan L M McDonald J L and Burge H A (1997) Using meteorologic data tomodel and predict daily ragweed pollen levelsAerobiologia 13 177-184
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ragweed)pairs(ragweedpch=)
residualsspm Residuals for semiparametric regression
Description
Extracts residuals from a semiparametric regression fit object
Usage
residualsspm(object)
Arguments
object a fittedspm object as produced byspm()
other possible arguments
Details
Extracts residuals from a semiparametric regression fit object The residuals are defined to be thedifference between the response variable and the fitted values
20 retireplan
Value
The vector of residuals
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(ageresiduals(fit))abline(00)
retireplan Retirement plan data
Description
The retireplan data frame has data on 401(k) retirement plans for employees of 92 firmsmanaged by a company code-named Best Retirement Inc (BRI)
Usage
data(retireplan)
Format
This data frame contains the following columns
contrib contribution to retirement plan at end of first year
group 1=client has group life of group health insurance policy 0=otherwise
turnover employee turnover rate
eligible number of employees eligible to participate in 401(k) plans
vest 1=plan has immediate vesting of employer contributions 0=otherwise
salinity 21
failsafe 1=plan has a fail-safe provision 0=otherwise
match percentage of contributions matched by the employer
salary average annual employee salary in dollars
estimate underwriterrsquos estimate of end-of-year contributions in dollars
susan 1=plan was sold by a sales representative who has been specifically trained to deal exclu-sively with 401(k) plans (code-named Susan Shepard)
Source
Bryant PG and Smith MA (1995)Practical data analysis case studies in business statisticsChicago Irwin
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(retireplan)pairs(retireplan)
salinity Salinity data
Description
The salinity data frame has 28 observations on hydrological measurements from PamlicoSound North Carolina USA
Usage
data(salinity)
Format
This data frame contains the following columns
salinity salinity in Pamlico Sound
laggedsalinity salinity in Pamlico Sound during the previous six weeks
trend trend=1 if the data is the first six-week period of the spring and so forth Used to detectpossible effects of the seasonal warming trend
discharge discharge of fresh water from rivers into the sound
Source
Ruppert D and Carroll RJ (1980) Trimmed least squares estimation in the linear modelJournalof the American Statistical Association 75 828-838
22 sausage
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(salinity)pairs(salinity)
sausage Sausage data
Description
Thesausage data frame has data on 54 lsquohot dogrsquo sausages
Usage
data(sausage)
Format
This data frame contains the following columns
type type of meat
calories number of calories
sodium measure of sodium content
Source
Moore DS and McCabe GP (2003)Introduction to the Practice of Statistics Fourth EditionWH Freeman and Company
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sausage)attach(sausage)pointscols lt- c(redbluegreen)plot(sodiumcaloriescol=pointscols[type]pch=16)legend(200180c(beefporkpoultry)col=pointscolspch=rep(163))
scallop 23
scallop Scallop abundance data
Description
Thescallop data frame has 148 triplets concerning scallop abundance based on a 1990 surveycruise in the Atlantic continental shelf off Long Island New York USA
Usage
data(scallop)
Format
This data frame contains the following columns
latitude degrees latitude (north of the Equator)
longitude degrees longitude (west of Greenwich)
totcatch size of scallop catch at location specified by latitude and longitude
Source
Ecker MD and Heltshe JF (1994) Geostatistical estimates of scallop abundance InCase Studiesin BiometryLange N Ryan L Billard L Brillinger D Conquest L and Greenhouse J (eds)New York John Wiley amp Sons 107-124
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(scallop)pairs(scallop)
sitka Sitka spruce data
Description
Thesitka data frame contains measurements of log-size for 79 Sitka spruce trees grown in nor-mal or ozone-enriched environments Within each year the data are organised in four blocks cor-responding to four controlled environment chambers The first two chambers containing 27 treeseach have an ozone-enriched atmosphere the remaining two containing 12 and 13 trees respec-tively have a normal (control) atmosphere
24 spm
Usage
data(sitka)
Format
This data frame contains the following columns
idnum identification number of tree
order time order ranking within each tree
days time in days since 1st January 1988
logsize tree size measured on a logarithmic scale
ozone indicator ozone treatment 0=control1=ozone
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sitka)attach(sitka)library(lattice)ozonechar lt- rep(controlnrow(sitka))ozonechar[ozone==1] lt- ozonexyplot(logsize~days|ozonechardata=sitkagroups=idnumtype=b)
spm Fit a SemiParametric regression Model
Description
spm is used to fit semiparametric regression models using the mixed model representation of pe-nalized splines (per Ruppert Wand and Carroll 2003)
Usage
spm(formrandom=NULLgroup=NULLfamily=gaussiansparmethod=REMLomitmissing=NULL)
spm 25
Arguments
form a formula describing the model to be fit Note that an intercept is always in-cluded whether given in the formula or not
random random= 1 specifies inclusion of a random intercept according to the groupsspecified by the group argument
group a vector of labels for specifying groups
family for specification of the type of likelihood model assumed in the fitting May begaussianbinomial or poisson
sparmethod method for automatic smoothing parameter selection May be REML (re-stricted maximum likelihood) or ML (maximum likelihood)
omitmissing a logical value indicating whether fields with missing values are to be omitted
Details
See the SemiPar Usersrsquo Manual for details and examples
Value
An list object of classspm containing the fitted model The components are
fit mimics fit object of lme() for family=gaussian and glmmPQL() for family=binomialor family=poisson
info information about the inputs
aux auxiliary information such as variability estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
gam (in package lsquomgcvrsquo)lme (in package lsquonlmersquo)glmmPQL(in package lsquoMASSrsquo)plotspmsummaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)summary(fit)
26 summaryspm
data(califairpoll)attach(califairpoll)fit lt- spm(ozonelevel ~ f(daggettpressuregradient)+
f(inversionbaseheight) +f(inversionbasetemp))
summary(fit)par(mfrow=c(22))plot(fit)
The SemiPar User Manual contains several other examples and details of plotting parameters The current version of the manual is posted on the web-site wwwmathsunsweduau~wandpapershtml
summaryspm Semiparametric regression summary
Description
Takes a fittedspm object produced byspm() and summarises the fit
Usage
summaryspm(object)
Arguments
object a fittedspm object as produced byspm()
other arguments
Details
Produces tables for the linear (parametric) and non-linear (nonparametric) components The lineartable provides coefficient estimates standard errors and p-values The non-linear table providesdegrees of freedom values and other information
Value
The function generates summary tables
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
8 fuelframe
fossil Fossil data
Description
Thefossil data frame has 106 observations on fossil shells
Usage
data(fossil)
Format
This data frame contains the following columns
age age in millions of years
strontiumratio ratios of strontium isotopes
Source
Bralower TJ Fullagar PD Paull CK Dwyer GS and Leckie RM (1997) Mid-cretaceousstrontium-isotope stratigraphy of deep-sea sectionsGeological Society of America Bulletin 1091421-1442
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(fossil)attach(fossil)plot(agestrontiumratio)
fuelframe Automobile data from consumer reports
Description
Thefuelframe data frame contains data on 5 variables (columns) for 117 cars (rows)
Usage
data(fuelframe)
janka 9
Format
This data frame contains the following columns
carname character variable giving the name (make) of the car
Weight the weight of the car in pounds
Disp the engine displacement in litres
Mileage gas mileage in milesgallon
Fuel a derived variable concerning fuel efficiency
Type a factor giving the general type of car The levels are Small Sporty Compact Medium Large Van
Source
Consumer Reports April 1990 pp 235-288
References
Chambers JM and Hastie TJ (eds) (1992)Statistical Models in SWadsworth and Brooks Pacific Grove California
Examples
library(SemiPar)data(fuelframe)pairs(fuelframe)par(mfrow=c(22))fuelfit lt- lm(Fuel ~ Weight + Dispfuelframe)plot(fuelfitask=FALSE)par(mfrow=c(11))
janka Janka hardness data
Description
The janka data frame has 36 observations on Australian timber samples
Usage
data(janka)
Format
This data frame contains the following columns
dens a measure of density of the timber
hardness the Janka hardness (structural property) of the timber
10 lidar
Source
Williams EJ (1959)Regression Analysis New York John Wiley amp Sons
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(janka)attach(janka)plot(denshardness)
lidar LIDAR data
Description
The lidar data frame has 221 observations from a light detection and ranging (LIDAR) experi-ment
Usage
data(lidar)
Format
This data frame contains the following columns
range distance travelled before the light is reflected back to its source
logratio logarithm of the ratio of received light from two laser sources
Source
Sigrist M (Ed) (1994)Air Monitoring by Spectroscopic Techniques(Chemical Analysis Seriesvol 197) New York Wiley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(lidar)attach(lidar)plot(rangelogratio)
linesspm 11
linesspm Add a curves to an existing plot
Description
Takes a fittedspm object produced byspm() and adds a curve The function is only appropriatein the case of a single predictor
Usage
linesspm(x)
Arguments
x a fittedspm object as produced byspm()
other graphics parameters described in Appendix B of the SemiPar Usersrsquo Man-ualhttpwwwmathsunsweduau~wandpapershtml
Details
Takes a fittedspm object produced byspm() and adds a curve The function is only appropriatein the case of a single predictor
Value
The function adds a curve to a plot
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fossiltype=n)lines(fit)
12 milanmort
points(fossil)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fossiltype=n)lines(fitcol=greenlwd=5shadecol=mediumpurple1)points(fossilcol=orangepch=16)par(op)
milanmort Milan mortality data
Description
Themilanmort data frame has data on 3652 consecutive days (10 consecutive years 1st Jan-uary 1980 to 30th December 1989) for the city of Milan Italy
Usage
data(milanmort)
Format
This data frame contains the following columns
daynum number of days since 31st December 1979
dayofweek 1=Monday2=Tuesday3=Wednesday4=Thursday 5=Friday6=Saturday7=Sunday
holiday indicator of public holiday 1=public holiday 0=otherwise
meantemp mean daily temperature in degrees Celcius
relhumid relative humidity
totmort total number of deaths
respmort total number of respiratory deaths
SO2 measure of sulphur dioxide level in ambient air
TSP total suspended particles in ambient air
Source
Vigotti MA Rossi G Bisanti L Zanobetti A and Schwartz J (1996) Short term effect ofurban air pollution on respiratory health in Milan Italy 1980-1989Journal of Epidemiology andCommunity Health 50 S71-S75
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
monitormercury 13
Examples
library(SemiPar)data(milanmort)pairs(milanmortpch=)
monitormercury Mercury biomonintoring data
Description
Themonitormercury data frame has 22 observations from sampling locations around a solidwaste incinerator in Warren County New Jersey USA
Usage
data(monitormercury)
Format
This data frame contains the following columns
UTMNorth longitude of sampling location
UTMEast latitude of sampling location
mercuryconcentration mercury concentration in dry sphagnum moss grown at the sampling lo-cation
Source
Opsomer JD Agras J Carpi A and Rodrigues G (1995) An application of locally weightedregression to airborne mercury deposition around an incinerator siteEnvironmetrics 6 205-221
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(monitormercury)pairs(monitormercury)
14 pigweights
onions Onions data
Description
Theonions data frame contains 84 sets of observations from an experiment involving the produc-tion of white Spanish onions in two South Australian locations
Usage
data(onions)
Format
This data frame contains the following columns
dens areal density of plants (plants per square metre)
yield onion yield (grammes per plant)
location indicator of location 0=Purnong Landing 1=Virginia
Source
Ratkowsky D A (1983)Nonlinear Regression Modeling A Unified Practical ApproachNewYork Marcel Dekker
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(onions)attach(onions)pointscols lt- c(redblue)plot(densyieldcol=pointscols[location+1]pch=16)legend(100250c(Purnong LandingVirginia)col=pointscolspch=rep(162))
pigweights Pig weight data
Description
Thepigweights data frame has 9 repeated weight measures on 48 pigs
Usage
data(pigweights)
plotspm 15
Format
This data frame contains the following columns
idnum identification number of pig
numweeks number of weeks since measurements commenced
weight bodyweight of pig idnum after numweeks weeks
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(pigweights)library(lattice)xyplot(weight~numweeksdata=pigweightsgroups=idnumtype=b)
plotspm Semiparametric regression plotting
Description
Takes a fittedspmobject produced byspm() and plots the component smooth functions that makeit up on the scale of the linear predictor
Usage
plotspm(x)
Arguments
x a fittedspm object as produced byspm()
other graphics parameters described in Appendix B of the SemiPar Usersrsquo Man-ualhttpwwwmathsunsweduau~wandpapershtml
Details
Produces plots with each panel corresponding to a component of the semiparametric regressionmodel
Value
The function generates plots
16 predictspm
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fitylim=range(strontiumratio)col=green
lwd=5shadecol=mediumpurple1rugcol=blue)points(agestrontiumratiocol=orangepch=16)par(op)
predictspm Semiparametric regression prediction
Description
Takes a fittedspm object produced byspm() and obtains predictions at new data values
Usage
predictspm(objectnewdatase)
Arguments
object a fittedspm object as produced byspm()
newdata a data frame containing the values of the predictors at which predictions arerequired The columns should have the same name as the predictors
se when this is TRUE standard error estimates are returned for each predictionThe default is FALSE
other arguments
printspm 17
Details
Takes a fittedspm object produced byspm() and obtains predictions at new data values as speci-fied by the lsquonewdatarsquo argument If lsquose=TRUErsquo then standard error estimates are also obtained
Value
If se=FALSE then a vector of predictions at lsquonewdatarsquo is returned If se=TRUE then a list withcomponents named lsquofitrsquo and lsquosersquo is returned The lsquofitrsquo component contains the predictions The lsquosersquocomponent contains standard error estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm plotspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))newdataage lt- dataframe(age=c(90100110120130))preds lt- predict(fitnewdata=newdataagese=TRUE)print(preds)
plot(fitxlim=c(90130))points(unlist(newdataage)preds$fitcol=red)points(unlist(newdataage)preds$fit+2preds$secol=blue)points(unlist(newdataage)preds$fit-2preds$secol=green)
printspm Prints semiparametric regression fit object
Description
Prints a brief description of a semiparametric regression fit object to the screen
Usage
printspm(x)
18 ragweed
Arguments
x a fittedspm object as produced byspm()
other possible arguments
Details
Prints a brief description of a semiparametric regression fit object to the screen
Value
The function prints to the screen
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))print(fit)
ragweed Ragweed data
Description
Theragweed data frame has data on ragweed levels and meteorological variables for 335 days inKalamazoo Michigan USA
Usage
data(ragweed)
residualsspm 19
Format
This data frame contains the following columns
ragweed ragweed level (grains per cubic metre)
year one of 1991 1992 1993 or 1994
dayinseasday number in the current ragweed pollen season
temperature temperature of following day (degrees Fahrenheit)
rain indicator of significant rain the following day 1=at least 3 hours of steady or brief but intenserain 0=otherwise
windspeed wind speed forecast for following day (knots)
Source
Stark P C Ryan L M McDonald J L and Burge H A (1997) Using meteorologic data tomodel and predict daily ragweed pollen levelsAerobiologia 13 177-184
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ragweed)pairs(ragweedpch=)
residualsspm Residuals for semiparametric regression
Description
Extracts residuals from a semiparametric regression fit object
Usage
residualsspm(object)
Arguments
object a fittedspm object as produced byspm()
other possible arguments
Details
Extracts residuals from a semiparametric regression fit object The residuals are defined to be thedifference between the response variable and the fitted values
20 retireplan
Value
The vector of residuals
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(ageresiduals(fit))abline(00)
retireplan Retirement plan data
Description
The retireplan data frame has data on 401(k) retirement plans for employees of 92 firmsmanaged by a company code-named Best Retirement Inc (BRI)
Usage
data(retireplan)
Format
This data frame contains the following columns
contrib contribution to retirement plan at end of first year
group 1=client has group life of group health insurance policy 0=otherwise
turnover employee turnover rate
eligible number of employees eligible to participate in 401(k) plans
vest 1=plan has immediate vesting of employer contributions 0=otherwise
salinity 21
failsafe 1=plan has a fail-safe provision 0=otherwise
match percentage of contributions matched by the employer
salary average annual employee salary in dollars
estimate underwriterrsquos estimate of end-of-year contributions in dollars
susan 1=plan was sold by a sales representative who has been specifically trained to deal exclu-sively with 401(k) plans (code-named Susan Shepard)
Source
Bryant PG and Smith MA (1995)Practical data analysis case studies in business statisticsChicago Irwin
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(retireplan)pairs(retireplan)
salinity Salinity data
Description
The salinity data frame has 28 observations on hydrological measurements from PamlicoSound North Carolina USA
Usage
data(salinity)
Format
This data frame contains the following columns
salinity salinity in Pamlico Sound
laggedsalinity salinity in Pamlico Sound during the previous six weeks
trend trend=1 if the data is the first six-week period of the spring and so forth Used to detectpossible effects of the seasonal warming trend
discharge discharge of fresh water from rivers into the sound
Source
Ruppert D and Carroll RJ (1980) Trimmed least squares estimation in the linear modelJournalof the American Statistical Association 75 828-838
22 sausage
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(salinity)pairs(salinity)
sausage Sausage data
Description
Thesausage data frame has data on 54 lsquohot dogrsquo sausages
Usage
data(sausage)
Format
This data frame contains the following columns
type type of meat
calories number of calories
sodium measure of sodium content
Source
Moore DS and McCabe GP (2003)Introduction to the Practice of Statistics Fourth EditionWH Freeman and Company
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sausage)attach(sausage)pointscols lt- c(redbluegreen)plot(sodiumcaloriescol=pointscols[type]pch=16)legend(200180c(beefporkpoultry)col=pointscolspch=rep(163))
scallop 23
scallop Scallop abundance data
Description
Thescallop data frame has 148 triplets concerning scallop abundance based on a 1990 surveycruise in the Atlantic continental shelf off Long Island New York USA
Usage
data(scallop)
Format
This data frame contains the following columns
latitude degrees latitude (north of the Equator)
longitude degrees longitude (west of Greenwich)
totcatch size of scallop catch at location specified by latitude and longitude
Source
Ecker MD and Heltshe JF (1994) Geostatistical estimates of scallop abundance InCase Studiesin BiometryLange N Ryan L Billard L Brillinger D Conquest L and Greenhouse J (eds)New York John Wiley amp Sons 107-124
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(scallop)pairs(scallop)
sitka Sitka spruce data
Description
Thesitka data frame contains measurements of log-size for 79 Sitka spruce trees grown in nor-mal or ozone-enriched environments Within each year the data are organised in four blocks cor-responding to four controlled environment chambers The first two chambers containing 27 treeseach have an ozone-enriched atmosphere the remaining two containing 12 and 13 trees respec-tively have a normal (control) atmosphere
24 spm
Usage
data(sitka)
Format
This data frame contains the following columns
idnum identification number of tree
order time order ranking within each tree
days time in days since 1st January 1988
logsize tree size measured on a logarithmic scale
ozone indicator ozone treatment 0=control1=ozone
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sitka)attach(sitka)library(lattice)ozonechar lt- rep(controlnrow(sitka))ozonechar[ozone==1] lt- ozonexyplot(logsize~days|ozonechardata=sitkagroups=idnumtype=b)
spm Fit a SemiParametric regression Model
Description
spm is used to fit semiparametric regression models using the mixed model representation of pe-nalized splines (per Ruppert Wand and Carroll 2003)
Usage
spm(formrandom=NULLgroup=NULLfamily=gaussiansparmethod=REMLomitmissing=NULL)
spm 25
Arguments
form a formula describing the model to be fit Note that an intercept is always in-cluded whether given in the formula or not
random random= 1 specifies inclusion of a random intercept according to the groupsspecified by the group argument
group a vector of labels for specifying groups
family for specification of the type of likelihood model assumed in the fitting May begaussianbinomial or poisson
sparmethod method for automatic smoothing parameter selection May be REML (re-stricted maximum likelihood) or ML (maximum likelihood)
omitmissing a logical value indicating whether fields with missing values are to be omitted
Details
See the SemiPar Usersrsquo Manual for details and examples
Value
An list object of classspm containing the fitted model The components are
fit mimics fit object of lme() for family=gaussian and glmmPQL() for family=binomialor family=poisson
info information about the inputs
aux auxiliary information such as variability estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
gam (in package lsquomgcvrsquo)lme (in package lsquonlmersquo)glmmPQL(in package lsquoMASSrsquo)plotspmsummaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)summary(fit)
26 summaryspm
data(califairpoll)attach(califairpoll)fit lt- spm(ozonelevel ~ f(daggettpressuregradient)+
f(inversionbaseheight) +f(inversionbasetemp))
summary(fit)par(mfrow=c(22))plot(fit)
The SemiPar User Manual contains several other examples and details of plotting parameters The current version of the manual is posted on the web-site wwwmathsunsweduau~wandpapershtml
summaryspm Semiparametric regression summary
Description
Takes a fittedspm object produced byspm() and summarises the fit
Usage
summaryspm(object)
Arguments
object a fittedspm object as produced byspm()
other arguments
Details
Produces tables for the linear (parametric) and non-linear (nonparametric) components The lineartable provides coefficient estimates standard errors and p-values The non-linear table providesdegrees of freedom values and other information
Value
The function generates summary tables
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
janka 9
Format
This data frame contains the following columns
carname character variable giving the name (make) of the car
Weight the weight of the car in pounds
Disp the engine displacement in litres
Mileage gas mileage in milesgallon
Fuel a derived variable concerning fuel efficiency
Type a factor giving the general type of car The levels are Small Sporty Compact Medium Large Van
Source
Consumer Reports April 1990 pp 235-288
References
Chambers JM and Hastie TJ (eds) (1992)Statistical Models in SWadsworth and Brooks Pacific Grove California
Examples
library(SemiPar)data(fuelframe)pairs(fuelframe)par(mfrow=c(22))fuelfit lt- lm(Fuel ~ Weight + Dispfuelframe)plot(fuelfitask=FALSE)par(mfrow=c(11))
janka Janka hardness data
Description
The janka data frame has 36 observations on Australian timber samples
Usage
data(janka)
Format
This data frame contains the following columns
dens a measure of density of the timber
hardness the Janka hardness (structural property) of the timber
10 lidar
Source
Williams EJ (1959)Regression Analysis New York John Wiley amp Sons
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(janka)attach(janka)plot(denshardness)
lidar LIDAR data
Description
The lidar data frame has 221 observations from a light detection and ranging (LIDAR) experi-ment
Usage
data(lidar)
Format
This data frame contains the following columns
range distance travelled before the light is reflected back to its source
logratio logarithm of the ratio of received light from two laser sources
Source
Sigrist M (Ed) (1994)Air Monitoring by Spectroscopic Techniques(Chemical Analysis Seriesvol 197) New York Wiley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(lidar)attach(lidar)plot(rangelogratio)
linesspm 11
linesspm Add a curves to an existing plot
Description
Takes a fittedspm object produced byspm() and adds a curve The function is only appropriatein the case of a single predictor
Usage
linesspm(x)
Arguments
x a fittedspm object as produced byspm()
other graphics parameters described in Appendix B of the SemiPar Usersrsquo Man-ualhttpwwwmathsunsweduau~wandpapershtml
Details
Takes a fittedspm object produced byspm() and adds a curve The function is only appropriatein the case of a single predictor
Value
The function adds a curve to a plot
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fossiltype=n)lines(fit)
12 milanmort
points(fossil)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fossiltype=n)lines(fitcol=greenlwd=5shadecol=mediumpurple1)points(fossilcol=orangepch=16)par(op)
milanmort Milan mortality data
Description
Themilanmort data frame has data on 3652 consecutive days (10 consecutive years 1st Jan-uary 1980 to 30th December 1989) for the city of Milan Italy
Usage
data(milanmort)
Format
This data frame contains the following columns
daynum number of days since 31st December 1979
dayofweek 1=Monday2=Tuesday3=Wednesday4=Thursday 5=Friday6=Saturday7=Sunday
holiday indicator of public holiday 1=public holiday 0=otherwise
meantemp mean daily temperature in degrees Celcius
relhumid relative humidity
totmort total number of deaths
respmort total number of respiratory deaths
SO2 measure of sulphur dioxide level in ambient air
TSP total suspended particles in ambient air
Source
Vigotti MA Rossi G Bisanti L Zanobetti A and Schwartz J (1996) Short term effect ofurban air pollution on respiratory health in Milan Italy 1980-1989Journal of Epidemiology andCommunity Health 50 S71-S75
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
monitormercury 13
Examples
library(SemiPar)data(milanmort)pairs(milanmortpch=)
monitormercury Mercury biomonintoring data
Description
Themonitormercury data frame has 22 observations from sampling locations around a solidwaste incinerator in Warren County New Jersey USA
Usage
data(monitormercury)
Format
This data frame contains the following columns
UTMNorth longitude of sampling location
UTMEast latitude of sampling location
mercuryconcentration mercury concentration in dry sphagnum moss grown at the sampling lo-cation
Source
Opsomer JD Agras J Carpi A and Rodrigues G (1995) An application of locally weightedregression to airborne mercury deposition around an incinerator siteEnvironmetrics 6 205-221
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(monitormercury)pairs(monitormercury)
14 pigweights
onions Onions data
Description
Theonions data frame contains 84 sets of observations from an experiment involving the produc-tion of white Spanish onions in two South Australian locations
Usage
data(onions)
Format
This data frame contains the following columns
dens areal density of plants (plants per square metre)
yield onion yield (grammes per plant)
location indicator of location 0=Purnong Landing 1=Virginia
Source
Ratkowsky D A (1983)Nonlinear Regression Modeling A Unified Practical ApproachNewYork Marcel Dekker
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(onions)attach(onions)pointscols lt- c(redblue)plot(densyieldcol=pointscols[location+1]pch=16)legend(100250c(Purnong LandingVirginia)col=pointscolspch=rep(162))
pigweights Pig weight data
Description
Thepigweights data frame has 9 repeated weight measures on 48 pigs
Usage
data(pigweights)
plotspm 15
Format
This data frame contains the following columns
idnum identification number of pig
numweeks number of weeks since measurements commenced
weight bodyweight of pig idnum after numweeks weeks
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(pigweights)library(lattice)xyplot(weight~numweeksdata=pigweightsgroups=idnumtype=b)
plotspm Semiparametric regression plotting
Description
Takes a fittedspmobject produced byspm() and plots the component smooth functions that makeit up on the scale of the linear predictor
Usage
plotspm(x)
Arguments
x a fittedspm object as produced byspm()
other graphics parameters described in Appendix B of the SemiPar Usersrsquo Man-ualhttpwwwmathsunsweduau~wandpapershtml
Details
Produces plots with each panel corresponding to a component of the semiparametric regressionmodel
Value
The function generates plots
16 predictspm
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fitylim=range(strontiumratio)col=green
lwd=5shadecol=mediumpurple1rugcol=blue)points(agestrontiumratiocol=orangepch=16)par(op)
predictspm Semiparametric regression prediction
Description
Takes a fittedspm object produced byspm() and obtains predictions at new data values
Usage
predictspm(objectnewdatase)
Arguments
object a fittedspm object as produced byspm()
newdata a data frame containing the values of the predictors at which predictions arerequired The columns should have the same name as the predictors
se when this is TRUE standard error estimates are returned for each predictionThe default is FALSE
other arguments
printspm 17
Details
Takes a fittedspm object produced byspm() and obtains predictions at new data values as speci-fied by the lsquonewdatarsquo argument If lsquose=TRUErsquo then standard error estimates are also obtained
Value
If se=FALSE then a vector of predictions at lsquonewdatarsquo is returned If se=TRUE then a list withcomponents named lsquofitrsquo and lsquosersquo is returned The lsquofitrsquo component contains the predictions The lsquosersquocomponent contains standard error estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm plotspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))newdataage lt- dataframe(age=c(90100110120130))preds lt- predict(fitnewdata=newdataagese=TRUE)print(preds)
plot(fitxlim=c(90130))points(unlist(newdataage)preds$fitcol=red)points(unlist(newdataage)preds$fit+2preds$secol=blue)points(unlist(newdataage)preds$fit-2preds$secol=green)
printspm Prints semiparametric regression fit object
Description
Prints a brief description of a semiparametric regression fit object to the screen
Usage
printspm(x)
18 ragweed
Arguments
x a fittedspm object as produced byspm()
other possible arguments
Details
Prints a brief description of a semiparametric regression fit object to the screen
Value
The function prints to the screen
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))print(fit)
ragweed Ragweed data
Description
Theragweed data frame has data on ragweed levels and meteorological variables for 335 days inKalamazoo Michigan USA
Usage
data(ragweed)
residualsspm 19
Format
This data frame contains the following columns
ragweed ragweed level (grains per cubic metre)
year one of 1991 1992 1993 or 1994
dayinseasday number in the current ragweed pollen season
temperature temperature of following day (degrees Fahrenheit)
rain indicator of significant rain the following day 1=at least 3 hours of steady or brief but intenserain 0=otherwise
windspeed wind speed forecast for following day (knots)
Source
Stark P C Ryan L M McDonald J L and Burge H A (1997) Using meteorologic data tomodel and predict daily ragweed pollen levelsAerobiologia 13 177-184
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ragweed)pairs(ragweedpch=)
residualsspm Residuals for semiparametric regression
Description
Extracts residuals from a semiparametric regression fit object
Usage
residualsspm(object)
Arguments
object a fittedspm object as produced byspm()
other possible arguments
Details
Extracts residuals from a semiparametric regression fit object The residuals are defined to be thedifference between the response variable and the fitted values
20 retireplan
Value
The vector of residuals
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(ageresiduals(fit))abline(00)
retireplan Retirement plan data
Description
The retireplan data frame has data on 401(k) retirement plans for employees of 92 firmsmanaged by a company code-named Best Retirement Inc (BRI)
Usage
data(retireplan)
Format
This data frame contains the following columns
contrib contribution to retirement plan at end of first year
group 1=client has group life of group health insurance policy 0=otherwise
turnover employee turnover rate
eligible number of employees eligible to participate in 401(k) plans
vest 1=plan has immediate vesting of employer contributions 0=otherwise
salinity 21
failsafe 1=plan has a fail-safe provision 0=otherwise
match percentage of contributions matched by the employer
salary average annual employee salary in dollars
estimate underwriterrsquos estimate of end-of-year contributions in dollars
susan 1=plan was sold by a sales representative who has been specifically trained to deal exclu-sively with 401(k) plans (code-named Susan Shepard)
Source
Bryant PG and Smith MA (1995)Practical data analysis case studies in business statisticsChicago Irwin
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(retireplan)pairs(retireplan)
salinity Salinity data
Description
The salinity data frame has 28 observations on hydrological measurements from PamlicoSound North Carolina USA
Usage
data(salinity)
Format
This data frame contains the following columns
salinity salinity in Pamlico Sound
laggedsalinity salinity in Pamlico Sound during the previous six weeks
trend trend=1 if the data is the first six-week period of the spring and so forth Used to detectpossible effects of the seasonal warming trend
discharge discharge of fresh water from rivers into the sound
Source
Ruppert D and Carroll RJ (1980) Trimmed least squares estimation in the linear modelJournalof the American Statistical Association 75 828-838
22 sausage
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(salinity)pairs(salinity)
sausage Sausage data
Description
Thesausage data frame has data on 54 lsquohot dogrsquo sausages
Usage
data(sausage)
Format
This data frame contains the following columns
type type of meat
calories number of calories
sodium measure of sodium content
Source
Moore DS and McCabe GP (2003)Introduction to the Practice of Statistics Fourth EditionWH Freeman and Company
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sausage)attach(sausage)pointscols lt- c(redbluegreen)plot(sodiumcaloriescol=pointscols[type]pch=16)legend(200180c(beefporkpoultry)col=pointscolspch=rep(163))
scallop 23
scallop Scallop abundance data
Description
Thescallop data frame has 148 triplets concerning scallop abundance based on a 1990 surveycruise in the Atlantic continental shelf off Long Island New York USA
Usage
data(scallop)
Format
This data frame contains the following columns
latitude degrees latitude (north of the Equator)
longitude degrees longitude (west of Greenwich)
totcatch size of scallop catch at location specified by latitude and longitude
Source
Ecker MD and Heltshe JF (1994) Geostatistical estimates of scallop abundance InCase Studiesin BiometryLange N Ryan L Billard L Brillinger D Conquest L and Greenhouse J (eds)New York John Wiley amp Sons 107-124
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(scallop)pairs(scallop)
sitka Sitka spruce data
Description
Thesitka data frame contains measurements of log-size for 79 Sitka spruce trees grown in nor-mal or ozone-enriched environments Within each year the data are organised in four blocks cor-responding to four controlled environment chambers The first two chambers containing 27 treeseach have an ozone-enriched atmosphere the remaining two containing 12 and 13 trees respec-tively have a normal (control) atmosphere
24 spm
Usage
data(sitka)
Format
This data frame contains the following columns
idnum identification number of tree
order time order ranking within each tree
days time in days since 1st January 1988
logsize tree size measured on a logarithmic scale
ozone indicator ozone treatment 0=control1=ozone
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sitka)attach(sitka)library(lattice)ozonechar lt- rep(controlnrow(sitka))ozonechar[ozone==1] lt- ozonexyplot(logsize~days|ozonechardata=sitkagroups=idnumtype=b)
spm Fit a SemiParametric regression Model
Description
spm is used to fit semiparametric regression models using the mixed model representation of pe-nalized splines (per Ruppert Wand and Carroll 2003)
Usage
spm(formrandom=NULLgroup=NULLfamily=gaussiansparmethod=REMLomitmissing=NULL)
spm 25
Arguments
form a formula describing the model to be fit Note that an intercept is always in-cluded whether given in the formula or not
random random= 1 specifies inclusion of a random intercept according to the groupsspecified by the group argument
group a vector of labels for specifying groups
family for specification of the type of likelihood model assumed in the fitting May begaussianbinomial or poisson
sparmethod method for automatic smoothing parameter selection May be REML (re-stricted maximum likelihood) or ML (maximum likelihood)
omitmissing a logical value indicating whether fields with missing values are to be omitted
Details
See the SemiPar Usersrsquo Manual for details and examples
Value
An list object of classspm containing the fitted model The components are
fit mimics fit object of lme() for family=gaussian and glmmPQL() for family=binomialor family=poisson
info information about the inputs
aux auxiliary information such as variability estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
gam (in package lsquomgcvrsquo)lme (in package lsquonlmersquo)glmmPQL(in package lsquoMASSrsquo)plotspmsummaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)summary(fit)
26 summaryspm
data(califairpoll)attach(califairpoll)fit lt- spm(ozonelevel ~ f(daggettpressuregradient)+
f(inversionbaseheight) +f(inversionbasetemp))
summary(fit)par(mfrow=c(22))plot(fit)
The SemiPar User Manual contains several other examples and details of plotting parameters The current version of the manual is posted on the web-site wwwmathsunsweduau~wandpapershtml
summaryspm Semiparametric regression summary
Description
Takes a fittedspm object produced byspm() and summarises the fit
Usage
summaryspm(object)
Arguments
object a fittedspm object as produced byspm()
other arguments
Details
Produces tables for the linear (parametric) and non-linear (nonparametric) components The lineartable provides coefficient estimates standard errors and p-values The non-linear table providesdegrees of freedom values and other information
Value
The function generates summary tables
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
10 lidar
Source
Williams EJ (1959)Regression Analysis New York John Wiley amp Sons
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(janka)attach(janka)plot(denshardness)
lidar LIDAR data
Description
The lidar data frame has 221 observations from a light detection and ranging (LIDAR) experi-ment
Usage
data(lidar)
Format
This data frame contains the following columns
range distance travelled before the light is reflected back to its source
logratio logarithm of the ratio of received light from two laser sources
Source
Sigrist M (Ed) (1994)Air Monitoring by Spectroscopic Techniques(Chemical Analysis Seriesvol 197) New York Wiley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(lidar)attach(lidar)plot(rangelogratio)
linesspm 11
linesspm Add a curves to an existing plot
Description
Takes a fittedspm object produced byspm() and adds a curve The function is only appropriatein the case of a single predictor
Usage
linesspm(x)
Arguments
x a fittedspm object as produced byspm()
other graphics parameters described in Appendix B of the SemiPar Usersrsquo Man-ualhttpwwwmathsunsweduau~wandpapershtml
Details
Takes a fittedspm object produced byspm() and adds a curve The function is only appropriatein the case of a single predictor
Value
The function adds a curve to a plot
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fossiltype=n)lines(fit)
12 milanmort
points(fossil)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fossiltype=n)lines(fitcol=greenlwd=5shadecol=mediumpurple1)points(fossilcol=orangepch=16)par(op)
milanmort Milan mortality data
Description
Themilanmort data frame has data on 3652 consecutive days (10 consecutive years 1st Jan-uary 1980 to 30th December 1989) for the city of Milan Italy
Usage
data(milanmort)
Format
This data frame contains the following columns
daynum number of days since 31st December 1979
dayofweek 1=Monday2=Tuesday3=Wednesday4=Thursday 5=Friday6=Saturday7=Sunday
holiday indicator of public holiday 1=public holiday 0=otherwise
meantemp mean daily temperature in degrees Celcius
relhumid relative humidity
totmort total number of deaths
respmort total number of respiratory deaths
SO2 measure of sulphur dioxide level in ambient air
TSP total suspended particles in ambient air
Source
Vigotti MA Rossi G Bisanti L Zanobetti A and Schwartz J (1996) Short term effect ofurban air pollution on respiratory health in Milan Italy 1980-1989Journal of Epidemiology andCommunity Health 50 S71-S75
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
monitormercury 13
Examples
library(SemiPar)data(milanmort)pairs(milanmortpch=)
monitormercury Mercury biomonintoring data
Description
Themonitormercury data frame has 22 observations from sampling locations around a solidwaste incinerator in Warren County New Jersey USA
Usage
data(monitormercury)
Format
This data frame contains the following columns
UTMNorth longitude of sampling location
UTMEast latitude of sampling location
mercuryconcentration mercury concentration in dry sphagnum moss grown at the sampling lo-cation
Source
Opsomer JD Agras J Carpi A and Rodrigues G (1995) An application of locally weightedregression to airborne mercury deposition around an incinerator siteEnvironmetrics 6 205-221
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(monitormercury)pairs(monitormercury)
14 pigweights
onions Onions data
Description
Theonions data frame contains 84 sets of observations from an experiment involving the produc-tion of white Spanish onions in two South Australian locations
Usage
data(onions)
Format
This data frame contains the following columns
dens areal density of plants (plants per square metre)
yield onion yield (grammes per plant)
location indicator of location 0=Purnong Landing 1=Virginia
Source
Ratkowsky D A (1983)Nonlinear Regression Modeling A Unified Practical ApproachNewYork Marcel Dekker
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(onions)attach(onions)pointscols lt- c(redblue)plot(densyieldcol=pointscols[location+1]pch=16)legend(100250c(Purnong LandingVirginia)col=pointscolspch=rep(162))
pigweights Pig weight data
Description
Thepigweights data frame has 9 repeated weight measures on 48 pigs
Usage
data(pigweights)
plotspm 15
Format
This data frame contains the following columns
idnum identification number of pig
numweeks number of weeks since measurements commenced
weight bodyweight of pig idnum after numweeks weeks
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(pigweights)library(lattice)xyplot(weight~numweeksdata=pigweightsgroups=idnumtype=b)
plotspm Semiparametric regression plotting
Description
Takes a fittedspmobject produced byspm() and plots the component smooth functions that makeit up on the scale of the linear predictor
Usage
plotspm(x)
Arguments
x a fittedspm object as produced byspm()
other graphics parameters described in Appendix B of the SemiPar Usersrsquo Man-ualhttpwwwmathsunsweduau~wandpapershtml
Details
Produces plots with each panel corresponding to a component of the semiparametric regressionmodel
Value
The function generates plots
16 predictspm
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fitylim=range(strontiumratio)col=green
lwd=5shadecol=mediumpurple1rugcol=blue)points(agestrontiumratiocol=orangepch=16)par(op)
predictspm Semiparametric regression prediction
Description
Takes a fittedspm object produced byspm() and obtains predictions at new data values
Usage
predictspm(objectnewdatase)
Arguments
object a fittedspm object as produced byspm()
newdata a data frame containing the values of the predictors at which predictions arerequired The columns should have the same name as the predictors
se when this is TRUE standard error estimates are returned for each predictionThe default is FALSE
other arguments
printspm 17
Details
Takes a fittedspm object produced byspm() and obtains predictions at new data values as speci-fied by the lsquonewdatarsquo argument If lsquose=TRUErsquo then standard error estimates are also obtained
Value
If se=FALSE then a vector of predictions at lsquonewdatarsquo is returned If se=TRUE then a list withcomponents named lsquofitrsquo and lsquosersquo is returned The lsquofitrsquo component contains the predictions The lsquosersquocomponent contains standard error estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm plotspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))newdataage lt- dataframe(age=c(90100110120130))preds lt- predict(fitnewdata=newdataagese=TRUE)print(preds)
plot(fitxlim=c(90130))points(unlist(newdataage)preds$fitcol=red)points(unlist(newdataage)preds$fit+2preds$secol=blue)points(unlist(newdataage)preds$fit-2preds$secol=green)
printspm Prints semiparametric regression fit object
Description
Prints a brief description of a semiparametric regression fit object to the screen
Usage
printspm(x)
18 ragweed
Arguments
x a fittedspm object as produced byspm()
other possible arguments
Details
Prints a brief description of a semiparametric regression fit object to the screen
Value
The function prints to the screen
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))print(fit)
ragweed Ragweed data
Description
Theragweed data frame has data on ragweed levels and meteorological variables for 335 days inKalamazoo Michigan USA
Usage
data(ragweed)
residualsspm 19
Format
This data frame contains the following columns
ragweed ragweed level (grains per cubic metre)
year one of 1991 1992 1993 or 1994
dayinseasday number in the current ragweed pollen season
temperature temperature of following day (degrees Fahrenheit)
rain indicator of significant rain the following day 1=at least 3 hours of steady or brief but intenserain 0=otherwise
windspeed wind speed forecast for following day (knots)
Source
Stark P C Ryan L M McDonald J L and Burge H A (1997) Using meteorologic data tomodel and predict daily ragweed pollen levelsAerobiologia 13 177-184
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ragweed)pairs(ragweedpch=)
residualsspm Residuals for semiparametric regression
Description
Extracts residuals from a semiparametric regression fit object
Usage
residualsspm(object)
Arguments
object a fittedspm object as produced byspm()
other possible arguments
Details
Extracts residuals from a semiparametric regression fit object The residuals are defined to be thedifference between the response variable and the fitted values
20 retireplan
Value
The vector of residuals
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(ageresiduals(fit))abline(00)
retireplan Retirement plan data
Description
The retireplan data frame has data on 401(k) retirement plans for employees of 92 firmsmanaged by a company code-named Best Retirement Inc (BRI)
Usage
data(retireplan)
Format
This data frame contains the following columns
contrib contribution to retirement plan at end of first year
group 1=client has group life of group health insurance policy 0=otherwise
turnover employee turnover rate
eligible number of employees eligible to participate in 401(k) plans
vest 1=plan has immediate vesting of employer contributions 0=otherwise
salinity 21
failsafe 1=plan has a fail-safe provision 0=otherwise
match percentage of contributions matched by the employer
salary average annual employee salary in dollars
estimate underwriterrsquos estimate of end-of-year contributions in dollars
susan 1=plan was sold by a sales representative who has been specifically trained to deal exclu-sively with 401(k) plans (code-named Susan Shepard)
Source
Bryant PG and Smith MA (1995)Practical data analysis case studies in business statisticsChicago Irwin
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(retireplan)pairs(retireplan)
salinity Salinity data
Description
The salinity data frame has 28 observations on hydrological measurements from PamlicoSound North Carolina USA
Usage
data(salinity)
Format
This data frame contains the following columns
salinity salinity in Pamlico Sound
laggedsalinity salinity in Pamlico Sound during the previous six weeks
trend trend=1 if the data is the first six-week period of the spring and so forth Used to detectpossible effects of the seasonal warming trend
discharge discharge of fresh water from rivers into the sound
Source
Ruppert D and Carroll RJ (1980) Trimmed least squares estimation in the linear modelJournalof the American Statistical Association 75 828-838
22 sausage
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(salinity)pairs(salinity)
sausage Sausage data
Description
Thesausage data frame has data on 54 lsquohot dogrsquo sausages
Usage
data(sausage)
Format
This data frame contains the following columns
type type of meat
calories number of calories
sodium measure of sodium content
Source
Moore DS and McCabe GP (2003)Introduction to the Practice of Statistics Fourth EditionWH Freeman and Company
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sausage)attach(sausage)pointscols lt- c(redbluegreen)plot(sodiumcaloriescol=pointscols[type]pch=16)legend(200180c(beefporkpoultry)col=pointscolspch=rep(163))
scallop 23
scallop Scallop abundance data
Description
Thescallop data frame has 148 triplets concerning scallop abundance based on a 1990 surveycruise in the Atlantic continental shelf off Long Island New York USA
Usage
data(scallop)
Format
This data frame contains the following columns
latitude degrees latitude (north of the Equator)
longitude degrees longitude (west of Greenwich)
totcatch size of scallop catch at location specified by latitude and longitude
Source
Ecker MD and Heltshe JF (1994) Geostatistical estimates of scallop abundance InCase Studiesin BiometryLange N Ryan L Billard L Brillinger D Conquest L and Greenhouse J (eds)New York John Wiley amp Sons 107-124
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(scallop)pairs(scallop)
sitka Sitka spruce data
Description
Thesitka data frame contains measurements of log-size for 79 Sitka spruce trees grown in nor-mal or ozone-enriched environments Within each year the data are organised in four blocks cor-responding to four controlled environment chambers The first two chambers containing 27 treeseach have an ozone-enriched atmosphere the remaining two containing 12 and 13 trees respec-tively have a normal (control) atmosphere
24 spm
Usage
data(sitka)
Format
This data frame contains the following columns
idnum identification number of tree
order time order ranking within each tree
days time in days since 1st January 1988
logsize tree size measured on a logarithmic scale
ozone indicator ozone treatment 0=control1=ozone
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sitka)attach(sitka)library(lattice)ozonechar lt- rep(controlnrow(sitka))ozonechar[ozone==1] lt- ozonexyplot(logsize~days|ozonechardata=sitkagroups=idnumtype=b)
spm Fit a SemiParametric regression Model
Description
spm is used to fit semiparametric regression models using the mixed model representation of pe-nalized splines (per Ruppert Wand and Carroll 2003)
Usage
spm(formrandom=NULLgroup=NULLfamily=gaussiansparmethod=REMLomitmissing=NULL)
spm 25
Arguments
form a formula describing the model to be fit Note that an intercept is always in-cluded whether given in the formula or not
random random= 1 specifies inclusion of a random intercept according to the groupsspecified by the group argument
group a vector of labels for specifying groups
family for specification of the type of likelihood model assumed in the fitting May begaussianbinomial or poisson
sparmethod method for automatic smoothing parameter selection May be REML (re-stricted maximum likelihood) or ML (maximum likelihood)
omitmissing a logical value indicating whether fields with missing values are to be omitted
Details
See the SemiPar Usersrsquo Manual for details and examples
Value
An list object of classspm containing the fitted model The components are
fit mimics fit object of lme() for family=gaussian and glmmPQL() for family=binomialor family=poisson
info information about the inputs
aux auxiliary information such as variability estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
gam (in package lsquomgcvrsquo)lme (in package lsquonlmersquo)glmmPQL(in package lsquoMASSrsquo)plotspmsummaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)summary(fit)
26 summaryspm
data(califairpoll)attach(califairpoll)fit lt- spm(ozonelevel ~ f(daggettpressuregradient)+
f(inversionbaseheight) +f(inversionbasetemp))
summary(fit)par(mfrow=c(22))plot(fit)
The SemiPar User Manual contains several other examples and details of plotting parameters The current version of the manual is posted on the web-site wwwmathsunsweduau~wandpapershtml
summaryspm Semiparametric regression summary
Description
Takes a fittedspm object produced byspm() and summarises the fit
Usage
summaryspm(object)
Arguments
object a fittedspm object as produced byspm()
other arguments
Details
Produces tables for the linear (parametric) and non-linear (nonparametric) components The lineartable provides coefficient estimates standard errors and p-values The non-linear table providesdegrees of freedom values and other information
Value
The function generates summary tables
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
linesspm 11
linesspm Add a curves to an existing plot
Description
Takes a fittedspm object produced byspm() and adds a curve The function is only appropriatein the case of a single predictor
Usage
linesspm(x)
Arguments
x a fittedspm object as produced byspm()
other graphics parameters described in Appendix B of the SemiPar Usersrsquo Man-ualhttpwwwmathsunsweduau~wandpapershtml
Details
Takes a fittedspm object produced byspm() and adds a curve The function is only appropriatein the case of a single predictor
Value
The function adds a curve to a plot
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fossiltype=n)lines(fit)
12 milanmort
points(fossil)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fossiltype=n)lines(fitcol=greenlwd=5shadecol=mediumpurple1)points(fossilcol=orangepch=16)par(op)
milanmort Milan mortality data
Description
Themilanmort data frame has data on 3652 consecutive days (10 consecutive years 1st Jan-uary 1980 to 30th December 1989) for the city of Milan Italy
Usage
data(milanmort)
Format
This data frame contains the following columns
daynum number of days since 31st December 1979
dayofweek 1=Monday2=Tuesday3=Wednesday4=Thursday 5=Friday6=Saturday7=Sunday
holiday indicator of public holiday 1=public holiday 0=otherwise
meantemp mean daily temperature in degrees Celcius
relhumid relative humidity
totmort total number of deaths
respmort total number of respiratory deaths
SO2 measure of sulphur dioxide level in ambient air
TSP total suspended particles in ambient air
Source
Vigotti MA Rossi G Bisanti L Zanobetti A and Schwartz J (1996) Short term effect ofurban air pollution on respiratory health in Milan Italy 1980-1989Journal of Epidemiology andCommunity Health 50 S71-S75
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
monitormercury 13
Examples
library(SemiPar)data(milanmort)pairs(milanmortpch=)
monitormercury Mercury biomonintoring data
Description
Themonitormercury data frame has 22 observations from sampling locations around a solidwaste incinerator in Warren County New Jersey USA
Usage
data(monitormercury)
Format
This data frame contains the following columns
UTMNorth longitude of sampling location
UTMEast latitude of sampling location
mercuryconcentration mercury concentration in dry sphagnum moss grown at the sampling lo-cation
Source
Opsomer JD Agras J Carpi A and Rodrigues G (1995) An application of locally weightedregression to airborne mercury deposition around an incinerator siteEnvironmetrics 6 205-221
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(monitormercury)pairs(monitormercury)
14 pigweights
onions Onions data
Description
Theonions data frame contains 84 sets of observations from an experiment involving the produc-tion of white Spanish onions in two South Australian locations
Usage
data(onions)
Format
This data frame contains the following columns
dens areal density of plants (plants per square metre)
yield onion yield (grammes per plant)
location indicator of location 0=Purnong Landing 1=Virginia
Source
Ratkowsky D A (1983)Nonlinear Regression Modeling A Unified Practical ApproachNewYork Marcel Dekker
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(onions)attach(onions)pointscols lt- c(redblue)plot(densyieldcol=pointscols[location+1]pch=16)legend(100250c(Purnong LandingVirginia)col=pointscolspch=rep(162))
pigweights Pig weight data
Description
Thepigweights data frame has 9 repeated weight measures on 48 pigs
Usage
data(pigweights)
plotspm 15
Format
This data frame contains the following columns
idnum identification number of pig
numweeks number of weeks since measurements commenced
weight bodyweight of pig idnum after numweeks weeks
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(pigweights)library(lattice)xyplot(weight~numweeksdata=pigweightsgroups=idnumtype=b)
plotspm Semiparametric regression plotting
Description
Takes a fittedspmobject produced byspm() and plots the component smooth functions that makeit up on the scale of the linear predictor
Usage
plotspm(x)
Arguments
x a fittedspm object as produced byspm()
other graphics parameters described in Appendix B of the SemiPar Usersrsquo Man-ualhttpwwwmathsunsweduau~wandpapershtml
Details
Produces plots with each panel corresponding to a component of the semiparametric regressionmodel
Value
The function generates plots
16 predictspm
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fitylim=range(strontiumratio)col=green
lwd=5shadecol=mediumpurple1rugcol=blue)points(agestrontiumratiocol=orangepch=16)par(op)
predictspm Semiparametric regression prediction
Description
Takes a fittedspm object produced byspm() and obtains predictions at new data values
Usage
predictspm(objectnewdatase)
Arguments
object a fittedspm object as produced byspm()
newdata a data frame containing the values of the predictors at which predictions arerequired The columns should have the same name as the predictors
se when this is TRUE standard error estimates are returned for each predictionThe default is FALSE
other arguments
printspm 17
Details
Takes a fittedspm object produced byspm() and obtains predictions at new data values as speci-fied by the lsquonewdatarsquo argument If lsquose=TRUErsquo then standard error estimates are also obtained
Value
If se=FALSE then a vector of predictions at lsquonewdatarsquo is returned If se=TRUE then a list withcomponents named lsquofitrsquo and lsquosersquo is returned The lsquofitrsquo component contains the predictions The lsquosersquocomponent contains standard error estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm plotspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))newdataage lt- dataframe(age=c(90100110120130))preds lt- predict(fitnewdata=newdataagese=TRUE)print(preds)
plot(fitxlim=c(90130))points(unlist(newdataage)preds$fitcol=red)points(unlist(newdataage)preds$fit+2preds$secol=blue)points(unlist(newdataage)preds$fit-2preds$secol=green)
printspm Prints semiparametric regression fit object
Description
Prints a brief description of a semiparametric regression fit object to the screen
Usage
printspm(x)
18 ragweed
Arguments
x a fittedspm object as produced byspm()
other possible arguments
Details
Prints a brief description of a semiparametric regression fit object to the screen
Value
The function prints to the screen
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))print(fit)
ragweed Ragweed data
Description
Theragweed data frame has data on ragweed levels and meteorological variables for 335 days inKalamazoo Michigan USA
Usage
data(ragweed)
residualsspm 19
Format
This data frame contains the following columns
ragweed ragweed level (grains per cubic metre)
year one of 1991 1992 1993 or 1994
dayinseasday number in the current ragweed pollen season
temperature temperature of following day (degrees Fahrenheit)
rain indicator of significant rain the following day 1=at least 3 hours of steady or brief but intenserain 0=otherwise
windspeed wind speed forecast for following day (knots)
Source
Stark P C Ryan L M McDonald J L and Burge H A (1997) Using meteorologic data tomodel and predict daily ragweed pollen levelsAerobiologia 13 177-184
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ragweed)pairs(ragweedpch=)
residualsspm Residuals for semiparametric regression
Description
Extracts residuals from a semiparametric regression fit object
Usage
residualsspm(object)
Arguments
object a fittedspm object as produced byspm()
other possible arguments
Details
Extracts residuals from a semiparametric regression fit object The residuals are defined to be thedifference between the response variable and the fitted values
20 retireplan
Value
The vector of residuals
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(ageresiduals(fit))abline(00)
retireplan Retirement plan data
Description
The retireplan data frame has data on 401(k) retirement plans for employees of 92 firmsmanaged by a company code-named Best Retirement Inc (BRI)
Usage
data(retireplan)
Format
This data frame contains the following columns
contrib contribution to retirement plan at end of first year
group 1=client has group life of group health insurance policy 0=otherwise
turnover employee turnover rate
eligible number of employees eligible to participate in 401(k) plans
vest 1=plan has immediate vesting of employer contributions 0=otherwise
salinity 21
failsafe 1=plan has a fail-safe provision 0=otherwise
match percentage of contributions matched by the employer
salary average annual employee salary in dollars
estimate underwriterrsquos estimate of end-of-year contributions in dollars
susan 1=plan was sold by a sales representative who has been specifically trained to deal exclu-sively with 401(k) plans (code-named Susan Shepard)
Source
Bryant PG and Smith MA (1995)Practical data analysis case studies in business statisticsChicago Irwin
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(retireplan)pairs(retireplan)
salinity Salinity data
Description
The salinity data frame has 28 observations on hydrological measurements from PamlicoSound North Carolina USA
Usage
data(salinity)
Format
This data frame contains the following columns
salinity salinity in Pamlico Sound
laggedsalinity salinity in Pamlico Sound during the previous six weeks
trend trend=1 if the data is the first six-week period of the spring and so forth Used to detectpossible effects of the seasonal warming trend
discharge discharge of fresh water from rivers into the sound
Source
Ruppert D and Carroll RJ (1980) Trimmed least squares estimation in the linear modelJournalof the American Statistical Association 75 828-838
22 sausage
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(salinity)pairs(salinity)
sausage Sausage data
Description
Thesausage data frame has data on 54 lsquohot dogrsquo sausages
Usage
data(sausage)
Format
This data frame contains the following columns
type type of meat
calories number of calories
sodium measure of sodium content
Source
Moore DS and McCabe GP (2003)Introduction to the Practice of Statistics Fourth EditionWH Freeman and Company
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sausage)attach(sausage)pointscols lt- c(redbluegreen)plot(sodiumcaloriescol=pointscols[type]pch=16)legend(200180c(beefporkpoultry)col=pointscolspch=rep(163))
scallop 23
scallop Scallop abundance data
Description
Thescallop data frame has 148 triplets concerning scallop abundance based on a 1990 surveycruise in the Atlantic continental shelf off Long Island New York USA
Usage
data(scallop)
Format
This data frame contains the following columns
latitude degrees latitude (north of the Equator)
longitude degrees longitude (west of Greenwich)
totcatch size of scallop catch at location specified by latitude and longitude
Source
Ecker MD and Heltshe JF (1994) Geostatistical estimates of scallop abundance InCase Studiesin BiometryLange N Ryan L Billard L Brillinger D Conquest L and Greenhouse J (eds)New York John Wiley amp Sons 107-124
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(scallop)pairs(scallop)
sitka Sitka spruce data
Description
Thesitka data frame contains measurements of log-size for 79 Sitka spruce trees grown in nor-mal or ozone-enriched environments Within each year the data are organised in four blocks cor-responding to four controlled environment chambers The first two chambers containing 27 treeseach have an ozone-enriched atmosphere the remaining two containing 12 and 13 trees respec-tively have a normal (control) atmosphere
24 spm
Usage
data(sitka)
Format
This data frame contains the following columns
idnum identification number of tree
order time order ranking within each tree
days time in days since 1st January 1988
logsize tree size measured on a logarithmic scale
ozone indicator ozone treatment 0=control1=ozone
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sitka)attach(sitka)library(lattice)ozonechar lt- rep(controlnrow(sitka))ozonechar[ozone==1] lt- ozonexyplot(logsize~days|ozonechardata=sitkagroups=idnumtype=b)
spm Fit a SemiParametric regression Model
Description
spm is used to fit semiparametric regression models using the mixed model representation of pe-nalized splines (per Ruppert Wand and Carroll 2003)
Usage
spm(formrandom=NULLgroup=NULLfamily=gaussiansparmethod=REMLomitmissing=NULL)
spm 25
Arguments
form a formula describing the model to be fit Note that an intercept is always in-cluded whether given in the formula or not
random random= 1 specifies inclusion of a random intercept according to the groupsspecified by the group argument
group a vector of labels for specifying groups
family for specification of the type of likelihood model assumed in the fitting May begaussianbinomial or poisson
sparmethod method for automatic smoothing parameter selection May be REML (re-stricted maximum likelihood) or ML (maximum likelihood)
omitmissing a logical value indicating whether fields with missing values are to be omitted
Details
See the SemiPar Usersrsquo Manual for details and examples
Value
An list object of classspm containing the fitted model The components are
fit mimics fit object of lme() for family=gaussian and glmmPQL() for family=binomialor family=poisson
info information about the inputs
aux auxiliary information such as variability estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
gam (in package lsquomgcvrsquo)lme (in package lsquonlmersquo)glmmPQL(in package lsquoMASSrsquo)plotspmsummaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)summary(fit)
26 summaryspm
data(califairpoll)attach(califairpoll)fit lt- spm(ozonelevel ~ f(daggettpressuregradient)+
f(inversionbaseheight) +f(inversionbasetemp))
summary(fit)par(mfrow=c(22))plot(fit)
The SemiPar User Manual contains several other examples and details of plotting parameters The current version of the manual is posted on the web-site wwwmathsunsweduau~wandpapershtml
summaryspm Semiparametric regression summary
Description
Takes a fittedspm object produced byspm() and summarises the fit
Usage
summaryspm(object)
Arguments
object a fittedspm object as produced byspm()
other arguments
Details
Produces tables for the linear (parametric) and non-linear (nonparametric) components The lineartable provides coefficient estimates standard errors and p-values The non-linear table providesdegrees of freedom values and other information
Value
The function generates summary tables
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
12 milanmort
points(fossil)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fossiltype=n)lines(fitcol=greenlwd=5shadecol=mediumpurple1)points(fossilcol=orangepch=16)par(op)
milanmort Milan mortality data
Description
Themilanmort data frame has data on 3652 consecutive days (10 consecutive years 1st Jan-uary 1980 to 30th December 1989) for the city of Milan Italy
Usage
data(milanmort)
Format
This data frame contains the following columns
daynum number of days since 31st December 1979
dayofweek 1=Monday2=Tuesday3=Wednesday4=Thursday 5=Friday6=Saturday7=Sunday
holiday indicator of public holiday 1=public holiday 0=otherwise
meantemp mean daily temperature in degrees Celcius
relhumid relative humidity
totmort total number of deaths
respmort total number of respiratory deaths
SO2 measure of sulphur dioxide level in ambient air
TSP total suspended particles in ambient air
Source
Vigotti MA Rossi G Bisanti L Zanobetti A and Schwartz J (1996) Short term effect ofurban air pollution on respiratory health in Milan Italy 1980-1989Journal of Epidemiology andCommunity Health 50 S71-S75
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
monitormercury 13
Examples
library(SemiPar)data(milanmort)pairs(milanmortpch=)
monitormercury Mercury biomonintoring data
Description
Themonitormercury data frame has 22 observations from sampling locations around a solidwaste incinerator in Warren County New Jersey USA
Usage
data(monitormercury)
Format
This data frame contains the following columns
UTMNorth longitude of sampling location
UTMEast latitude of sampling location
mercuryconcentration mercury concentration in dry sphagnum moss grown at the sampling lo-cation
Source
Opsomer JD Agras J Carpi A and Rodrigues G (1995) An application of locally weightedregression to airborne mercury deposition around an incinerator siteEnvironmetrics 6 205-221
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(monitormercury)pairs(monitormercury)
14 pigweights
onions Onions data
Description
Theonions data frame contains 84 sets of observations from an experiment involving the produc-tion of white Spanish onions in two South Australian locations
Usage
data(onions)
Format
This data frame contains the following columns
dens areal density of plants (plants per square metre)
yield onion yield (grammes per plant)
location indicator of location 0=Purnong Landing 1=Virginia
Source
Ratkowsky D A (1983)Nonlinear Regression Modeling A Unified Practical ApproachNewYork Marcel Dekker
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(onions)attach(onions)pointscols lt- c(redblue)plot(densyieldcol=pointscols[location+1]pch=16)legend(100250c(Purnong LandingVirginia)col=pointscolspch=rep(162))
pigweights Pig weight data
Description
Thepigweights data frame has 9 repeated weight measures on 48 pigs
Usage
data(pigweights)
plotspm 15
Format
This data frame contains the following columns
idnum identification number of pig
numweeks number of weeks since measurements commenced
weight bodyweight of pig idnum after numweeks weeks
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(pigweights)library(lattice)xyplot(weight~numweeksdata=pigweightsgroups=idnumtype=b)
plotspm Semiparametric regression plotting
Description
Takes a fittedspmobject produced byspm() and plots the component smooth functions that makeit up on the scale of the linear predictor
Usage
plotspm(x)
Arguments
x a fittedspm object as produced byspm()
other graphics parameters described in Appendix B of the SemiPar Usersrsquo Man-ualhttpwwwmathsunsweduau~wandpapershtml
Details
Produces plots with each panel corresponding to a component of the semiparametric regressionmodel
Value
The function generates plots
16 predictspm
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fitylim=range(strontiumratio)col=green
lwd=5shadecol=mediumpurple1rugcol=blue)points(agestrontiumratiocol=orangepch=16)par(op)
predictspm Semiparametric regression prediction
Description
Takes a fittedspm object produced byspm() and obtains predictions at new data values
Usage
predictspm(objectnewdatase)
Arguments
object a fittedspm object as produced byspm()
newdata a data frame containing the values of the predictors at which predictions arerequired The columns should have the same name as the predictors
se when this is TRUE standard error estimates are returned for each predictionThe default is FALSE
other arguments
printspm 17
Details
Takes a fittedspm object produced byspm() and obtains predictions at new data values as speci-fied by the lsquonewdatarsquo argument If lsquose=TRUErsquo then standard error estimates are also obtained
Value
If se=FALSE then a vector of predictions at lsquonewdatarsquo is returned If se=TRUE then a list withcomponents named lsquofitrsquo and lsquosersquo is returned The lsquofitrsquo component contains the predictions The lsquosersquocomponent contains standard error estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm plotspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))newdataage lt- dataframe(age=c(90100110120130))preds lt- predict(fitnewdata=newdataagese=TRUE)print(preds)
plot(fitxlim=c(90130))points(unlist(newdataage)preds$fitcol=red)points(unlist(newdataage)preds$fit+2preds$secol=blue)points(unlist(newdataage)preds$fit-2preds$secol=green)
printspm Prints semiparametric regression fit object
Description
Prints a brief description of a semiparametric regression fit object to the screen
Usage
printspm(x)
18 ragweed
Arguments
x a fittedspm object as produced byspm()
other possible arguments
Details
Prints a brief description of a semiparametric regression fit object to the screen
Value
The function prints to the screen
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))print(fit)
ragweed Ragweed data
Description
Theragweed data frame has data on ragweed levels and meteorological variables for 335 days inKalamazoo Michigan USA
Usage
data(ragweed)
residualsspm 19
Format
This data frame contains the following columns
ragweed ragweed level (grains per cubic metre)
year one of 1991 1992 1993 or 1994
dayinseasday number in the current ragweed pollen season
temperature temperature of following day (degrees Fahrenheit)
rain indicator of significant rain the following day 1=at least 3 hours of steady or brief but intenserain 0=otherwise
windspeed wind speed forecast for following day (knots)
Source
Stark P C Ryan L M McDonald J L and Burge H A (1997) Using meteorologic data tomodel and predict daily ragweed pollen levelsAerobiologia 13 177-184
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ragweed)pairs(ragweedpch=)
residualsspm Residuals for semiparametric regression
Description
Extracts residuals from a semiparametric regression fit object
Usage
residualsspm(object)
Arguments
object a fittedspm object as produced byspm()
other possible arguments
Details
Extracts residuals from a semiparametric regression fit object The residuals are defined to be thedifference between the response variable and the fitted values
20 retireplan
Value
The vector of residuals
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(ageresiduals(fit))abline(00)
retireplan Retirement plan data
Description
The retireplan data frame has data on 401(k) retirement plans for employees of 92 firmsmanaged by a company code-named Best Retirement Inc (BRI)
Usage
data(retireplan)
Format
This data frame contains the following columns
contrib contribution to retirement plan at end of first year
group 1=client has group life of group health insurance policy 0=otherwise
turnover employee turnover rate
eligible number of employees eligible to participate in 401(k) plans
vest 1=plan has immediate vesting of employer contributions 0=otherwise
salinity 21
failsafe 1=plan has a fail-safe provision 0=otherwise
match percentage of contributions matched by the employer
salary average annual employee salary in dollars
estimate underwriterrsquos estimate of end-of-year contributions in dollars
susan 1=plan was sold by a sales representative who has been specifically trained to deal exclu-sively with 401(k) plans (code-named Susan Shepard)
Source
Bryant PG and Smith MA (1995)Practical data analysis case studies in business statisticsChicago Irwin
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(retireplan)pairs(retireplan)
salinity Salinity data
Description
The salinity data frame has 28 observations on hydrological measurements from PamlicoSound North Carolina USA
Usage
data(salinity)
Format
This data frame contains the following columns
salinity salinity in Pamlico Sound
laggedsalinity salinity in Pamlico Sound during the previous six weeks
trend trend=1 if the data is the first six-week period of the spring and so forth Used to detectpossible effects of the seasonal warming trend
discharge discharge of fresh water from rivers into the sound
Source
Ruppert D and Carroll RJ (1980) Trimmed least squares estimation in the linear modelJournalof the American Statistical Association 75 828-838
22 sausage
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(salinity)pairs(salinity)
sausage Sausage data
Description
Thesausage data frame has data on 54 lsquohot dogrsquo sausages
Usage
data(sausage)
Format
This data frame contains the following columns
type type of meat
calories number of calories
sodium measure of sodium content
Source
Moore DS and McCabe GP (2003)Introduction to the Practice of Statistics Fourth EditionWH Freeman and Company
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sausage)attach(sausage)pointscols lt- c(redbluegreen)plot(sodiumcaloriescol=pointscols[type]pch=16)legend(200180c(beefporkpoultry)col=pointscolspch=rep(163))
scallop 23
scallop Scallop abundance data
Description
Thescallop data frame has 148 triplets concerning scallop abundance based on a 1990 surveycruise in the Atlantic continental shelf off Long Island New York USA
Usage
data(scallop)
Format
This data frame contains the following columns
latitude degrees latitude (north of the Equator)
longitude degrees longitude (west of Greenwich)
totcatch size of scallop catch at location specified by latitude and longitude
Source
Ecker MD and Heltshe JF (1994) Geostatistical estimates of scallop abundance InCase Studiesin BiometryLange N Ryan L Billard L Brillinger D Conquest L and Greenhouse J (eds)New York John Wiley amp Sons 107-124
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(scallop)pairs(scallop)
sitka Sitka spruce data
Description
Thesitka data frame contains measurements of log-size for 79 Sitka spruce trees grown in nor-mal or ozone-enriched environments Within each year the data are organised in four blocks cor-responding to four controlled environment chambers The first two chambers containing 27 treeseach have an ozone-enriched atmosphere the remaining two containing 12 and 13 trees respec-tively have a normal (control) atmosphere
24 spm
Usage
data(sitka)
Format
This data frame contains the following columns
idnum identification number of tree
order time order ranking within each tree
days time in days since 1st January 1988
logsize tree size measured on a logarithmic scale
ozone indicator ozone treatment 0=control1=ozone
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sitka)attach(sitka)library(lattice)ozonechar lt- rep(controlnrow(sitka))ozonechar[ozone==1] lt- ozonexyplot(logsize~days|ozonechardata=sitkagroups=idnumtype=b)
spm Fit a SemiParametric regression Model
Description
spm is used to fit semiparametric regression models using the mixed model representation of pe-nalized splines (per Ruppert Wand and Carroll 2003)
Usage
spm(formrandom=NULLgroup=NULLfamily=gaussiansparmethod=REMLomitmissing=NULL)
spm 25
Arguments
form a formula describing the model to be fit Note that an intercept is always in-cluded whether given in the formula or not
random random= 1 specifies inclusion of a random intercept according to the groupsspecified by the group argument
group a vector of labels for specifying groups
family for specification of the type of likelihood model assumed in the fitting May begaussianbinomial or poisson
sparmethod method for automatic smoothing parameter selection May be REML (re-stricted maximum likelihood) or ML (maximum likelihood)
omitmissing a logical value indicating whether fields with missing values are to be omitted
Details
See the SemiPar Usersrsquo Manual for details and examples
Value
An list object of classspm containing the fitted model The components are
fit mimics fit object of lme() for family=gaussian and glmmPQL() for family=binomialor family=poisson
info information about the inputs
aux auxiliary information such as variability estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
gam (in package lsquomgcvrsquo)lme (in package lsquonlmersquo)glmmPQL(in package lsquoMASSrsquo)plotspmsummaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)summary(fit)
26 summaryspm
data(califairpoll)attach(califairpoll)fit lt- spm(ozonelevel ~ f(daggettpressuregradient)+
f(inversionbaseheight) +f(inversionbasetemp))
summary(fit)par(mfrow=c(22))plot(fit)
The SemiPar User Manual contains several other examples and details of plotting parameters The current version of the manual is posted on the web-site wwwmathsunsweduau~wandpapershtml
summaryspm Semiparametric regression summary
Description
Takes a fittedspm object produced byspm() and summarises the fit
Usage
summaryspm(object)
Arguments
object a fittedspm object as produced byspm()
other arguments
Details
Produces tables for the linear (parametric) and non-linear (nonparametric) components The lineartable provides coefficient estimates standard errors and p-values The non-linear table providesdegrees of freedom values and other information
Value
The function generates summary tables
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
monitormercury 13
Examples
library(SemiPar)data(milanmort)pairs(milanmortpch=)
monitormercury Mercury biomonintoring data
Description
Themonitormercury data frame has 22 observations from sampling locations around a solidwaste incinerator in Warren County New Jersey USA
Usage
data(monitormercury)
Format
This data frame contains the following columns
UTMNorth longitude of sampling location
UTMEast latitude of sampling location
mercuryconcentration mercury concentration in dry sphagnum moss grown at the sampling lo-cation
Source
Opsomer JD Agras J Carpi A and Rodrigues G (1995) An application of locally weightedregression to airborne mercury deposition around an incinerator siteEnvironmetrics 6 205-221
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(monitormercury)pairs(monitormercury)
14 pigweights
onions Onions data
Description
Theonions data frame contains 84 sets of observations from an experiment involving the produc-tion of white Spanish onions in two South Australian locations
Usage
data(onions)
Format
This data frame contains the following columns
dens areal density of plants (plants per square metre)
yield onion yield (grammes per plant)
location indicator of location 0=Purnong Landing 1=Virginia
Source
Ratkowsky D A (1983)Nonlinear Regression Modeling A Unified Practical ApproachNewYork Marcel Dekker
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(onions)attach(onions)pointscols lt- c(redblue)plot(densyieldcol=pointscols[location+1]pch=16)legend(100250c(Purnong LandingVirginia)col=pointscolspch=rep(162))
pigweights Pig weight data
Description
Thepigweights data frame has 9 repeated weight measures on 48 pigs
Usage
data(pigweights)
plotspm 15
Format
This data frame contains the following columns
idnum identification number of pig
numweeks number of weeks since measurements commenced
weight bodyweight of pig idnum after numweeks weeks
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(pigweights)library(lattice)xyplot(weight~numweeksdata=pigweightsgroups=idnumtype=b)
plotspm Semiparametric regression plotting
Description
Takes a fittedspmobject produced byspm() and plots the component smooth functions that makeit up on the scale of the linear predictor
Usage
plotspm(x)
Arguments
x a fittedspm object as produced byspm()
other graphics parameters described in Appendix B of the SemiPar Usersrsquo Man-ualhttpwwwmathsunsweduau~wandpapershtml
Details
Produces plots with each panel corresponding to a component of the semiparametric regressionmodel
Value
The function generates plots
16 predictspm
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fitylim=range(strontiumratio)col=green
lwd=5shadecol=mediumpurple1rugcol=blue)points(agestrontiumratiocol=orangepch=16)par(op)
predictspm Semiparametric regression prediction
Description
Takes a fittedspm object produced byspm() and obtains predictions at new data values
Usage
predictspm(objectnewdatase)
Arguments
object a fittedspm object as produced byspm()
newdata a data frame containing the values of the predictors at which predictions arerequired The columns should have the same name as the predictors
se when this is TRUE standard error estimates are returned for each predictionThe default is FALSE
other arguments
printspm 17
Details
Takes a fittedspm object produced byspm() and obtains predictions at new data values as speci-fied by the lsquonewdatarsquo argument If lsquose=TRUErsquo then standard error estimates are also obtained
Value
If se=FALSE then a vector of predictions at lsquonewdatarsquo is returned If se=TRUE then a list withcomponents named lsquofitrsquo and lsquosersquo is returned The lsquofitrsquo component contains the predictions The lsquosersquocomponent contains standard error estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm plotspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))newdataage lt- dataframe(age=c(90100110120130))preds lt- predict(fitnewdata=newdataagese=TRUE)print(preds)
plot(fitxlim=c(90130))points(unlist(newdataage)preds$fitcol=red)points(unlist(newdataage)preds$fit+2preds$secol=blue)points(unlist(newdataage)preds$fit-2preds$secol=green)
printspm Prints semiparametric regression fit object
Description
Prints a brief description of a semiparametric regression fit object to the screen
Usage
printspm(x)
18 ragweed
Arguments
x a fittedspm object as produced byspm()
other possible arguments
Details
Prints a brief description of a semiparametric regression fit object to the screen
Value
The function prints to the screen
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))print(fit)
ragweed Ragweed data
Description
Theragweed data frame has data on ragweed levels and meteorological variables for 335 days inKalamazoo Michigan USA
Usage
data(ragweed)
residualsspm 19
Format
This data frame contains the following columns
ragweed ragweed level (grains per cubic metre)
year one of 1991 1992 1993 or 1994
dayinseasday number in the current ragweed pollen season
temperature temperature of following day (degrees Fahrenheit)
rain indicator of significant rain the following day 1=at least 3 hours of steady or brief but intenserain 0=otherwise
windspeed wind speed forecast for following day (knots)
Source
Stark P C Ryan L M McDonald J L and Burge H A (1997) Using meteorologic data tomodel and predict daily ragweed pollen levelsAerobiologia 13 177-184
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ragweed)pairs(ragweedpch=)
residualsspm Residuals for semiparametric regression
Description
Extracts residuals from a semiparametric regression fit object
Usage
residualsspm(object)
Arguments
object a fittedspm object as produced byspm()
other possible arguments
Details
Extracts residuals from a semiparametric regression fit object The residuals are defined to be thedifference between the response variable and the fitted values
20 retireplan
Value
The vector of residuals
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(ageresiduals(fit))abline(00)
retireplan Retirement plan data
Description
The retireplan data frame has data on 401(k) retirement plans for employees of 92 firmsmanaged by a company code-named Best Retirement Inc (BRI)
Usage
data(retireplan)
Format
This data frame contains the following columns
contrib contribution to retirement plan at end of first year
group 1=client has group life of group health insurance policy 0=otherwise
turnover employee turnover rate
eligible number of employees eligible to participate in 401(k) plans
vest 1=plan has immediate vesting of employer contributions 0=otherwise
salinity 21
failsafe 1=plan has a fail-safe provision 0=otherwise
match percentage of contributions matched by the employer
salary average annual employee salary in dollars
estimate underwriterrsquos estimate of end-of-year contributions in dollars
susan 1=plan was sold by a sales representative who has been specifically trained to deal exclu-sively with 401(k) plans (code-named Susan Shepard)
Source
Bryant PG and Smith MA (1995)Practical data analysis case studies in business statisticsChicago Irwin
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(retireplan)pairs(retireplan)
salinity Salinity data
Description
The salinity data frame has 28 observations on hydrological measurements from PamlicoSound North Carolina USA
Usage
data(salinity)
Format
This data frame contains the following columns
salinity salinity in Pamlico Sound
laggedsalinity salinity in Pamlico Sound during the previous six weeks
trend trend=1 if the data is the first six-week period of the spring and so forth Used to detectpossible effects of the seasonal warming trend
discharge discharge of fresh water from rivers into the sound
Source
Ruppert D and Carroll RJ (1980) Trimmed least squares estimation in the linear modelJournalof the American Statistical Association 75 828-838
22 sausage
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(salinity)pairs(salinity)
sausage Sausage data
Description
Thesausage data frame has data on 54 lsquohot dogrsquo sausages
Usage
data(sausage)
Format
This data frame contains the following columns
type type of meat
calories number of calories
sodium measure of sodium content
Source
Moore DS and McCabe GP (2003)Introduction to the Practice of Statistics Fourth EditionWH Freeman and Company
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sausage)attach(sausage)pointscols lt- c(redbluegreen)plot(sodiumcaloriescol=pointscols[type]pch=16)legend(200180c(beefporkpoultry)col=pointscolspch=rep(163))
scallop 23
scallop Scallop abundance data
Description
Thescallop data frame has 148 triplets concerning scallop abundance based on a 1990 surveycruise in the Atlantic continental shelf off Long Island New York USA
Usage
data(scallop)
Format
This data frame contains the following columns
latitude degrees latitude (north of the Equator)
longitude degrees longitude (west of Greenwich)
totcatch size of scallop catch at location specified by latitude and longitude
Source
Ecker MD and Heltshe JF (1994) Geostatistical estimates of scallop abundance InCase Studiesin BiometryLange N Ryan L Billard L Brillinger D Conquest L and Greenhouse J (eds)New York John Wiley amp Sons 107-124
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(scallop)pairs(scallop)
sitka Sitka spruce data
Description
Thesitka data frame contains measurements of log-size for 79 Sitka spruce trees grown in nor-mal or ozone-enriched environments Within each year the data are organised in four blocks cor-responding to four controlled environment chambers The first two chambers containing 27 treeseach have an ozone-enriched atmosphere the remaining two containing 12 and 13 trees respec-tively have a normal (control) atmosphere
24 spm
Usage
data(sitka)
Format
This data frame contains the following columns
idnum identification number of tree
order time order ranking within each tree
days time in days since 1st January 1988
logsize tree size measured on a logarithmic scale
ozone indicator ozone treatment 0=control1=ozone
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sitka)attach(sitka)library(lattice)ozonechar lt- rep(controlnrow(sitka))ozonechar[ozone==1] lt- ozonexyplot(logsize~days|ozonechardata=sitkagroups=idnumtype=b)
spm Fit a SemiParametric regression Model
Description
spm is used to fit semiparametric regression models using the mixed model representation of pe-nalized splines (per Ruppert Wand and Carroll 2003)
Usage
spm(formrandom=NULLgroup=NULLfamily=gaussiansparmethod=REMLomitmissing=NULL)
spm 25
Arguments
form a formula describing the model to be fit Note that an intercept is always in-cluded whether given in the formula or not
random random= 1 specifies inclusion of a random intercept according to the groupsspecified by the group argument
group a vector of labels for specifying groups
family for specification of the type of likelihood model assumed in the fitting May begaussianbinomial or poisson
sparmethod method for automatic smoothing parameter selection May be REML (re-stricted maximum likelihood) or ML (maximum likelihood)
omitmissing a logical value indicating whether fields with missing values are to be omitted
Details
See the SemiPar Usersrsquo Manual for details and examples
Value
An list object of classspm containing the fitted model The components are
fit mimics fit object of lme() for family=gaussian and glmmPQL() for family=binomialor family=poisson
info information about the inputs
aux auxiliary information such as variability estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
gam (in package lsquomgcvrsquo)lme (in package lsquonlmersquo)glmmPQL(in package lsquoMASSrsquo)plotspmsummaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)summary(fit)
26 summaryspm
data(califairpoll)attach(califairpoll)fit lt- spm(ozonelevel ~ f(daggettpressuregradient)+
f(inversionbaseheight) +f(inversionbasetemp))
summary(fit)par(mfrow=c(22))plot(fit)
The SemiPar User Manual contains several other examples and details of plotting parameters The current version of the manual is posted on the web-site wwwmathsunsweduau~wandpapershtml
summaryspm Semiparametric regression summary
Description
Takes a fittedspm object produced byspm() and summarises the fit
Usage
summaryspm(object)
Arguments
object a fittedspm object as produced byspm()
other arguments
Details
Produces tables for the linear (parametric) and non-linear (nonparametric) components The lineartable provides coefficient estimates standard errors and p-values The non-linear table providesdegrees of freedom values and other information
Value
The function generates summary tables
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
14 pigweights
onions Onions data
Description
Theonions data frame contains 84 sets of observations from an experiment involving the produc-tion of white Spanish onions in two South Australian locations
Usage
data(onions)
Format
This data frame contains the following columns
dens areal density of plants (plants per square metre)
yield onion yield (grammes per plant)
location indicator of location 0=Purnong Landing 1=Virginia
Source
Ratkowsky D A (1983)Nonlinear Regression Modeling A Unified Practical ApproachNewYork Marcel Dekker
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(onions)attach(onions)pointscols lt- c(redblue)plot(densyieldcol=pointscols[location+1]pch=16)legend(100250c(Purnong LandingVirginia)col=pointscolspch=rep(162))
pigweights Pig weight data
Description
Thepigweights data frame has 9 repeated weight measures on 48 pigs
Usage
data(pigweights)
plotspm 15
Format
This data frame contains the following columns
idnum identification number of pig
numweeks number of weeks since measurements commenced
weight bodyweight of pig idnum after numweeks weeks
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(pigweights)library(lattice)xyplot(weight~numweeksdata=pigweightsgroups=idnumtype=b)
plotspm Semiparametric regression plotting
Description
Takes a fittedspmobject produced byspm() and plots the component smooth functions that makeit up on the scale of the linear predictor
Usage
plotspm(x)
Arguments
x a fittedspm object as produced byspm()
other graphics parameters described in Appendix B of the SemiPar Usersrsquo Man-ualhttpwwwmathsunsweduau~wandpapershtml
Details
Produces plots with each panel corresponding to a component of the semiparametric regressionmodel
Value
The function generates plots
16 predictspm
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fitylim=range(strontiumratio)col=green
lwd=5shadecol=mediumpurple1rugcol=blue)points(agestrontiumratiocol=orangepch=16)par(op)
predictspm Semiparametric regression prediction
Description
Takes a fittedspm object produced byspm() and obtains predictions at new data values
Usage
predictspm(objectnewdatase)
Arguments
object a fittedspm object as produced byspm()
newdata a data frame containing the values of the predictors at which predictions arerequired The columns should have the same name as the predictors
se when this is TRUE standard error estimates are returned for each predictionThe default is FALSE
other arguments
printspm 17
Details
Takes a fittedspm object produced byspm() and obtains predictions at new data values as speci-fied by the lsquonewdatarsquo argument If lsquose=TRUErsquo then standard error estimates are also obtained
Value
If se=FALSE then a vector of predictions at lsquonewdatarsquo is returned If se=TRUE then a list withcomponents named lsquofitrsquo and lsquosersquo is returned The lsquofitrsquo component contains the predictions The lsquosersquocomponent contains standard error estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm plotspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))newdataage lt- dataframe(age=c(90100110120130))preds lt- predict(fitnewdata=newdataagese=TRUE)print(preds)
plot(fitxlim=c(90130))points(unlist(newdataage)preds$fitcol=red)points(unlist(newdataage)preds$fit+2preds$secol=blue)points(unlist(newdataage)preds$fit-2preds$secol=green)
printspm Prints semiparametric regression fit object
Description
Prints a brief description of a semiparametric regression fit object to the screen
Usage
printspm(x)
18 ragweed
Arguments
x a fittedspm object as produced byspm()
other possible arguments
Details
Prints a brief description of a semiparametric regression fit object to the screen
Value
The function prints to the screen
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))print(fit)
ragweed Ragweed data
Description
Theragweed data frame has data on ragweed levels and meteorological variables for 335 days inKalamazoo Michigan USA
Usage
data(ragweed)
residualsspm 19
Format
This data frame contains the following columns
ragweed ragweed level (grains per cubic metre)
year one of 1991 1992 1993 or 1994
dayinseasday number in the current ragweed pollen season
temperature temperature of following day (degrees Fahrenheit)
rain indicator of significant rain the following day 1=at least 3 hours of steady or brief but intenserain 0=otherwise
windspeed wind speed forecast for following day (knots)
Source
Stark P C Ryan L M McDonald J L and Burge H A (1997) Using meteorologic data tomodel and predict daily ragweed pollen levelsAerobiologia 13 177-184
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ragweed)pairs(ragweedpch=)
residualsspm Residuals for semiparametric regression
Description
Extracts residuals from a semiparametric regression fit object
Usage
residualsspm(object)
Arguments
object a fittedspm object as produced byspm()
other possible arguments
Details
Extracts residuals from a semiparametric regression fit object The residuals are defined to be thedifference between the response variable and the fitted values
20 retireplan
Value
The vector of residuals
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(ageresiduals(fit))abline(00)
retireplan Retirement plan data
Description
The retireplan data frame has data on 401(k) retirement plans for employees of 92 firmsmanaged by a company code-named Best Retirement Inc (BRI)
Usage
data(retireplan)
Format
This data frame contains the following columns
contrib contribution to retirement plan at end of first year
group 1=client has group life of group health insurance policy 0=otherwise
turnover employee turnover rate
eligible number of employees eligible to participate in 401(k) plans
vest 1=plan has immediate vesting of employer contributions 0=otherwise
salinity 21
failsafe 1=plan has a fail-safe provision 0=otherwise
match percentage of contributions matched by the employer
salary average annual employee salary in dollars
estimate underwriterrsquos estimate of end-of-year contributions in dollars
susan 1=plan was sold by a sales representative who has been specifically trained to deal exclu-sively with 401(k) plans (code-named Susan Shepard)
Source
Bryant PG and Smith MA (1995)Practical data analysis case studies in business statisticsChicago Irwin
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(retireplan)pairs(retireplan)
salinity Salinity data
Description
The salinity data frame has 28 observations on hydrological measurements from PamlicoSound North Carolina USA
Usage
data(salinity)
Format
This data frame contains the following columns
salinity salinity in Pamlico Sound
laggedsalinity salinity in Pamlico Sound during the previous six weeks
trend trend=1 if the data is the first six-week period of the spring and so forth Used to detectpossible effects of the seasonal warming trend
discharge discharge of fresh water from rivers into the sound
Source
Ruppert D and Carroll RJ (1980) Trimmed least squares estimation in the linear modelJournalof the American Statistical Association 75 828-838
22 sausage
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(salinity)pairs(salinity)
sausage Sausage data
Description
Thesausage data frame has data on 54 lsquohot dogrsquo sausages
Usage
data(sausage)
Format
This data frame contains the following columns
type type of meat
calories number of calories
sodium measure of sodium content
Source
Moore DS and McCabe GP (2003)Introduction to the Practice of Statistics Fourth EditionWH Freeman and Company
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sausage)attach(sausage)pointscols lt- c(redbluegreen)plot(sodiumcaloriescol=pointscols[type]pch=16)legend(200180c(beefporkpoultry)col=pointscolspch=rep(163))
scallop 23
scallop Scallop abundance data
Description
Thescallop data frame has 148 triplets concerning scallop abundance based on a 1990 surveycruise in the Atlantic continental shelf off Long Island New York USA
Usage
data(scallop)
Format
This data frame contains the following columns
latitude degrees latitude (north of the Equator)
longitude degrees longitude (west of Greenwich)
totcatch size of scallop catch at location specified by latitude and longitude
Source
Ecker MD and Heltshe JF (1994) Geostatistical estimates of scallop abundance InCase Studiesin BiometryLange N Ryan L Billard L Brillinger D Conquest L and Greenhouse J (eds)New York John Wiley amp Sons 107-124
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(scallop)pairs(scallop)
sitka Sitka spruce data
Description
Thesitka data frame contains measurements of log-size for 79 Sitka spruce trees grown in nor-mal or ozone-enriched environments Within each year the data are organised in four blocks cor-responding to four controlled environment chambers The first two chambers containing 27 treeseach have an ozone-enriched atmosphere the remaining two containing 12 and 13 trees respec-tively have a normal (control) atmosphere
24 spm
Usage
data(sitka)
Format
This data frame contains the following columns
idnum identification number of tree
order time order ranking within each tree
days time in days since 1st January 1988
logsize tree size measured on a logarithmic scale
ozone indicator ozone treatment 0=control1=ozone
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sitka)attach(sitka)library(lattice)ozonechar lt- rep(controlnrow(sitka))ozonechar[ozone==1] lt- ozonexyplot(logsize~days|ozonechardata=sitkagroups=idnumtype=b)
spm Fit a SemiParametric regression Model
Description
spm is used to fit semiparametric regression models using the mixed model representation of pe-nalized splines (per Ruppert Wand and Carroll 2003)
Usage
spm(formrandom=NULLgroup=NULLfamily=gaussiansparmethod=REMLomitmissing=NULL)
spm 25
Arguments
form a formula describing the model to be fit Note that an intercept is always in-cluded whether given in the formula or not
random random= 1 specifies inclusion of a random intercept according to the groupsspecified by the group argument
group a vector of labels for specifying groups
family for specification of the type of likelihood model assumed in the fitting May begaussianbinomial or poisson
sparmethod method for automatic smoothing parameter selection May be REML (re-stricted maximum likelihood) or ML (maximum likelihood)
omitmissing a logical value indicating whether fields with missing values are to be omitted
Details
See the SemiPar Usersrsquo Manual for details and examples
Value
An list object of classspm containing the fitted model The components are
fit mimics fit object of lme() for family=gaussian and glmmPQL() for family=binomialor family=poisson
info information about the inputs
aux auxiliary information such as variability estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
gam (in package lsquomgcvrsquo)lme (in package lsquonlmersquo)glmmPQL(in package lsquoMASSrsquo)plotspmsummaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)summary(fit)
26 summaryspm
data(califairpoll)attach(califairpoll)fit lt- spm(ozonelevel ~ f(daggettpressuregradient)+
f(inversionbaseheight) +f(inversionbasetemp))
summary(fit)par(mfrow=c(22))plot(fit)
The SemiPar User Manual contains several other examples and details of plotting parameters The current version of the manual is posted on the web-site wwwmathsunsweduau~wandpapershtml
summaryspm Semiparametric regression summary
Description
Takes a fittedspm object produced byspm() and summarises the fit
Usage
summaryspm(object)
Arguments
object a fittedspm object as produced byspm()
other arguments
Details
Produces tables for the linear (parametric) and non-linear (nonparametric) components The lineartable provides coefficient estimates standard errors and p-values The non-linear table providesdegrees of freedom values and other information
Value
The function generates summary tables
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
plotspm 15
Format
This data frame contains the following columns
idnum identification number of pig
numweeks number of weeks since measurements commenced
weight bodyweight of pig idnum after numweeks weeks
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(pigweights)library(lattice)xyplot(weight~numweeksdata=pigweightsgroups=idnumtype=b)
plotspm Semiparametric regression plotting
Description
Takes a fittedspmobject produced byspm() and plots the component smooth functions that makeit up on the scale of the linear predictor
Usage
plotspm(x)
Arguments
x a fittedspm object as produced byspm()
other graphics parameters described in Appendix B of the SemiPar Usersrsquo Man-ualhttpwwwmathsunsweduau~wandpapershtml
Details
Produces plots with each panel corresponding to a component of the semiparametric regressionmodel
Value
The function generates plots
16 predictspm
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fitylim=range(strontiumratio)col=green
lwd=5shadecol=mediumpurple1rugcol=blue)points(agestrontiumratiocol=orangepch=16)par(op)
predictspm Semiparametric regression prediction
Description
Takes a fittedspm object produced byspm() and obtains predictions at new data values
Usage
predictspm(objectnewdatase)
Arguments
object a fittedspm object as produced byspm()
newdata a data frame containing the values of the predictors at which predictions arerequired The columns should have the same name as the predictors
se when this is TRUE standard error estimates are returned for each predictionThe default is FALSE
other arguments
printspm 17
Details
Takes a fittedspm object produced byspm() and obtains predictions at new data values as speci-fied by the lsquonewdatarsquo argument If lsquose=TRUErsquo then standard error estimates are also obtained
Value
If se=FALSE then a vector of predictions at lsquonewdatarsquo is returned If se=TRUE then a list withcomponents named lsquofitrsquo and lsquosersquo is returned The lsquofitrsquo component contains the predictions The lsquosersquocomponent contains standard error estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm plotspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))newdataage lt- dataframe(age=c(90100110120130))preds lt- predict(fitnewdata=newdataagese=TRUE)print(preds)
plot(fitxlim=c(90130))points(unlist(newdataage)preds$fitcol=red)points(unlist(newdataage)preds$fit+2preds$secol=blue)points(unlist(newdataage)preds$fit-2preds$secol=green)
printspm Prints semiparametric regression fit object
Description
Prints a brief description of a semiparametric regression fit object to the screen
Usage
printspm(x)
18 ragweed
Arguments
x a fittedspm object as produced byspm()
other possible arguments
Details
Prints a brief description of a semiparametric regression fit object to the screen
Value
The function prints to the screen
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))print(fit)
ragweed Ragweed data
Description
Theragweed data frame has data on ragweed levels and meteorological variables for 335 days inKalamazoo Michigan USA
Usage
data(ragweed)
residualsspm 19
Format
This data frame contains the following columns
ragweed ragweed level (grains per cubic metre)
year one of 1991 1992 1993 or 1994
dayinseasday number in the current ragweed pollen season
temperature temperature of following day (degrees Fahrenheit)
rain indicator of significant rain the following day 1=at least 3 hours of steady or brief but intenserain 0=otherwise
windspeed wind speed forecast for following day (knots)
Source
Stark P C Ryan L M McDonald J L and Burge H A (1997) Using meteorologic data tomodel and predict daily ragweed pollen levelsAerobiologia 13 177-184
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ragweed)pairs(ragweedpch=)
residualsspm Residuals for semiparametric regression
Description
Extracts residuals from a semiparametric regression fit object
Usage
residualsspm(object)
Arguments
object a fittedspm object as produced byspm()
other possible arguments
Details
Extracts residuals from a semiparametric regression fit object The residuals are defined to be thedifference between the response variable and the fitted values
20 retireplan
Value
The vector of residuals
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(ageresiduals(fit))abline(00)
retireplan Retirement plan data
Description
The retireplan data frame has data on 401(k) retirement plans for employees of 92 firmsmanaged by a company code-named Best Retirement Inc (BRI)
Usage
data(retireplan)
Format
This data frame contains the following columns
contrib contribution to retirement plan at end of first year
group 1=client has group life of group health insurance policy 0=otherwise
turnover employee turnover rate
eligible number of employees eligible to participate in 401(k) plans
vest 1=plan has immediate vesting of employer contributions 0=otherwise
salinity 21
failsafe 1=plan has a fail-safe provision 0=otherwise
match percentage of contributions matched by the employer
salary average annual employee salary in dollars
estimate underwriterrsquos estimate of end-of-year contributions in dollars
susan 1=plan was sold by a sales representative who has been specifically trained to deal exclu-sively with 401(k) plans (code-named Susan Shepard)
Source
Bryant PG and Smith MA (1995)Practical data analysis case studies in business statisticsChicago Irwin
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(retireplan)pairs(retireplan)
salinity Salinity data
Description
The salinity data frame has 28 observations on hydrological measurements from PamlicoSound North Carolina USA
Usage
data(salinity)
Format
This data frame contains the following columns
salinity salinity in Pamlico Sound
laggedsalinity salinity in Pamlico Sound during the previous six weeks
trend trend=1 if the data is the first six-week period of the spring and so forth Used to detectpossible effects of the seasonal warming trend
discharge discharge of fresh water from rivers into the sound
Source
Ruppert D and Carroll RJ (1980) Trimmed least squares estimation in the linear modelJournalof the American Statistical Association 75 828-838
22 sausage
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(salinity)pairs(salinity)
sausage Sausage data
Description
Thesausage data frame has data on 54 lsquohot dogrsquo sausages
Usage
data(sausage)
Format
This data frame contains the following columns
type type of meat
calories number of calories
sodium measure of sodium content
Source
Moore DS and McCabe GP (2003)Introduction to the Practice of Statistics Fourth EditionWH Freeman and Company
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sausage)attach(sausage)pointscols lt- c(redbluegreen)plot(sodiumcaloriescol=pointscols[type]pch=16)legend(200180c(beefporkpoultry)col=pointscolspch=rep(163))
scallop 23
scallop Scallop abundance data
Description
Thescallop data frame has 148 triplets concerning scallop abundance based on a 1990 surveycruise in the Atlantic continental shelf off Long Island New York USA
Usage
data(scallop)
Format
This data frame contains the following columns
latitude degrees latitude (north of the Equator)
longitude degrees longitude (west of Greenwich)
totcatch size of scallop catch at location specified by latitude and longitude
Source
Ecker MD and Heltshe JF (1994) Geostatistical estimates of scallop abundance InCase Studiesin BiometryLange N Ryan L Billard L Brillinger D Conquest L and Greenhouse J (eds)New York John Wiley amp Sons 107-124
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(scallop)pairs(scallop)
sitka Sitka spruce data
Description
Thesitka data frame contains measurements of log-size for 79 Sitka spruce trees grown in nor-mal or ozone-enriched environments Within each year the data are organised in four blocks cor-responding to four controlled environment chambers The first two chambers containing 27 treeseach have an ozone-enriched atmosphere the remaining two containing 12 and 13 trees respec-tively have a normal (control) atmosphere
24 spm
Usage
data(sitka)
Format
This data frame contains the following columns
idnum identification number of tree
order time order ranking within each tree
days time in days since 1st January 1988
logsize tree size measured on a logarithmic scale
ozone indicator ozone treatment 0=control1=ozone
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sitka)attach(sitka)library(lattice)ozonechar lt- rep(controlnrow(sitka))ozonechar[ozone==1] lt- ozonexyplot(logsize~days|ozonechardata=sitkagroups=idnumtype=b)
spm Fit a SemiParametric regression Model
Description
spm is used to fit semiparametric regression models using the mixed model representation of pe-nalized splines (per Ruppert Wand and Carroll 2003)
Usage
spm(formrandom=NULLgroup=NULLfamily=gaussiansparmethod=REMLomitmissing=NULL)
spm 25
Arguments
form a formula describing the model to be fit Note that an intercept is always in-cluded whether given in the formula or not
random random= 1 specifies inclusion of a random intercept according to the groupsspecified by the group argument
group a vector of labels for specifying groups
family for specification of the type of likelihood model assumed in the fitting May begaussianbinomial or poisson
sparmethod method for automatic smoothing parameter selection May be REML (re-stricted maximum likelihood) or ML (maximum likelihood)
omitmissing a logical value indicating whether fields with missing values are to be omitted
Details
See the SemiPar Usersrsquo Manual for details and examples
Value
An list object of classspm containing the fitted model The components are
fit mimics fit object of lme() for family=gaussian and glmmPQL() for family=binomialor family=poisson
info information about the inputs
aux auxiliary information such as variability estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
gam (in package lsquomgcvrsquo)lme (in package lsquonlmersquo)glmmPQL(in package lsquoMASSrsquo)plotspmsummaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)summary(fit)
26 summaryspm
data(califairpoll)attach(califairpoll)fit lt- spm(ozonelevel ~ f(daggettpressuregradient)+
f(inversionbaseheight) +f(inversionbasetemp))
summary(fit)par(mfrow=c(22))plot(fit)
The SemiPar User Manual contains several other examples and details of plotting parameters The current version of the manual is posted on the web-site wwwmathsunsweduau~wandpapershtml
summaryspm Semiparametric regression summary
Description
Takes a fittedspm object produced byspm() and summarises the fit
Usage
summaryspm(object)
Arguments
object a fittedspm object as produced byspm()
other arguments
Details
Produces tables for the linear (parametric) and non-linear (nonparametric) components The lineartable provides coefficient estimates standard errors and p-values The non-linear table providesdegrees of freedom values and other information
Value
The function generates summary tables
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
16 predictspm
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)
Now do several customisations
op lt- par(bg=white)par(bg=honeydew)plot(fitylim=range(strontiumratio)col=green
lwd=5shadecol=mediumpurple1rugcol=blue)points(agestrontiumratiocol=orangepch=16)par(op)
predictspm Semiparametric regression prediction
Description
Takes a fittedspm object produced byspm() and obtains predictions at new data values
Usage
predictspm(objectnewdatase)
Arguments
object a fittedspm object as produced byspm()
newdata a data frame containing the values of the predictors at which predictions arerequired The columns should have the same name as the predictors
se when this is TRUE standard error estimates are returned for each predictionThe default is FALSE
other arguments
printspm 17
Details
Takes a fittedspm object produced byspm() and obtains predictions at new data values as speci-fied by the lsquonewdatarsquo argument If lsquose=TRUErsquo then standard error estimates are also obtained
Value
If se=FALSE then a vector of predictions at lsquonewdatarsquo is returned If se=TRUE then a list withcomponents named lsquofitrsquo and lsquosersquo is returned The lsquofitrsquo component contains the predictions The lsquosersquocomponent contains standard error estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm plotspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))newdataage lt- dataframe(age=c(90100110120130))preds lt- predict(fitnewdata=newdataagese=TRUE)print(preds)
plot(fitxlim=c(90130))points(unlist(newdataage)preds$fitcol=red)points(unlist(newdataage)preds$fit+2preds$secol=blue)points(unlist(newdataage)preds$fit-2preds$secol=green)
printspm Prints semiparametric regression fit object
Description
Prints a brief description of a semiparametric regression fit object to the screen
Usage
printspm(x)
18 ragweed
Arguments
x a fittedspm object as produced byspm()
other possible arguments
Details
Prints a brief description of a semiparametric regression fit object to the screen
Value
The function prints to the screen
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))print(fit)
ragweed Ragweed data
Description
Theragweed data frame has data on ragweed levels and meteorological variables for 335 days inKalamazoo Michigan USA
Usage
data(ragweed)
residualsspm 19
Format
This data frame contains the following columns
ragweed ragweed level (grains per cubic metre)
year one of 1991 1992 1993 or 1994
dayinseasday number in the current ragweed pollen season
temperature temperature of following day (degrees Fahrenheit)
rain indicator of significant rain the following day 1=at least 3 hours of steady or brief but intenserain 0=otherwise
windspeed wind speed forecast for following day (knots)
Source
Stark P C Ryan L M McDonald J L and Burge H A (1997) Using meteorologic data tomodel and predict daily ragweed pollen levelsAerobiologia 13 177-184
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ragweed)pairs(ragweedpch=)
residualsspm Residuals for semiparametric regression
Description
Extracts residuals from a semiparametric regression fit object
Usage
residualsspm(object)
Arguments
object a fittedspm object as produced byspm()
other possible arguments
Details
Extracts residuals from a semiparametric regression fit object The residuals are defined to be thedifference between the response variable and the fitted values
20 retireplan
Value
The vector of residuals
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(ageresiduals(fit))abline(00)
retireplan Retirement plan data
Description
The retireplan data frame has data on 401(k) retirement plans for employees of 92 firmsmanaged by a company code-named Best Retirement Inc (BRI)
Usage
data(retireplan)
Format
This data frame contains the following columns
contrib contribution to retirement plan at end of first year
group 1=client has group life of group health insurance policy 0=otherwise
turnover employee turnover rate
eligible number of employees eligible to participate in 401(k) plans
vest 1=plan has immediate vesting of employer contributions 0=otherwise
salinity 21
failsafe 1=plan has a fail-safe provision 0=otherwise
match percentage of contributions matched by the employer
salary average annual employee salary in dollars
estimate underwriterrsquos estimate of end-of-year contributions in dollars
susan 1=plan was sold by a sales representative who has been specifically trained to deal exclu-sively with 401(k) plans (code-named Susan Shepard)
Source
Bryant PG and Smith MA (1995)Practical data analysis case studies in business statisticsChicago Irwin
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(retireplan)pairs(retireplan)
salinity Salinity data
Description
The salinity data frame has 28 observations on hydrological measurements from PamlicoSound North Carolina USA
Usage
data(salinity)
Format
This data frame contains the following columns
salinity salinity in Pamlico Sound
laggedsalinity salinity in Pamlico Sound during the previous six weeks
trend trend=1 if the data is the first six-week period of the spring and so forth Used to detectpossible effects of the seasonal warming trend
discharge discharge of fresh water from rivers into the sound
Source
Ruppert D and Carroll RJ (1980) Trimmed least squares estimation in the linear modelJournalof the American Statistical Association 75 828-838
22 sausage
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(salinity)pairs(salinity)
sausage Sausage data
Description
Thesausage data frame has data on 54 lsquohot dogrsquo sausages
Usage
data(sausage)
Format
This data frame contains the following columns
type type of meat
calories number of calories
sodium measure of sodium content
Source
Moore DS and McCabe GP (2003)Introduction to the Practice of Statistics Fourth EditionWH Freeman and Company
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sausage)attach(sausage)pointscols lt- c(redbluegreen)plot(sodiumcaloriescol=pointscols[type]pch=16)legend(200180c(beefporkpoultry)col=pointscolspch=rep(163))
scallop 23
scallop Scallop abundance data
Description
Thescallop data frame has 148 triplets concerning scallop abundance based on a 1990 surveycruise in the Atlantic continental shelf off Long Island New York USA
Usage
data(scallop)
Format
This data frame contains the following columns
latitude degrees latitude (north of the Equator)
longitude degrees longitude (west of Greenwich)
totcatch size of scallop catch at location specified by latitude and longitude
Source
Ecker MD and Heltshe JF (1994) Geostatistical estimates of scallop abundance InCase Studiesin BiometryLange N Ryan L Billard L Brillinger D Conquest L and Greenhouse J (eds)New York John Wiley amp Sons 107-124
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(scallop)pairs(scallop)
sitka Sitka spruce data
Description
Thesitka data frame contains measurements of log-size for 79 Sitka spruce trees grown in nor-mal or ozone-enriched environments Within each year the data are organised in four blocks cor-responding to four controlled environment chambers The first two chambers containing 27 treeseach have an ozone-enriched atmosphere the remaining two containing 12 and 13 trees respec-tively have a normal (control) atmosphere
24 spm
Usage
data(sitka)
Format
This data frame contains the following columns
idnum identification number of tree
order time order ranking within each tree
days time in days since 1st January 1988
logsize tree size measured on a logarithmic scale
ozone indicator ozone treatment 0=control1=ozone
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sitka)attach(sitka)library(lattice)ozonechar lt- rep(controlnrow(sitka))ozonechar[ozone==1] lt- ozonexyplot(logsize~days|ozonechardata=sitkagroups=idnumtype=b)
spm Fit a SemiParametric regression Model
Description
spm is used to fit semiparametric regression models using the mixed model representation of pe-nalized splines (per Ruppert Wand and Carroll 2003)
Usage
spm(formrandom=NULLgroup=NULLfamily=gaussiansparmethod=REMLomitmissing=NULL)
spm 25
Arguments
form a formula describing the model to be fit Note that an intercept is always in-cluded whether given in the formula or not
random random= 1 specifies inclusion of a random intercept according to the groupsspecified by the group argument
group a vector of labels for specifying groups
family for specification of the type of likelihood model assumed in the fitting May begaussianbinomial or poisson
sparmethod method for automatic smoothing parameter selection May be REML (re-stricted maximum likelihood) or ML (maximum likelihood)
omitmissing a logical value indicating whether fields with missing values are to be omitted
Details
See the SemiPar Usersrsquo Manual for details and examples
Value
An list object of classspm containing the fitted model The components are
fit mimics fit object of lme() for family=gaussian and glmmPQL() for family=binomialor family=poisson
info information about the inputs
aux auxiliary information such as variability estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
gam (in package lsquomgcvrsquo)lme (in package lsquonlmersquo)glmmPQL(in package lsquoMASSrsquo)plotspmsummaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)summary(fit)
26 summaryspm
data(califairpoll)attach(califairpoll)fit lt- spm(ozonelevel ~ f(daggettpressuregradient)+
f(inversionbaseheight) +f(inversionbasetemp))
summary(fit)par(mfrow=c(22))plot(fit)
The SemiPar User Manual contains several other examples and details of plotting parameters The current version of the manual is posted on the web-site wwwmathsunsweduau~wandpapershtml
summaryspm Semiparametric regression summary
Description
Takes a fittedspm object produced byspm() and summarises the fit
Usage
summaryspm(object)
Arguments
object a fittedspm object as produced byspm()
other arguments
Details
Produces tables for the linear (parametric) and non-linear (nonparametric) components The lineartable provides coefficient estimates standard errors and p-values The non-linear table providesdegrees of freedom values and other information
Value
The function generates summary tables
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
printspm 17
Details
Takes a fittedspm object produced byspm() and obtains predictions at new data values as speci-fied by the lsquonewdatarsquo argument If lsquose=TRUErsquo then standard error estimates are also obtained
Value
If se=FALSE then a vector of predictions at lsquonewdatarsquo is returned If se=TRUE then a list withcomponents named lsquofitrsquo and lsquosersquo is returned The lsquofitrsquo component contains the predictions The lsquosersquocomponent contains standard error estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm linesspm plotspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))newdataage lt- dataframe(age=c(90100110120130))preds lt- predict(fitnewdata=newdataagese=TRUE)print(preds)
plot(fitxlim=c(90130))points(unlist(newdataage)preds$fitcol=red)points(unlist(newdataage)preds$fit+2preds$secol=blue)points(unlist(newdataage)preds$fit-2preds$secol=green)
printspm Prints semiparametric regression fit object
Description
Prints a brief description of a semiparametric regression fit object to the screen
Usage
printspm(x)
18 ragweed
Arguments
x a fittedspm object as produced byspm()
other possible arguments
Details
Prints a brief description of a semiparametric regression fit object to the screen
Value
The function prints to the screen
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))print(fit)
ragweed Ragweed data
Description
Theragweed data frame has data on ragweed levels and meteorological variables for 335 days inKalamazoo Michigan USA
Usage
data(ragweed)
residualsspm 19
Format
This data frame contains the following columns
ragweed ragweed level (grains per cubic metre)
year one of 1991 1992 1993 or 1994
dayinseasday number in the current ragweed pollen season
temperature temperature of following day (degrees Fahrenheit)
rain indicator of significant rain the following day 1=at least 3 hours of steady or brief but intenserain 0=otherwise
windspeed wind speed forecast for following day (knots)
Source
Stark P C Ryan L M McDonald J L and Burge H A (1997) Using meteorologic data tomodel and predict daily ragweed pollen levelsAerobiologia 13 177-184
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ragweed)pairs(ragweedpch=)
residualsspm Residuals for semiparametric regression
Description
Extracts residuals from a semiparametric regression fit object
Usage
residualsspm(object)
Arguments
object a fittedspm object as produced byspm()
other possible arguments
Details
Extracts residuals from a semiparametric regression fit object The residuals are defined to be thedifference between the response variable and the fitted values
20 retireplan
Value
The vector of residuals
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(ageresiduals(fit))abline(00)
retireplan Retirement plan data
Description
The retireplan data frame has data on 401(k) retirement plans for employees of 92 firmsmanaged by a company code-named Best Retirement Inc (BRI)
Usage
data(retireplan)
Format
This data frame contains the following columns
contrib contribution to retirement plan at end of first year
group 1=client has group life of group health insurance policy 0=otherwise
turnover employee turnover rate
eligible number of employees eligible to participate in 401(k) plans
vest 1=plan has immediate vesting of employer contributions 0=otherwise
salinity 21
failsafe 1=plan has a fail-safe provision 0=otherwise
match percentage of contributions matched by the employer
salary average annual employee salary in dollars
estimate underwriterrsquos estimate of end-of-year contributions in dollars
susan 1=plan was sold by a sales representative who has been specifically trained to deal exclu-sively with 401(k) plans (code-named Susan Shepard)
Source
Bryant PG and Smith MA (1995)Practical data analysis case studies in business statisticsChicago Irwin
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(retireplan)pairs(retireplan)
salinity Salinity data
Description
The salinity data frame has 28 observations on hydrological measurements from PamlicoSound North Carolina USA
Usage
data(salinity)
Format
This data frame contains the following columns
salinity salinity in Pamlico Sound
laggedsalinity salinity in Pamlico Sound during the previous six weeks
trend trend=1 if the data is the first six-week period of the spring and so forth Used to detectpossible effects of the seasonal warming trend
discharge discharge of fresh water from rivers into the sound
Source
Ruppert D and Carroll RJ (1980) Trimmed least squares estimation in the linear modelJournalof the American Statistical Association 75 828-838
22 sausage
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(salinity)pairs(salinity)
sausage Sausage data
Description
Thesausage data frame has data on 54 lsquohot dogrsquo sausages
Usage
data(sausage)
Format
This data frame contains the following columns
type type of meat
calories number of calories
sodium measure of sodium content
Source
Moore DS and McCabe GP (2003)Introduction to the Practice of Statistics Fourth EditionWH Freeman and Company
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sausage)attach(sausage)pointscols lt- c(redbluegreen)plot(sodiumcaloriescol=pointscols[type]pch=16)legend(200180c(beefporkpoultry)col=pointscolspch=rep(163))
scallop 23
scallop Scallop abundance data
Description
Thescallop data frame has 148 triplets concerning scallop abundance based on a 1990 surveycruise in the Atlantic continental shelf off Long Island New York USA
Usage
data(scallop)
Format
This data frame contains the following columns
latitude degrees latitude (north of the Equator)
longitude degrees longitude (west of Greenwich)
totcatch size of scallop catch at location specified by latitude and longitude
Source
Ecker MD and Heltshe JF (1994) Geostatistical estimates of scallop abundance InCase Studiesin BiometryLange N Ryan L Billard L Brillinger D Conquest L and Greenhouse J (eds)New York John Wiley amp Sons 107-124
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(scallop)pairs(scallop)
sitka Sitka spruce data
Description
Thesitka data frame contains measurements of log-size for 79 Sitka spruce trees grown in nor-mal or ozone-enriched environments Within each year the data are organised in four blocks cor-responding to four controlled environment chambers The first two chambers containing 27 treeseach have an ozone-enriched atmosphere the remaining two containing 12 and 13 trees respec-tively have a normal (control) atmosphere
24 spm
Usage
data(sitka)
Format
This data frame contains the following columns
idnum identification number of tree
order time order ranking within each tree
days time in days since 1st January 1988
logsize tree size measured on a logarithmic scale
ozone indicator ozone treatment 0=control1=ozone
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sitka)attach(sitka)library(lattice)ozonechar lt- rep(controlnrow(sitka))ozonechar[ozone==1] lt- ozonexyplot(logsize~days|ozonechardata=sitkagroups=idnumtype=b)
spm Fit a SemiParametric regression Model
Description
spm is used to fit semiparametric regression models using the mixed model representation of pe-nalized splines (per Ruppert Wand and Carroll 2003)
Usage
spm(formrandom=NULLgroup=NULLfamily=gaussiansparmethod=REMLomitmissing=NULL)
spm 25
Arguments
form a formula describing the model to be fit Note that an intercept is always in-cluded whether given in the formula or not
random random= 1 specifies inclusion of a random intercept according to the groupsspecified by the group argument
group a vector of labels for specifying groups
family for specification of the type of likelihood model assumed in the fitting May begaussianbinomial or poisson
sparmethod method for automatic smoothing parameter selection May be REML (re-stricted maximum likelihood) or ML (maximum likelihood)
omitmissing a logical value indicating whether fields with missing values are to be omitted
Details
See the SemiPar Usersrsquo Manual for details and examples
Value
An list object of classspm containing the fitted model The components are
fit mimics fit object of lme() for family=gaussian and glmmPQL() for family=binomialor family=poisson
info information about the inputs
aux auxiliary information such as variability estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
gam (in package lsquomgcvrsquo)lme (in package lsquonlmersquo)glmmPQL(in package lsquoMASSrsquo)plotspmsummaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)summary(fit)
26 summaryspm
data(califairpoll)attach(califairpoll)fit lt- spm(ozonelevel ~ f(daggettpressuregradient)+
f(inversionbaseheight) +f(inversionbasetemp))
summary(fit)par(mfrow=c(22))plot(fit)
The SemiPar User Manual contains several other examples and details of plotting parameters The current version of the manual is posted on the web-site wwwmathsunsweduau~wandpapershtml
summaryspm Semiparametric regression summary
Description
Takes a fittedspm object produced byspm() and summarises the fit
Usage
summaryspm(object)
Arguments
object a fittedspm object as produced byspm()
other arguments
Details
Produces tables for the linear (parametric) and non-linear (nonparametric) components The lineartable provides coefficient estimates standard errors and p-values The non-linear table providesdegrees of freedom values and other information
Value
The function generates summary tables
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
18 ragweed
Arguments
x a fittedspm object as produced byspm()
other possible arguments
Details
Prints a brief description of a semiparametric regression fit object to the screen
Value
The function prints to the screen
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))print(fit)
ragweed Ragweed data
Description
Theragweed data frame has data on ragweed levels and meteorological variables for 335 days inKalamazoo Michigan USA
Usage
data(ragweed)
residualsspm 19
Format
This data frame contains the following columns
ragweed ragweed level (grains per cubic metre)
year one of 1991 1992 1993 or 1994
dayinseasday number in the current ragweed pollen season
temperature temperature of following day (degrees Fahrenheit)
rain indicator of significant rain the following day 1=at least 3 hours of steady or brief but intenserain 0=otherwise
windspeed wind speed forecast for following day (knots)
Source
Stark P C Ryan L M McDonald J L and Burge H A (1997) Using meteorologic data tomodel and predict daily ragweed pollen levelsAerobiologia 13 177-184
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ragweed)pairs(ragweedpch=)
residualsspm Residuals for semiparametric regression
Description
Extracts residuals from a semiparametric regression fit object
Usage
residualsspm(object)
Arguments
object a fittedspm object as produced byspm()
other possible arguments
Details
Extracts residuals from a semiparametric regression fit object The residuals are defined to be thedifference between the response variable and the fitted values
20 retireplan
Value
The vector of residuals
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(ageresiduals(fit))abline(00)
retireplan Retirement plan data
Description
The retireplan data frame has data on 401(k) retirement plans for employees of 92 firmsmanaged by a company code-named Best Retirement Inc (BRI)
Usage
data(retireplan)
Format
This data frame contains the following columns
contrib contribution to retirement plan at end of first year
group 1=client has group life of group health insurance policy 0=otherwise
turnover employee turnover rate
eligible number of employees eligible to participate in 401(k) plans
vest 1=plan has immediate vesting of employer contributions 0=otherwise
salinity 21
failsafe 1=plan has a fail-safe provision 0=otherwise
match percentage of contributions matched by the employer
salary average annual employee salary in dollars
estimate underwriterrsquos estimate of end-of-year contributions in dollars
susan 1=plan was sold by a sales representative who has been specifically trained to deal exclu-sively with 401(k) plans (code-named Susan Shepard)
Source
Bryant PG and Smith MA (1995)Practical data analysis case studies in business statisticsChicago Irwin
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(retireplan)pairs(retireplan)
salinity Salinity data
Description
The salinity data frame has 28 observations on hydrological measurements from PamlicoSound North Carolina USA
Usage
data(salinity)
Format
This data frame contains the following columns
salinity salinity in Pamlico Sound
laggedsalinity salinity in Pamlico Sound during the previous six weeks
trend trend=1 if the data is the first six-week period of the spring and so forth Used to detectpossible effects of the seasonal warming trend
discharge discharge of fresh water from rivers into the sound
Source
Ruppert D and Carroll RJ (1980) Trimmed least squares estimation in the linear modelJournalof the American Statistical Association 75 828-838
22 sausage
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(salinity)pairs(salinity)
sausage Sausage data
Description
Thesausage data frame has data on 54 lsquohot dogrsquo sausages
Usage
data(sausage)
Format
This data frame contains the following columns
type type of meat
calories number of calories
sodium measure of sodium content
Source
Moore DS and McCabe GP (2003)Introduction to the Practice of Statistics Fourth EditionWH Freeman and Company
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sausage)attach(sausage)pointscols lt- c(redbluegreen)plot(sodiumcaloriescol=pointscols[type]pch=16)legend(200180c(beefporkpoultry)col=pointscolspch=rep(163))
scallop 23
scallop Scallop abundance data
Description
Thescallop data frame has 148 triplets concerning scallop abundance based on a 1990 surveycruise in the Atlantic continental shelf off Long Island New York USA
Usage
data(scallop)
Format
This data frame contains the following columns
latitude degrees latitude (north of the Equator)
longitude degrees longitude (west of Greenwich)
totcatch size of scallop catch at location specified by latitude and longitude
Source
Ecker MD and Heltshe JF (1994) Geostatistical estimates of scallop abundance InCase Studiesin BiometryLange N Ryan L Billard L Brillinger D Conquest L and Greenhouse J (eds)New York John Wiley amp Sons 107-124
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(scallop)pairs(scallop)
sitka Sitka spruce data
Description
Thesitka data frame contains measurements of log-size for 79 Sitka spruce trees grown in nor-mal or ozone-enriched environments Within each year the data are organised in four blocks cor-responding to four controlled environment chambers The first two chambers containing 27 treeseach have an ozone-enriched atmosphere the remaining two containing 12 and 13 trees respec-tively have a normal (control) atmosphere
24 spm
Usage
data(sitka)
Format
This data frame contains the following columns
idnum identification number of tree
order time order ranking within each tree
days time in days since 1st January 1988
logsize tree size measured on a logarithmic scale
ozone indicator ozone treatment 0=control1=ozone
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sitka)attach(sitka)library(lattice)ozonechar lt- rep(controlnrow(sitka))ozonechar[ozone==1] lt- ozonexyplot(logsize~days|ozonechardata=sitkagroups=idnumtype=b)
spm Fit a SemiParametric regression Model
Description
spm is used to fit semiparametric regression models using the mixed model representation of pe-nalized splines (per Ruppert Wand and Carroll 2003)
Usage
spm(formrandom=NULLgroup=NULLfamily=gaussiansparmethod=REMLomitmissing=NULL)
spm 25
Arguments
form a formula describing the model to be fit Note that an intercept is always in-cluded whether given in the formula or not
random random= 1 specifies inclusion of a random intercept according to the groupsspecified by the group argument
group a vector of labels for specifying groups
family for specification of the type of likelihood model assumed in the fitting May begaussianbinomial or poisson
sparmethod method for automatic smoothing parameter selection May be REML (re-stricted maximum likelihood) or ML (maximum likelihood)
omitmissing a logical value indicating whether fields with missing values are to be omitted
Details
See the SemiPar Usersrsquo Manual for details and examples
Value
An list object of classspm containing the fitted model The components are
fit mimics fit object of lme() for family=gaussian and glmmPQL() for family=binomialor family=poisson
info information about the inputs
aux auxiliary information such as variability estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
gam (in package lsquomgcvrsquo)lme (in package lsquonlmersquo)glmmPQL(in package lsquoMASSrsquo)plotspmsummaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)summary(fit)
26 summaryspm
data(califairpoll)attach(califairpoll)fit lt- spm(ozonelevel ~ f(daggettpressuregradient)+
f(inversionbaseheight) +f(inversionbasetemp))
summary(fit)par(mfrow=c(22))plot(fit)
The SemiPar User Manual contains several other examples and details of plotting parameters The current version of the manual is posted on the web-site wwwmathsunsweduau~wandpapershtml
summaryspm Semiparametric regression summary
Description
Takes a fittedspm object produced byspm() and summarises the fit
Usage
summaryspm(object)
Arguments
object a fittedspm object as produced byspm()
other arguments
Details
Produces tables for the linear (parametric) and non-linear (nonparametric) components The lineartable provides coefficient estimates standard errors and p-values The non-linear table providesdegrees of freedom values and other information
Value
The function generates summary tables
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
residualsspm 19
Format
This data frame contains the following columns
ragweed ragweed level (grains per cubic metre)
year one of 1991 1992 1993 or 1994
dayinseasday number in the current ragweed pollen season
temperature temperature of following day (degrees Fahrenheit)
rain indicator of significant rain the following day 1=at least 3 hours of steady or brief but intenserain 0=otherwise
windspeed wind speed forecast for following day (knots)
Source
Stark P C Ryan L M McDonald J L and Burge H A (1997) Using meteorologic data tomodel and predict daily ragweed pollen levelsAerobiologia 13 177-184
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ragweed)pairs(ragweedpch=)
residualsspm Residuals for semiparametric regression
Description
Extracts residuals from a semiparametric regression fit object
Usage
residualsspm(object)
Arguments
object a fittedspm object as produced byspm()
other possible arguments
Details
Extracts residuals from a semiparametric regression fit object The residuals are defined to be thedifference between the response variable and the fitted values
20 retireplan
Value
The vector of residuals
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(ageresiduals(fit))abline(00)
retireplan Retirement plan data
Description
The retireplan data frame has data on 401(k) retirement plans for employees of 92 firmsmanaged by a company code-named Best Retirement Inc (BRI)
Usage
data(retireplan)
Format
This data frame contains the following columns
contrib contribution to retirement plan at end of first year
group 1=client has group life of group health insurance policy 0=otherwise
turnover employee turnover rate
eligible number of employees eligible to participate in 401(k) plans
vest 1=plan has immediate vesting of employer contributions 0=otherwise
salinity 21
failsafe 1=plan has a fail-safe provision 0=otherwise
match percentage of contributions matched by the employer
salary average annual employee salary in dollars
estimate underwriterrsquos estimate of end-of-year contributions in dollars
susan 1=plan was sold by a sales representative who has been specifically trained to deal exclu-sively with 401(k) plans (code-named Susan Shepard)
Source
Bryant PG and Smith MA (1995)Practical data analysis case studies in business statisticsChicago Irwin
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(retireplan)pairs(retireplan)
salinity Salinity data
Description
The salinity data frame has 28 observations on hydrological measurements from PamlicoSound North Carolina USA
Usage
data(salinity)
Format
This data frame contains the following columns
salinity salinity in Pamlico Sound
laggedsalinity salinity in Pamlico Sound during the previous six weeks
trend trend=1 if the data is the first six-week period of the spring and so forth Used to detectpossible effects of the seasonal warming trend
discharge discharge of fresh water from rivers into the sound
Source
Ruppert D and Carroll RJ (1980) Trimmed least squares estimation in the linear modelJournalof the American Statistical Association 75 828-838
22 sausage
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(salinity)pairs(salinity)
sausage Sausage data
Description
Thesausage data frame has data on 54 lsquohot dogrsquo sausages
Usage
data(sausage)
Format
This data frame contains the following columns
type type of meat
calories number of calories
sodium measure of sodium content
Source
Moore DS and McCabe GP (2003)Introduction to the Practice of Statistics Fourth EditionWH Freeman and Company
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sausage)attach(sausage)pointscols lt- c(redbluegreen)plot(sodiumcaloriescol=pointscols[type]pch=16)legend(200180c(beefporkpoultry)col=pointscolspch=rep(163))
scallop 23
scallop Scallop abundance data
Description
Thescallop data frame has 148 triplets concerning scallop abundance based on a 1990 surveycruise in the Atlantic continental shelf off Long Island New York USA
Usage
data(scallop)
Format
This data frame contains the following columns
latitude degrees latitude (north of the Equator)
longitude degrees longitude (west of Greenwich)
totcatch size of scallop catch at location specified by latitude and longitude
Source
Ecker MD and Heltshe JF (1994) Geostatistical estimates of scallop abundance InCase Studiesin BiometryLange N Ryan L Billard L Brillinger D Conquest L and Greenhouse J (eds)New York John Wiley amp Sons 107-124
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(scallop)pairs(scallop)
sitka Sitka spruce data
Description
Thesitka data frame contains measurements of log-size for 79 Sitka spruce trees grown in nor-mal or ozone-enriched environments Within each year the data are organised in four blocks cor-responding to four controlled environment chambers The first two chambers containing 27 treeseach have an ozone-enriched atmosphere the remaining two containing 12 and 13 trees respec-tively have a normal (control) atmosphere
24 spm
Usage
data(sitka)
Format
This data frame contains the following columns
idnum identification number of tree
order time order ranking within each tree
days time in days since 1st January 1988
logsize tree size measured on a logarithmic scale
ozone indicator ozone treatment 0=control1=ozone
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sitka)attach(sitka)library(lattice)ozonechar lt- rep(controlnrow(sitka))ozonechar[ozone==1] lt- ozonexyplot(logsize~days|ozonechardata=sitkagroups=idnumtype=b)
spm Fit a SemiParametric regression Model
Description
spm is used to fit semiparametric regression models using the mixed model representation of pe-nalized splines (per Ruppert Wand and Carroll 2003)
Usage
spm(formrandom=NULLgroup=NULLfamily=gaussiansparmethod=REMLomitmissing=NULL)
spm 25
Arguments
form a formula describing the model to be fit Note that an intercept is always in-cluded whether given in the formula or not
random random= 1 specifies inclusion of a random intercept according to the groupsspecified by the group argument
group a vector of labels for specifying groups
family for specification of the type of likelihood model assumed in the fitting May begaussianbinomial or poisson
sparmethod method for automatic smoothing parameter selection May be REML (re-stricted maximum likelihood) or ML (maximum likelihood)
omitmissing a logical value indicating whether fields with missing values are to be omitted
Details
See the SemiPar Usersrsquo Manual for details and examples
Value
An list object of classspm containing the fitted model The components are
fit mimics fit object of lme() for family=gaussian and glmmPQL() for family=binomialor family=poisson
info information about the inputs
aux auxiliary information such as variability estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
gam (in package lsquomgcvrsquo)lme (in package lsquonlmersquo)glmmPQL(in package lsquoMASSrsquo)plotspmsummaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)summary(fit)
26 summaryspm
data(califairpoll)attach(califairpoll)fit lt- spm(ozonelevel ~ f(daggettpressuregradient)+
f(inversionbaseheight) +f(inversionbasetemp))
summary(fit)par(mfrow=c(22))plot(fit)
The SemiPar User Manual contains several other examples and details of plotting parameters The current version of the manual is posted on the web-site wwwmathsunsweduau~wandpapershtml
summaryspm Semiparametric regression summary
Description
Takes a fittedspm object produced byspm() and summarises the fit
Usage
summaryspm(object)
Arguments
object a fittedspm object as produced byspm()
other arguments
Details
Produces tables for the linear (parametric) and non-linear (nonparametric) components The lineartable provides coefficient estimates standard errors and p-values The non-linear table providesdegrees of freedom values and other information
Value
The function generates summary tables
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
20 retireplan
Value
The vector of residuals
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
spm plotspm linesspm predictspm summaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(ageresiduals(fit))abline(00)
retireplan Retirement plan data
Description
The retireplan data frame has data on 401(k) retirement plans for employees of 92 firmsmanaged by a company code-named Best Retirement Inc (BRI)
Usage
data(retireplan)
Format
This data frame contains the following columns
contrib contribution to retirement plan at end of first year
group 1=client has group life of group health insurance policy 0=otherwise
turnover employee turnover rate
eligible number of employees eligible to participate in 401(k) plans
vest 1=plan has immediate vesting of employer contributions 0=otherwise
salinity 21
failsafe 1=plan has a fail-safe provision 0=otherwise
match percentage of contributions matched by the employer
salary average annual employee salary in dollars
estimate underwriterrsquos estimate of end-of-year contributions in dollars
susan 1=plan was sold by a sales representative who has been specifically trained to deal exclu-sively with 401(k) plans (code-named Susan Shepard)
Source
Bryant PG and Smith MA (1995)Practical data analysis case studies in business statisticsChicago Irwin
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(retireplan)pairs(retireplan)
salinity Salinity data
Description
The salinity data frame has 28 observations on hydrological measurements from PamlicoSound North Carolina USA
Usage
data(salinity)
Format
This data frame contains the following columns
salinity salinity in Pamlico Sound
laggedsalinity salinity in Pamlico Sound during the previous six weeks
trend trend=1 if the data is the first six-week period of the spring and so forth Used to detectpossible effects of the seasonal warming trend
discharge discharge of fresh water from rivers into the sound
Source
Ruppert D and Carroll RJ (1980) Trimmed least squares estimation in the linear modelJournalof the American Statistical Association 75 828-838
22 sausage
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(salinity)pairs(salinity)
sausage Sausage data
Description
Thesausage data frame has data on 54 lsquohot dogrsquo sausages
Usage
data(sausage)
Format
This data frame contains the following columns
type type of meat
calories number of calories
sodium measure of sodium content
Source
Moore DS and McCabe GP (2003)Introduction to the Practice of Statistics Fourth EditionWH Freeman and Company
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sausage)attach(sausage)pointscols lt- c(redbluegreen)plot(sodiumcaloriescol=pointscols[type]pch=16)legend(200180c(beefporkpoultry)col=pointscolspch=rep(163))
scallop 23
scallop Scallop abundance data
Description
Thescallop data frame has 148 triplets concerning scallop abundance based on a 1990 surveycruise in the Atlantic continental shelf off Long Island New York USA
Usage
data(scallop)
Format
This data frame contains the following columns
latitude degrees latitude (north of the Equator)
longitude degrees longitude (west of Greenwich)
totcatch size of scallop catch at location specified by latitude and longitude
Source
Ecker MD and Heltshe JF (1994) Geostatistical estimates of scallop abundance InCase Studiesin BiometryLange N Ryan L Billard L Brillinger D Conquest L and Greenhouse J (eds)New York John Wiley amp Sons 107-124
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(scallop)pairs(scallop)
sitka Sitka spruce data
Description
Thesitka data frame contains measurements of log-size for 79 Sitka spruce trees grown in nor-mal or ozone-enriched environments Within each year the data are organised in four blocks cor-responding to four controlled environment chambers The first two chambers containing 27 treeseach have an ozone-enriched atmosphere the remaining two containing 12 and 13 trees respec-tively have a normal (control) atmosphere
24 spm
Usage
data(sitka)
Format
This data frame contains the following columns
idnum identification number of tree
order time order ranking within each tree
days time in days since 1st January 1988
logsize tree size measured on a logarithmic scale
ozone indicator ozone treatment 0=control1=ozone
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sitka)attach(sitka)library(lattice)ozonechar lt- rep(controlnrow(sitka))ozonechar[ozone==1] lt- ozonexyplot(logsize~days|ozonechardata=sitkagroups=idnumtype=b)
spm Fit a SemiParametric regression Model
Description
spm is used to fit semiparametric regression models using the mixed model representation of pe-nalized splines (per Ruppert Wand and Carroll 2003)
Usage
spm(formrandom=NULLgroup=NULLfamily=gaussiansparmethod=REMLomitmissing=NULL)
spm 25
Arguments
form a formula describing the model to be fit Note that an intercept is always in-cluded whether given in the formula or not
random random= 1 specifies inclusion of a random intercept according to the groupsspecified by the group argument
group a vector of labels for specifying groups
family for specification of the type of likelihood model assumed in the fitting May begaussianbinomial or poisson
sparmethod method for automatic smoothing parameter selection May be REML (re-stricted maximum likelihood) or ML (maximum likelihood)
omitmissing a logical value indicating whether fields with missing values are to be omitted
Details
See the SemiPar Usersrsquo Manual for details and examples
Value
An list object of classspm containing the fitted model The components are
fit mimics fit object of lme() for family=gaussian and glmmPQL() for family=binomialor family=poisson
info information about the inputs
aux auxiliary information such as variability estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
gam (in package lsquomgcvrsquo)lme (in package lsquonlmersquo)glmmPQL(in package lsquoMASSrsquo)plotspmsummaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)summary(fit)
26 summaryspm
data(califairpoll)attach(califairpoll)fit lt- spm(ozonelevel ~ f(daggettpressuregradient)+
f(inversionbaseheight) +f(inversionbasetemp))
summary(fit)par(mfrow=c(22))plot(fit)
The SemiPar User Manual contains several other examples and details of plotting parameters The current version of the manual is posted on the web-site wwwmathsunsweduau~wandpapershtml
summaryspm Semiparametric regression summary
Description
Takes a fittedspm object produced byspm() and summarises the fit
Usage
summaryspm(object)
Arguments
object a fittedspm object as produced byspm()
other arguments
Details
Produces tables for the linear (parametric) and non-linear (nonparametric) components The lineartable provides coefficient estimates standard errors and p-values The non-linear table providesdegrees of freedom values and other information
Value
The function generates summary tables
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
salinity 21
failsafe 1=plan has a fail-safe provision 0=otherwise
match percentage of contributions matched by the employer
salary average annual employee salary in dollars
estimate underwriterrsquos estimate of end-of-year contributions in dollars
susan 1=plan was sold by a sales representative who has been specifically trained to deal exclu-sively with 401(k) plans (code-named Susan Shepard)
Source
Bryant PG and Smith MA (1995)Practical data analysis case studies in business statisticsChicago Irwin
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(retireplan)pairs(retireplan)
salinity Salinity data
Description
The salinity data frame has 28 observations on hydrological measurements from PamlicoSound North Carolina USA
Usage
data(salinity)
Format
This data frame contains the following columns
salinity salinity in Pamlico Sound
laggedsalinity salinity in Pamlico Sound during the previous six weeks
trend trend=1 if the data is the first six-week period of the spring and so forth Used to detectpossible effects of the seasonal warming trend
discharge discharge of fresh water from rivers into the sound
Source
Ruppert D and Carroll RJ (1980) Trimmed least squares estimation in the linear modelJournalof the American Statistical Association 75 828-838
22 sausage
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(salinity)pairs(salinity)
sausage Sausage data
Description
Thesausage data frame has data on 54 lsquohot dogrsquo sausages
Usage
data(sausage)
Format
This data frame contains the following columns
type type of meat
calories number of calories
sodium measure of sodium content
Source
Moore DS and McCabe GP (2003)Introduction to the Practice of Statistics Fourth EditionWH Freeman and Company
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sausage)attach(sausage)pointscols lt- c(redbluegreen)plot(sodiumcaloriescol=pointscols[type]pch=16)legend(200180c(beefporkpoultry)col=pointscolspch=rep(163))
scallop 23
scallop Scallop abundance data
Description
Thescallop data frame has 148 triplets concerning scallop abundance based on a 1990 surveycruise in the Atlantic continental shelf off Long Island New York USA
Usage
data(scallop)
Format
This data frame contains the following columns
latitude degrees latitude (north of the Equator)
longitude degrees longitude (west of Greenwich)
totcatch size of scallop catch at location specified by latitude and longitude
Source
Ecker MD and Heltshe JF (1994) Geostatistical estimates of scallop abundance InCase Studiesin BiometryLange N Ryan L Billard L Brillinger D Conquest L and Greenhouse J (eds)New York John Wiley amp Sons 107-124
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(scallop)pairs(scallop)
sitka Sitka spruce data
Description
Thesitka data frame contains measurements of log-size for 79 Sitka spruce trees grown in nor-mal or ozone-enriched environments Within each year the data are organised in four blocks cor-responding to four controlled environment chambers The first two chambers containing 27 treeseach have an ozone-enriched atmosphere the remaining two containing 12 and 13 trees respec-tively have a normal (control) atmosphere
24 spm
Usage
data(sitka)
Format
This data frame contains the following columns
idnum identification number of tree
order time order ranking within each tree
days time in days since 1st January 1988
logsize tree size measured on a logarithmic scale
ozone indicator ozone treatment 0=control1=ozone
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sitka)attach(sitka)library(lattice)ozonechar lt- rep(controlnrow(sitka))ozonechar[ozone==1] lt- ozonexyplot(logsize~days|ozonechardata=sitkagroups=idnumtype=b)
spm Fit a SemiParametric regression Model
Description
spm is used to fit semiparametric regression models using the mixed model representation of pe-nalized splines (per Ruppert Wand and Carroll 2003)
Usage
spm(formrandom=NULLgroup=NULLfamily=gaussiansparmethod=REMLomitmissing=NULL)
spm 25
Arguments
form a formula describing the model to be fit Note that an intercept is always in-cluded whether given in the formula or not
random random= 1 specifies inclusion of a random intercept according to the groupsspecified by the group argument
group a vector of labels for specifying groups
family for specification of the type of likelihood model assumed in the fitting May begaussianbinomial or poisson
sparmethod method for automatic smoothing parameter selection May be REML (re-stricted maximum likelihood) or ML (maximum likelihood)
omitmissing a logical value indicating whether fields with missing values are to be omitted
Details
See the SemiPar Usersrsquo Manual for details and examples
Value
An list object of classspm containing the fitted model The components are
fit mimics fit object of lme() for family=gaussian and glmmPQL() for family=binomialor family=poisson
info information about the inputs
aux auxiliary information such as variability estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
gam (in package lsquomgcvrsquo)lme (in package lsquonlmersquo)glmmPQL(in package lsquoMASSrsquo)plotspmsummaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)summary(fit)
26 summaryspm
data(califairpoll)attach(califairpoll)fit lt- spm(ozonelevel ~ f(daggettpressuregradient)+
f(inversionbaseheight) +f(inversionbasetemp))
summary(fit)par(mfrow=c(22))plot(fit)
The SemiPar User Manual contains several other examples and details of plotting parameters The current version of the manual is posted on the web-site wwwmathsunsweduau~wandpapershtml
summaryspm Semiparametric regression summary
Description
Takes a fittedspm object produced byspm() and summarises the fit
Usage
summaryspm(object)
Arguments
object a fittedspm object as produced byspm()
other arguments
Details
Produces tables for the linear (parametric) and non-linear (nonparametric) components The lineartable provides coefficient estimates standard errors and p-values The non-linear table providesdegrees of freedom values and other information
Value
The function generates summary tables
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
22 sausage
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(salinity)pairs(salinity)
sausage Sausage data
Description
Thesausage data frame has data on 54 lsquohot dogrsquo sausages
Usage
data(sausage)
Format
This data frame contains the following columns
type type of meat
calories number of calories
sodium measure of sodium content
Source
Moore DS and McCabe GP (2003)Introduction to the Practice of Statistics Fourth EditionWH Freeman and Company
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sausage)attach(sausage)pointscols lt- c(redbluegreen)plot(sodiumcaloriescol=pointscols[type]pch=16)legend(200180c(beefporkpoultry)col=pointscolspch=rep(163))
scallop 23
scallop Scallop abundance data
Description
Thescallop data frame has 148 triplets concerning scallop abundance based on a 1990 surveycruise in the Atlantic continental shelf off Long Island New York USA
Usage
data(scallop)
Format
This data frame contains the following columns
latitude degrees latitude (north of the Equator)
longitude degrees longitude (west of Greenwich)
totcatch size of scallop catch at location specified by latitude and longitude
Source
Ecker MD and Heltshe JF (1994) Geostatistical estimates of scallop abundance InCase Studiesin BiometryLange N Ryan L Billard L Brillinger D Conquest L and Greenhouse J (eds)New York John Wiley amp Sons 107-124
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(scallop)pairs(scallop)
sitka Sitka spruce data
Description
Thesitka data frame contains measurements of log-size for 79 Sitka spruce trees grown in nor-mal or ozone-enriched environments Within each year the data are organised in four blocks cor-responding to four controlled environment chambers The first two chambers containing 27 treeseach have an ozone-enriched atmosphere the remaining two containing 12 and 13 trees respec-tively have a normal (control) atmosphere
24 spm
Usage
data(sitka)
Format
This data frame contains the following columns
idnum identification number of tree
order time order ranking within each tree
days time in days since 1st January 1988
logsize tree size measured on a logarithmic scale
ozone indicator ozone treatment 0=control1=ozone
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sitka)attach(sitka)library(lattice)ozonechar lt- rep(controlnrow(sitka))ozonechar[ozone==1] lt- ozonexyplot(logsize~days|ozonechardata=sitkagroups=idnumtype=b)
spm Fit a SemiParametric regression Model
Description
spm is used to fit semiparametric regression models using the mixed model representation of pe-nalized splines (per Ruppert Wand and Carroll 2003)
Usage
spm(formrandom=NULLgroup=NULLfamily=gaussiansparmethod=REMLomitmissing=NULL)
spm 25
Arguments
form a formula describing the model to be fit Note that an intercept is always in-cluded whether given in the formula or not
random random= 1 specifies inclusion of a random intercept according to the groupsspecified by the group argument
group a vector of labels for specifying groups
family for specification of the type of likelihood model assumed in the fitting May begaussianbinomial or poisson
sparmethod method for automatic smoothing parameter selection May be REML (re-stricted maximum likelihood) or ML (maximum likelihood)
omitmissing a logical value indicating whether fields with missing values are to be omitted
Details
See the SemiPar Usersrsquo Manual for details and examples
Value
An list object of classspm containing the fitted model The components are
fit mimics fit object of lme() for family=gaussian and glmmPQL() for family=binomialor family=poisson
info information about the inputs
aux auxiliary information such as variability estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
gam (in package lsquomgcvrsquo)lme (in package lsquonlmersquo)glmmPQL(in package lsquoMASSrsquo)plotspmsummaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)summary(fit)
26 summaryspm
data(califairpoll)attach(califairpoll)fit lt- spm(ozonelevel ~ f(daggettpressuregradient)+
f(inversionbaseheight) +f(inversionbasetemp))
summary(fit)par(mfrow=c(22))plot(fit)
The SemiPar User Manual contains several other examples and details of plotting parameters The current version of the manual is posted on the web-site wwwmathsunsweduau~wandpapershtml
summaryspm Semiparametric regression summary
Description
Takes a fittedspm object produced byspm() and summarises the fit
Usage
summaryspm(object)
Arguments
object a fittedspm object as produced byspm()
other arguments
Details
Produces tables for the linear (parametric) and non-linear (nonparametric) components The lineartable provides coefficient estimates standard errors and p-values The non-linear table providesdegrees of freedom values and other information
Value
The function generates summary tables
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
scallop 23
scallop Scallop abundance data
Description
Thescallop data frame has 148 triplets concerning scallop abundance based on a 1990 surveycruise in the Atlantic continental shelf off Long Island New York USA
Usage
data(scallop)
Format
This data frame contains the following columns
latitude degrees latitude (north of the Equator)
longitude degrees longitude (west of Greenwich)
totcatch size of scallop catch at location specified by latitude and longitude
Source
Ecker MD and Heltshe JF (1994) Geostatistical estimates of scallop abundance InCase Studiesin BiometryLange N Ryan L Billard L Brillinger D Conquest L and Greenhouse J (eds)New York John Wiley amp Sons 107-124
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(scallop)pairs(scallop)
sitka Sitka spruce data
Description
Thesitka data frame contains measurements of log-size for 79 Sitka spruce trees grown in nor-mal or ozone-enriched environments Within each year the data are organised in four blocks cor-responding to four controlled environment chambers The first two chambers containing 27 treeseach have an ozone-enriched atmosphere the remaining two containing 12 and 13 trees respec-tively have a normal (control) atmosphere
24 spm
Usage
data(sitka)
Format
This data frame contains the following columns
idnum identification number of tree
order time order ranking within each tree
days time in days since 1st January 1988
logsize tree size measured on a logarithmic scale
ozone indicator ozone treatment 0=control1=ozone
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sitka)attach(sitka)library(lattice)ozonechar lt- rep(controlnrow(sitka))ozonechar[ozone==1] lt- ozonexyplot(logsize~days|ozonechardata=sitkagroups=idnumtype=b)
spm Fit a SemiParametric regression Model
Description
spm is used to fit semiparametric regression models using the mixed model representation of pe-nalized splines (per Ruppert Wand and Carroll 2003)
Usage
spm(formrandom=NULLgroup=NULLfamily=gaussiansparmethod=REMLomitmissing=NULL)
spm 25
Arguments
form a formula describing the model to be fit Note that an intercept is always in-cluded whether given in the formula or not
random random= 1 specifies inclusion of a random intercept according to the groupsspecified by the group argument
group a vector of labels for specifying groups
family for specification of the type of likelihood model assumed in the fitting May begaussianbinomial or poisson
sparmethod method for automatic smoothing parameter selection May be REML (re-stricted maximum likelihood) or ML (maximum likelihood)
omitmissing a logical value indicating whether fields with missing values are to be omitted
Details
See the SemiPar Usersrsquo Manual for details and examples
Value
An list object of classspm containing the fitted model The components are
fit mimics fit object of lme() for family=gaussian and glmmPQL() for family=binomialor family=poisson
info information about the inputs
aux auxiliary information such as variability estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
gam (in package lsquomgcvrsquo)lme (in package lsquonlmersquo)glmmPQL(in package lsquoMASSrsquo)plotspmsummaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)summary(fit)
26 summaryspm
data(califairpoll)attach(califairpoll)fit lt- spm(ozonelevel ~ f(daggettpressuregradient)+
f(inversionbaseheight) +f(inversionbasetemp))
summary(fit)par(mfrow=c(22))plot(fit)
The SemiPar User Manual contains several other examples and details of plotting parameters The current version of the manual is posted on the web-site wwwmathsunsweduau~wandpapershtml
summaryspm Semiparametric regression summary
Description
Takes a fittedspm object produced byspm() and summarises the fit
Usage
summaryspm(object)
Arguments
object a fittedspm object as produced byspm()
other arguments
Details
Produces tables for the linear (parametric) and non-linear (nonparametric) components The lineartable provides coefficient estimates standard errors and p-values The non-linear table providesdegrees of freedom values and other information
Value
The function generates summary tables
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
24 spm
Usage
data(sitka)
Format
This data frame contains the following columns
idnum identification number of tree
order time order ranking within each tree
days time in days since 1st January 1988
logsize tree size measured on a logarithmic scale
ozone indicator ozone treatment 0=control1=ozone
Source
Diggle PJ Heagerty P Liang K-Y and Zeger SL (2002)Analysis of Longitudinal DataSecond Edition Oxord Oxford University Press
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(sitka)attach(sitka)library(lattice)ozonechar lt- rep(controlnrow(sitka))ozonechar[ozone==1] lt- ozonexyplot(logsize~days|ozonechardata=sitkagroups=idnumtype=b)
spm Fit a SemiParametric regression Model
Description
spm is used to fit semiparametric regression models using the mixed model representation of pe-nalized splines (per Ruppert Wand and Carroll 2003)
Usage
spm(formrandom=NULLgroup=NULLfamily=gaussiansparmethod=REMLomitmissing=NULL)
spm 25
Arguments
form a formula describing the model to be fit Note that an intercept is always in-cluded whether given in the formula or not
random random= 1 specifies inclusion of a random intercept according to the groupsspecified by the group argument
group a vector of labels for specifying groups
family for specification of the type of likelihood model assumed in the fitting May begaussianbinomial or poisson
sparmethod method for automatic smoothing parameter selection May be REML (re-stricted maximum likelihood) or ML (maximum likelihood)
omitmissing a logical value indicating whether fields with missing values are to be omitted
Details
See the SemiPar Usersrsquo Manual for details and examples
Value
An list object of classspm containing the fitted model The components are
fit mimics fit object of lme() for family=gaussian and glmmPQL() for family=binomialor family=poisson
info information about the inputs
aux auxiliary information such as variability estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
gam (in package lsquomgcvrsquo)lme (in package lsquonlmersquo)glmmPQL(in package lsquoMASSrsquo)plotspmsummaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)summary(fit)
26 summaryspm
data(califairpoll)attach(califairpoll)fit lt- spm(ozonelevel ~ f(daggettpressuregradient)+
f(inversionbaseheight) +f(inversionbasetemp))
summary(fit)par(mfrow=c(22))plot(fit)
The SemiPar User Manual contains several other examples and details of plotting parameters The current version of the manual is posted on the web-site wwwmathsunsweduau~wandpapershtml
summaryspm Semiparametric regression summary
Description
Takes a fittedspm object produced byspm() and summarises the fit
Usage
summaryspm(object)
Arguments
object a fittedspm object as produced byspm()
other arguments
Details
Produces tables for the linear (parametric) and non-linear (nonparametric) components The lineartable provides coefficient estimates standard errors and p-values The non-linear table providesdegrees of freedom values and other information
Value
The function generates summary tables
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
spm 25
Arguments
form a formula describing the model to be fit Note that an intercept is always in-cluded whether given in the formula or not
random random= 1 specifies inclusion of a random intercept according to the groupsspecified by the group argument
group a vector of labels for specifying groups
family for specification of the type of likelihood model assumed in the fitting May begaussianbinomial or poisson
sparmethod method for automatic smoothing parameter selection May be REML (re-stricted maximum likelihood) or ML (maximum likelihood)
omitmissing a logical value indicating whether fields with missing values are to be omitted
Details
See the SemiPar Usersrsquo Manual for details and examples
Value
An list object of classspm containing the fitted model The components are
fit mimics fit object of lme() for family=gaussian and glmmPQL() for family=binomialor family=poisson
info information about the inputs
aux auxiliary information such as variability estimates
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
See Also
gam (in package lsquomgcvrsquo)lme (in package lsquonlmersquo)glmmPQL(in package lsquoMASSrsquo)plotspmsummaryspm
Examples
library(SemiPar)data(fossil)attach(fossil)fit lt- spm(strontiumratio~f(age))plot(fit)summary(fit)
26 summaryspm
data(califairpoll)attach(califairpoll)fit lt- spm(ozonelevel ~ f(daggettpressuregradient)+
f(inversionbaseheight) +f(inversionbasetemp))
summary(fit)par(mfrow=c(22))plot(fit)
The SemiPar User Manual contains several other examples and details of plotting parameters The current version of the manual is posted on the web-site wwwmathsunsweduau~wandpapershtml
summaryspm Semiparametric regression summary
Description
Takes a fittedspm object produced byspm() and summarises the fit
Usage
summaryspm(object)
Arguments
object a fittedspm object as produced byspm()
other arguments
Details
Produces tables for the linear (parametric) and non-linear (nonparametric) components The lineartable provides coefficient estimates standard errors and p-values The non-linear table providesdegrees of freedom values and other information
Value
The function generates summary tables
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
26 summaryspm
data(califairpoll)attach(califairpoll)fit lt- spm(ozonelevel ~ f(daggettpressuregradient)+
f(inversionbaseheight) +f(inversionbasetemp))
summary(fit)par(mfrow=c(22))plot(fit)
The SemiPar User Manual contains several other examples and details of plotting parameters The current version of the manual is posted on the web-site wwwmathsunsweduau~wandpapershtml
summaryspm Semiparametric regression summary
Description
Takes a fittedspm object produced byspm() and summarises the fit
Usage
summaryspm(object)
Arguments
object a fittedspm object as produced byspm()
other arguments
Details
Produces tables for the linear (parametric) and non-linear (nonparametric) components The lineartable provides coefficient estimates standard errors and p-values The non-linear table providesdegrees of freedom values and other information
Value
The function generates summary tables
Author(s)
MP Wand〈wandmathsunsweduau〉 (other contributors listed in SemiPar Usersrsquo Manual)
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Ganguli B and Wand MP (2005)SemiPar 10 Usersrsquo Manualhttpwwwmathsunsweduau~wandpapershtml
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
termstructure 27
See Also
spm plotspm
Examples
library(SemiPar)data(onions)attach(onions)logyield lt- log(yield)fit lt- spm(logyield~location+f(dens))summary(fit)
termstructure Term structure data
Description
The termstructure data frame has 117 observations on the prices of US STRIPS (SeparateTrading on Registered Interest and Principal of Securities) on December 31 1995
Usage
data(termstructure)
Format
This data frame contains the following columns
timetomaturity time in years between 31st December 1995 and the date on which the STRIPSmatures
price price of the STRIPS as a percent of par
Source
University of Houston Fixed Income Database
References
Jarrow R Ruppert D and Yu Y (2004) Estimating the term structure of corporate debt with asemiparametric penalized spline modelJournal of the American Statistical Association 99 57-66
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(termstructure)attach(termstructure)plot(timetomaturityprice)
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
28 tradeunion
tradeunion Trade union data
Description
Thetradeunion data frame has data on 534 US workers
Usage
data(tradeunion)
Format
This data frame contains the following columns
yearseducnumber of years of education
south indicator of living in southern region of USA
female gender indicator 0=male1=female
yearsexperiencenumber of years of work experience
unionmember indicator of trade union membership 0=non-member 1=member
wage wages in dollars per hour
age age in years
race 1=black 2=Hispanic 3=white
occupation 1=management 2=sales 3=clerical 4=service 5=professional 6=other
sector 0=other 1=manufacturing 2=construction
married indicator of being married 0=unmarried 1=married
Source
Berndt ER (1991)The Practice of EconometricsNew York Addison-Wesley
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(tradeunion)pairs(tradeunionpch=)
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
ustemp 29
ustemp US temperature data
Description
Theustemp data frame has 56 observations on the temperature and location of 56 US cities
Usage
data(ustemp)
Format
This data frame contains the following columns
city character string giving name of city and state (two-letter abbreviation)
mintemp average minimum January temperature
latitude degrees latitude (north of Equator)
longitude degrees longitude (west of Greenwich)
Source
Peixoto JL (1990) A property of well-formulated polynomial regression modelsAmericanStatistician 44 26-30
References
Ruppert D Wand MP and Carroll RJ (2003)Semiparametric RegressionCambridge University Presshttpstattamuedu~carrollsemiregbook
Examples
library(SemiPar)data(ustemp)attach(ustemp)greylevs lt- mintemp+20colvec lt- paste(greyascharacter(greylevs)sep=)plot(-longitudelatitudecol=colvecpch=16cex=3xlim=c(-130-60))text(-longitudelatitudeascharacter(city))
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
Index
lowastTopic datasetsageincome 2bpd 2califairpoll 3copper 4electemp 5ethanol 5fossil 7fuelframe 8janka 9lidar 10milanmort 12monitormercury 13onions 13pigweights 14ragweed 18retireplan 20salinity 21sausage 22scallop 22sitka 23termstructure 27tradeunion 27ustemp 28
lowastTopic internalSemiPar-internal 1
lowastTopic modelsfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
lowastTopic nonlinearspm 24
lowastTopic regressionfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17
residualsspm 19spm 24summaryspm 26
lowastTopic smoothfittedspm 6linesspm 10plotspm 15predictspm 16printspm 17residualsspm 19spm 24summaryspm 26
addmodelflag(SemiPar-internal ) 1
ageincome 2argsearch (SemiPar-internal ) 1
blendcol (SemiPar-internal ) 1bpd 2breakstring (SemiPar-internal ) 1
califairpoll 3computeCTRinvC
(SemiPar-internal ) 1copper 4
defaultbdry (SemiPar-internal ) 1defaultknots (SemiPar-internal )
1dftospar (SemiPar-internal ) 1dinvlink (SemiPar-internal ) 1dup (SemiPar-internal ) 1
electemp 5ethanol 5
fittedspm 6fossil 7fuelframe 8
gam 25gencorrrange
(SemiPar-internal ) 1
30
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
INDEX 31
getpixelinfo(SemiPar-internal ) 1
glmdftospar(SemiPar-internal ) 1
glmeAux (SemiPar-internal ) 1glmmPQL 25
imageFull (SemiPar-internal ) 1imageLegend (SemiPar-internal ) 1invlink (SemiPar-internal ) 1irlsridge (SemiPar-internal ) 1
janka 9
lidar 10linesspm 7 10 16ndash18 20link (SemiPar-internal ) 1lme 25lmeAux (SemiPar-internal ) 1lmeFitQr (SemiPar-internal ) 1
matrixsqrt (SemiPar-internal ) 1milanmort 12monitormercury 13
onions 13
pigweights 14pixelframe (SemiPar-internal ) 1plotspm 7 11 15 17 18 20 25 26plotControl (SemiPar-internal ) 1pointsInPoly (SemiPar-internal ) 1predictspm 7 11 16 16 18 20printspm 17
ragweed 18randomread (SemiPar-internal ) 1residualsspm 19retireplan 20ridgereg (SemiPar-internal ) 1rmchar (SemiPar-internal ) 1
salinity 21sausage 22scallop 22SemiPar-internal 1setplotdflts
(SemiPar-internal ) 1sitka 23spm 7 11 16ndash18 20 24 26spmArgRead (SemiPar-internal ) 1spmDesign (SemiPar-internal ) 1spmFormRead (SemiPar-internal ) 1spmKrigeRead (SemiPar-internal ) 1
spmPenRead (SemiPar-internal ) 1spmTermType (SemiPar-internal ) 1summaryspm 7 11 16ndash18 20 25 26
termstructure 27tpscov (SemiPar-internal ) 1tradeunion 27
ustemp 28
