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4hcDh=FhhhzL@ graphics:::mosaicplot
plot(Draft1970table, shade=TRUE)
mosaic(Draft1970table, gp=shading_Friendly)
# correspondence analysis
if(require(ca)) {
ca(Draft1970table)
plot(ca(Draft1970table))
}
# convert to a frequency data frame with ordered factors
Draft1970df <- as.data.frame(Draft1970table)
Draft1970df <- within(Draft1970df, {
Month <- ordered(Month)
Risk <- ordered(Risk, levels=rev(levels(Risk)))
})
str(Draft1970df)
# similar model, as a Poisson GLM
indep <- glm(Freq ~ Month + Risk, family = poisson, data = Draft1970df)
mosaic(indep, residuals_type="rstandard", gp=shading_Friendly)
# numeric scores for tests of ordinal factors
Cscore <- as.numeric(Draft1970df$Risk)
Rscore <- as.numeric(Draft1970df$Month)
# linear x linear association between Month and Risk
linlin <- glm(Freq ~ Month + Risk + Rscore:Cscore, family = poisson, data = Draft1970df)
# compare models
anova(indep, linlin, test="Chisq")
mosaic(linlin, residuals_type="rstandard", gp=shading_Friendly)
}
\keyword{datasets}
vcdExtra/man/print.Kappa.Rd 0000644 0001762 0000144 00000002351 13164013351 015261 0 ustar ligges users \name{print.Kappa}
\alias{print.Kappa}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{ Print Kappa }
\description{
This is a replacement for the \code{print.Kappa} method in \code{vcd},
adding display of \code{z} values to the \code{vcd} version and
optional confidence intervals.
}
\usage{
\method{print}{Kappa}(x, digits=max(getOption("digits") - 3, 3), CI=FALSE, level=0.95, ...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{x}{ A Kappa object}
\item{digits}{number of digits to print}
\item{CI}{Include confidence intervals in the display?}
\item{level}{confidence level}
\item{\dots}{ Other arguments }
}
%\details{
% ~~ If necessary, more details than the description above ~~
%}
\value{
Returns the Kappa object, invisibly.
}
%\references{ ~put references to the literature/web site here ~ }
\author{ Michael Friendly}
\seealso{
\code{\link[vcd]{confint.Kappa}}
}
\examples{
data("SexualFun")
Kappa(SexualFun)
print(Kappa(SexualFun), CI=TRUE)
# stratified 3-way table
apply(MSPatients, 3, Kappa)
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{htest}
\keyword{category}
vcdExtra/man/Donner.Rd 0000644 0001762 0000144 00000007420 14003052406 014316 0 ustar ligges users \name{Donner}
\alias{Donner}
\docType{data}
\title{
Survival in the Donner Party
}
\description{
This data frame contains information on the members of the Donner Party,
a group of people who attempted to migrate to California in 1846.
They were trapped by an early blizzard on the eastern side of the
Sierra Nevada mountains, and before they could be rescued,
nearly half of the party had died.
What factors affected who lived and who died?
}
\usage{data(Donner)}
\format{
A data frame with 90 observations on the following 5 variables.
\describe{
\item{\code{family}}{family name, a factor with 10 levels }
\item{\code{age}}{age of person, a numeric vector}
\item{\code{sex}}{a factor with levels \code{Female} \code{Male}}
\item{\code{survived}}{a numeric vector, 0 or 1}
\item{\code{death}}{date of death for those who died before rescue, a POSIXct}
}
}
\details{
This data frame uses the person's name as row labels.
\code{family} reflects a recoding of the last names of individuals
to reduce the number of factor levels.
The main families in the Donner party were: Donner, Graves, Breen and Reed.
The families of Murphy, Foster and Pike are grouped as \code{'MurFosPik'},
those of Fosdick and Wolfinger are coded as \code{'FosdWolf'}, and
all others as \code{'Other'}.
}
\source{
D. K. Grayson, 1990, "Donner party deaths: A demographic
assessment", \emph{J. Anthropological Research}, \bold{46}, 223-242.
Johnson, K. (1996). \emph{Unfortunate Emigrants: Narratives of the Donner Party}.
Logan, UT: Utah State University Press.
Additions, and dates of death from \url{http://user.xmission.com/~octa/DonnerParty/Roster.htm}.
}
\references{
Ramsey, F.L. and Schafer, D.W. (2002).
\emph{The Statistical Sleuth: A Course in Methods of Data Analysis}, (2nd ed), Duxbury.
Friendly, M. and Meyer, D. (2016).
\emph{Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data}.
Boca Raton, FL: Chapman & Hall/CRC. \url{http://ddar.datavis.ca}.
}
\seealso{
\code{donner} in \pkg{alr3},
\code{\link[Sleuth2]{case2001}} in \pkg{Sleuth2}(adults only) provide similar data sets.
}
\examples{
# conditional density plots
op <- par(mfrow=c(1,2), cex.lab=1.5)
cdplot(factor(survived) ~ age, subset=sex=='Male', data=Donner,
main="Donner party: Males", ylevels=2:1, ylab="Survived", yaxlabels=c("yes", "no"))
with(Donner, rug(jitter(age[sex=="Male"]), col="white", quiet=TRUE))
cdplot(factor(survived) ~ age, subset=sex=='Female', data=Donner,
main="Donner party: Females", ylevels=2:1, ylab="Survived", yaxlabels=c("yes", "no"))
with(Donner, rug(jitter(age[sex=="Female"]), col="white", quiet=TRUE))
par(op)
# fit some models
(mod1 <- glm(survived ~ age + sex, data=Donner, family=binomial))
(mod2 <- glm(survived ~ age * sex, data=Donner, family=binomial))
anova(mod2, test="Chisq")
(mod3 <- glm(survived ~ poly(age,2) * sex, data=Donner, family=binomial))
anova(mod3, test="Chisq")
LRstats(glmlist(mod1, mod2, mod3))
# plot fitted probabilities from mod2 and mod3
# idea from: http://www.ling.upenn.edu/~joseff/rstudy/summer2010_ggplot2_intro.html
library(ggplot2)
# separate linear fits on age for M/F
ggplot(Donner, aes(age, survived, color = sex)) +
geom_point(position = position_jitter(height = 0.02, width = 0)) +
stat_smooth(method = "glm", method.args = list(family = binomial), formula = y ~ x,
alpha = 0.2, size=2, aes(fill = sex))
# separate quadratics
ggplot(Donner, aes(age, survived, color = sex)) +
geom_point(position = position_jitter(height = 0.02, width = 0)) +
stat_smooth(method = "glm", method.args = list(family = binomial), formula = y ~ poly(x,2),
alpha = 0.2, size=2, aes(fill = sex))
}
\keyword{datasets}
vcdExtra/man/HairEyePlace.Rd 0000644 0001762 0000144 00000003323 13164013351 015365 0 ustar ligges users \name{HairEyePlace}
\alias{HairEyePlace}
\docType{data}
\title{
Hair Color and Eye Color in Caithness and Aberdeen
}
\description{
A three-way frequency table crossing eye color and hair color in two places, Caithness and Aberdeen,
Scotland.
These data were of interest to Fisher (1940) and others because
there are mixtures of people of Nordic, Celtic and Anglo-Saxon origin.
One or both tables have been widely analyzed in conjunction with
RC and canonical correlation models for categorical data, e.g.,
Becker and Clogg (1989).
}
\usage{data(HairEyePlace)}
\format{
The format is:
num [1:4, 1:5, 1:2] 326 688 343 98 38 116 84 48 241 584 ...
- attr(*, "dimnames")=List of 3
..$ Eye : chr [1:4] "Blue" "Light" "Medium" "Dark"
..$ Hair : chr [1:5] "Fair" "Red" "Medium" "Dark" ...
..$ Place: chr [1:2] "Caithness" "Aberdeen"
}
\details{
The hair and eye colors are ordered as in the original source,
suggesting that they form ordered categories.
}
\source{
This data was taken from the \code{colors} data in \pkg{logmult}.
}
\references{
Becker, M. P., and Clogg, C. C. (1989).
Analysis of Sets of Two-Way Contingency Tables Using Association Models.
\emph{Journal of the American Statistical Association}, 84(405), 142-151.
Fisher, R.A. (1940) The precision of discriminant functions.
\emph{Annals of Eugenics}, 10, 422-429.
}
\examples{
data(HairEyePlace)
# separate mosaics
mosaic(HairEyePlace[,,1], shade=TRUE, main="Caithness")
mosaic(HairEyePlace[,,2], shade=TRUE, main="Aberdeen")
# condition on Place
mosaic(~Hair + Eye |Place, data=HairEyePlace, shade=TRUE, legend=FALSE)
cotabplot(~Hair+Eye|Place, data=HairEyePlace, shade=TRUE, legend=FALSE)
}
\keyword{datasets}
vcdExtra/man/Burt.Rd 0000644 0001762 0000144 00000003727 13164013351 014016 0 ustar ligges users \name{Burt}
\alias{Burt}
\docType{data}
\title{
Burt (1950) Data on Hair, Eyes, Head and Stature
}
\description{
Cyril Burt (1950) gave these data, on a sample of 100 people from Liverpool,
to illustrate the application of a method of factor analysis (later
called multiple correspondence analysis) applied to
categorical data.
He presented these data initially in the form that has come to be called a "Burt table",
giving the univariate and bivariate frequencies for an n-way frequency table.
}
\usage{data("Burt")}
\format{
A frequency data frame (representing a 3 x 3 x 2 x 2 frequency table) with 36 observations on the following 5 variables.
\describe{
\item{\code{Hair}}{hair color, a factor with levels \code{Fair} \code{Red} \code{Dark}}
\item{\code{Eyes}}{eye color, a factor with levels \code{Light} \code{Mixed} \code{Dark}}
\item{\code{Head}}{head shape, a factor with levels \code{Narrow} \code{Wide}}
\item{\code{Stature}}{height, a factor with levels \code{Tall} \code{Short}}
\item{\code{Freq}}{a numeric vector}
}
}
\details{
Burt says: "In all, 217 individuals were examined, about two-thirds of them males.
But, partly to simplify the calculations and partly because the later observations were rather more trustworthy,
I shall here restrict my analysis to the data obtained from the last hundred males in the series."
\code{Head} and \code{Stature} reflect a binary coding where people are classified
according to whether they are below or above the average for the population.
}
\source{
Burt, C. (1950). The factorial analysis of qualitative data,
\emph{British Journal of Statistical Psychology}, 3(3), 166-185.
Table IX.
}
%\references{
%% ~~ possibly secondary sources and usages ~~
%}
\examples{
data(Burt)
mosaic(Freq ~ Hair + Eyes + Head + Stature, data=Burt, shade=TRUE)
#or
burt.tab <- xtabs(Freq ~ Hair + Eyes + Head + Stature, data=Burt)
mosaic(burt.tab, shade=TRUE)
}
\keyword{datasets}
vcdExtra/man/blogits.Rd 0000644 0001762 0000144 00000005317 13164013351 014542 0 ustar ligges users \name{blogits}
\alias{blogits}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Bivariate Logits and Log Odds Ratio
}
\description{
This function calculates the log odds and log odds ratio for
two binary responses classified by one or more stratifying
variables.
It is useful for plotting the results of bivariate logistic regression
models, such as those fit using \code{\link[VGAM]{vglm}} in the
\pkg{VGAM}.
}
\usage{
blogits(Y, add, colnames, row.vars, rev=FALSE)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{Y}{
A four-column matrix or data frame whose columns correspond to the 2 x 2
combinations of two binary responses.
}
\item{add}{
Constant added to all cells to allow for zero frequencies. The default
is 0.5 if \code{any(Y)==0} and 0 otherwise.
}
\item{colnames}{
Names for the columns of the results. The default is
\code{c("logit1", "logit2", "logOR")}. If less than three
names are supplied, the remaining ones are filled in from the default.
}
\item{row.vars}{
A data frame or matrix giving the factor levels of one or more factors
corresponding to the rows of \code{Y}
}
\item{rev}{A logical, indicating whether the order of the columns in \code{Y}
should be reversed.}
}
\details{
For two binary variables with levels 0,1 the logits are calculated
assuming the columns in \code{Y} are given in the order
11, 10, 01, 00, so the logits give the log odds of the 1 response
compared to 0.
If this is not the case, either use \code{rev=TRUE} or supply
\code{Y[,4:1]} as the first argument.
}
\value{
A data frame with \code{nrow(Y)} rows and \code{3 + ncol(row.vars)} columns
}
\references{
Friendly, M. and Meyer, D. (2016).
\emph{Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data}.
Boca Raton, FL: Chapman & Hall/CRC. \url{http://ddar.datavis.ca}.
}
\author{
Michael Friendly
}
%\note{
%%% ~~further notes~~
%}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
\code{\link[VGAM]{vglm}}
}
\examples{
data(Toxaemia)
tox.tab <- xtabs(Freq~class + smoke + hyper + urea, Toxaemia)
# reshape to 4-column matrix
toxaemia <- t(matrix(aperm(tox.tab), 4, 15))
colnames(toxaemia) <- c("hu", "hU", "Hu", "HU")
rowlabs <- expand.grid(smoke=c("0", "1-19", "20+"), class=factor(1:5))
toxaemia <- cbind(toxaemia, rowlabs)
# logits for H and U
logitsTox <- blogits(toxaemia[,4:1], add=0.5, colnames=c("logitH", "logitW"), row.vars=rowlabs)
logitsTox
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{manip}
%\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
vcdExtra/man/WorkerSat.Rd 0000644 0001762 0000144 00000002313 14202253304 015007 0 ustar ligges users \name{WorkerSat}
\alias{WorkerSat}
\docType{data}
\title{
Worker Satisfaction Data
}
\description{
Blue collar workers job satisfaction from large scale investigation in Denmark in 1968 (Andersen, 1991).
}
\usage{data("WorkerSat")}
\format{
A frequency data frame with 8 observations on the following 4 variables, representing the 2 x 2 x 2 classification of 715 cases.
\describe{
\item{\code{Manage}}{Quality of management, an ordered factor with levels \code{bad} < \code{good}}
\item{\code{Super}}{Supervisor satisfaction, an ordered factor with levels \code{low} < \code{high}}
\item{\code{Worker}}{Worker job satisfaction, an ordered factor with levels \code{low} < \code{high}}
\item{\code{Freq}}{a numeric vector}
}
}
%\details{
%%% ~~ If necessary, more details than the __description__ above ~~
%}
\source{
Originally from \url{https://online.stat.psu.edu/stat504/lesson/10/}
}
\references{
Andersen, E. B. (1991)
Statistical Analysis of Categorical Data, 2nd Ed., Springer-Verlag.
}
\examples{
data(WorkerSat)
worker.tab <- xtabs(Freq ~ Worker + Super + Manage, data=WorkerSat)
fourfold(worker.tab)
mosaic(worker.tab, shade=TRUE)
}
\keyword{datasets}
vcdExtra/man/Summarise.Rd 0000644 0001762 0000144 00000006307 13164013351 015044 0 ustar ligges users \name{Summarise}
\alias{Summarise}
\alias{Summarise.glmlist}
\alias{Summarise.loglmlist}
\alias{Summarise.default}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Brief Summary of Model Fit for glm and loglm Models
}
\description{
For \code{glm} objects, the \code{print} and \code{summary} methods give
too much information if all one wants to see is a brief summary of model
goodness of fit, and there is no easy way to display a compact comparison of
model goodness of fit for a collection of models fit to the same data.
All \code{loglm} models have equivalent glm forms, but the
\code{print} and \code{summary} methods give quite different results.
\code{Summarise} provides a brief summary for one or more models
fit to the same dataset
for which \code{logLik} and \code{nobs} methods exist
(e.g., \code{glm} and \code{loglm} models).
%This implementation is experimental, and is subject to change.
}
\usage{
Summarise(object, ...)
\method{Summarise}{glmlist}(object, ..., saturated = NULL, sortby = NULL)
\method{Summarise}{loglmlist}(object, ..., saturated = NULL, sortby = NULL)
\method{Summarise}{default}(object, ..., saturated = NULL, sortby = NULL)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{object}{
a fitted model object for which there exists a logLik method to extract the corresponding log-likelihood}
\item{\dots}{
optionally more fitted model objects
}
\item{saturated}{
saturated model log likelihood reference value (use 0 if deviance is not available)
}
\item{sortby}{
either a numeric or character string specifying
the column in the result by which the rows are sorted (in decreasing order)}
}
\details{
The function relies on residual degrees of freedom for the LR chisq test being available
in the model object. This is true for objects inheriting from
\code{lm}, \code{glm}, \code{loglm}, \code{polr}
and \code{negbin}.
}
\value{
A data frame (also of class \code{anova}) with columns
\code{c("AIC", "BIC", "LR Chisq", "Df", "Pr(>Chisq)")}.
Row names are taken from the names of the model object(s).
}
%\references{
%% ~put references to the literature/web site here ~
%}
\author{
Achim Zeileis
}
%\note{
%% ~~further notes~~
%}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
\code{\link[stats]{logLik}}, \code{\link[stats]{glm}}, \code{\link[MASS]{loglm}},
\code{\link{logLik.loglm}}, \code{\link{modFit}}
}
\examples{
data(Mental)
indep <- glm(Freq ~ mental+ses,
family = poisson, data = Mental)
Summarise(indep)
Cscore <- as.numeric(Mental$ses)
Rscore <- as.numeric(Mental$mental)
coleff <- glm(Freq ~ mental + ses + Rscore:ses,
family = poisson, data = Mental)
roweff <- glm(Freq ~ mental + ses + mental:Cscore,
family = poisson, data = Mental)
linlin <- glm(Freq ~ mental + ses + Rscore:Cscore,
family = poisson, data = Mental)
# compare models
Summarise(indep, coleff, roweff, linlin)
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{models}
%\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
vcdExtra/man/split3d.Rd 0000644 0001762 0000144 00000006054 13164013351 014460 0 ustar ligges users \name{split3d}
\Rdversion{1.1}
\alias{split3d}
\alias{split3d.shape3d}
\alias{split3d.list}
\alias{range3d}
\alias{center3d}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Subdivide a 3D Object
}
\description{
Subdivides a \code{shape3d} object or a list of \code{shape3d} objects
into objects of the same shape along a given dimension
according to the proportions or frequencies specified in vector(s).
\code{split3d} is the basic workhorse used in \code{\link{mosaic3d}},
but may be useful in other contexts.
\code{range3d} and \code{center3d} are utility functions, also useful in other contexts.
}
\usage{
split3d(obj, ...)
\method{split3d}{shape3d}(obj, p, dim, space = 0.1, ...)
\method{split3d}{list}(obj, p, dim, space = 0.1, ...)
range3d(obj)
center3d(obj)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{obj}{
A \code{shape3d} object, or a list composed of them
}
\item{\dots}{
Other arguments for split3d methods
}
\item{p}{
For a single \code{shade3d} object, a vector of proportions
(or a vector of non-negative numbers which will be normed to proportions)
indicating the number of subdivisions and their scaling along dimension \code{dim}.
For a list of \code{shade3d} objects, a matrix whose columns indicate the
subdivisions of each object.
}
\item{dim}{
The dimension along which the object is to be subdivided.
Either an integer: 1, 2, or 3, or a character: "x", "y", or "z".
}
\item{space}{
The total space used to separate the copies of the object along
dimension \code{dim}.
The unit inter-object space is therefore \code{space/(length(p)-1)}.
}
}
\details{
The resulting list of \code{shape3d} objects is actually composed of \emph{copies}
of the input object(s), scaled according to the proportions in \code{p}
and then translated to make their range along the splitting dimension
equal to that of the input object(s).
}
\value{
\code{split3d} returns a list of \code{shape3d} objects.
\code{range3d} returns a 2 x 3 matrix, whose first row contains
the minima on dimensions x, y, z, and whose second row contains
the maxima.
\code{center3d} returns a numeric vector containing
the means of the minima and maxima on dimensions x, y, z.
}
\author{
Duncan Murdoch, with refinements by Michael Friendly
}
%\note{
%%% ~~further notes~~
%}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
\code{\link{mosaic3d}}
\code{\link[rgl]{shapelist3d}} for the plotting of lists of \code{shape3d} objects.
}
\examples{
if (require(rgl)) {
open3d()
cube <- cube3d(alpha=0.4)
sl1 <- split3d(cube, c(.2, .3, .5), 1)
col <- c("#FF000080", "#E5E5E580", "#0000FF80")
shapelist3d(sl1, col=col)
open3d()
p <- matrix(c(.6, .4, .5, .5, .2, .8), nrow=2)
sl2 <- split3d(sl1, p, 2)
shapelist3d(sl2, col=col)
}
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{dplot}
%\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
vcdExtra/man/Caesar.Rd 0000644 0001762 0000144 00000004742 13164013351 014276 0 ustar ligges users \name{Caesar}
\alias{Caesar}
\docType{data}
\title{Risk Factors for Infection in Caesarian Births}
\description{Data from infection from birth by Caesarian section, classified by \code{Risk} (two levels),
whether \code{Antibiotics} were used (two levels) and whether the Caesarian section was \code{Planned} or not.
The outcome is \code{Infection} (three levels).}
\usage{
data(Caesar)
}
\format{
A 4-dimensional array resulting from cross-tabulating 4 variables for 251 observations. The variable names and their levels are:
\tabular{rll}{
No \tab Name \tab Levels \cr
1\tab \code{Infection}\tab \code{"Type 1", "Type 2", "None"}\cr
2\tab \code{Risk}\tab \code{"Yes", "No"} (presence of risk factors)\cr
3\tab \code{Antibiotics}\tab \code{"Yes", "No"} (were antibiotics given?)\cr
4\tab \code{Planned}\tab \code{"Yes", "No"} (was the C section planned?)\cr
}
}
\details{
\code{Infection} is regarded as the response variable here.
There are quite a few 0 cells here, particularly when \code{Risk}
is absent and the Caesarian section was unplanned.
Should these be treated as structural or sampling zeros?
}
\source{
% \cite{Fahrmeir:94}
Fahrmeir, L. & Tutz, G. (1994).
Multivariate Statistical Modelling Based on Generalized Linear Models
New York: Springer Verlag, Table 1.1.
}
%\references{
%}
\seealso{\code{\link[Fahrmeir]{caesar}} for the same data recorded as a frequency data frame
with other variables.}
\examples{
data(Caesar)
#display table; note that there are quite a few 0 cells
structable(Caesar)
require(MASS)
# baseline model, Infection as response
Caesar.mod0 <- loglm(~Infection + (Risk*Antibiotics*Planned), data=Caesar)
# NB: Pearson chisq cannot be computed due to the 0 cells
Caesar.mod0
mosaic(Caesar.mod0, main="Baseline model")
# Illustrate handling structural zeros
zeros <- 0+ (Caesar >0)
zeros[1,,1,1] <- 1
structable(zeros)
# fit model excluding possible structural zeros
Caesar.mod0s <- loglm(~Infection + (Risk*Antibiotics*Planned), data=Caesar,
start=zeros)
Caesar.mod0s
anova(Caesar.mod0, Caesar.mod0s, test="Chisq")
mosaic (Caesar.mod0s)
# what terms to add?
add1(Caesar.mod0, ~.^2, test="Chisq")
# add Association of Infection:Antibiotics
Caesar.mod1 <- update(Caesar.mod0, ~.+Infection:Antibiotics)
anova(Caesar.mod0, Caesar.mod1, test="Chisq")
mosaic(Caesar.mod1, gp=shading_Friendly, main="Adding Infection:Antibiotics")
}
\keyword{datasets}
vcdExtra/man/logseries.Rd 0000644 0001762 0000144 00000006707 14202244463 015103 0 ustar ligges users \name{logseries}
\alias{Logseries}
\alias{dlogseries}
\alias{plogseries}
\alias{qlogseries}
\alias{rlogseries}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
The Logarithmic Series Distribution
}
\description{
The logarithmic series distribution is a long-tailed distribution
introduced by
Fisher etal. (1943)
in connection with data on the abundance of individuals
classified by species.
These functions provide the density, distribution function, quantile function and
random generation for the
logarithmic series distribution with parameter \code{prob}.
}
\usage{
dlogseries(x, prob = 0.5, log = FALSE)
plogseries(q, prob = 0.5, lower.tail = TRUE, log.p = FALSE)
qlogseries(p, prob = 0.5, lower.tail = TRUE, log.p = FALSE, max.value = 10000)
rlogseries(n, prob = 0.5)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{x, q}{
vector of quantiles representing the number of events.
}
\item{prob}{
parameter for the distribution, \code{0 < prob < 1}
}
\item{log, log.p}{
logical; if TRUE, probabilities \code{p} are given as \code{log(p)}
}
\item{lower.tail}{
logical; if TRUE (default), probabilities are \eqn{P[X \le x]}{P[X <= x]},
otherwise, \eqn{P[X > x]}{P[X > x]}.
}
\item{p}{
vector of probabilities
}
\item{max.value}{
maximum value returned by \code{qlogseries}
}
\item{n}{
number of observations for \code{rlogseries}
}
}
\details{
The logarithmic series distribution with \code{prob} = \eqn{p} has density
\deqn{
p ( x ) = \alpha p^x / x
}
for \eqn{x = 1, 2, \dots}, where \eqn{\alpha= -1 / \log(1 - p)}
and \eqn{0 < p <1}. Note that counts \code{x==2} cannot occur.
}
\value{
\code{dlogseries} gives the density,
\code{plogseries} gives the distribution function,
\code{qlogseries} gives the quantile function, and
\code{rlogseries} generates random deviates.
%% ~Describe the value returned
%% If it is a LIST, use
%% \item{comp1 }{Description of 'comp1'}
%% \item{comp2 }{Description of 'comp2'}
%% ...
}
\references{
\url{https://en.wikipedia.org/wiki/Logarithmic_distribution}
Fisher, R. A. and Corbet, A. S. and Williams, C. B. (1943).
The relation between the number of species and the number of individuals
\emph{Journal of Animal Ecology}, 12, 42-58.
}
\author{
Michael Friendly, using
original code modified from the \code{gmlss.dist} package
by Mikis Stasinopoulos.
}
%\note{
%%% ~~further notes~~
%}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
\code{\link[stats]{Distributions}}, ~~~
}
\examples{
XL <-expand.grid(x=1:5, p=c(0.33, 0.66, 0.99))
lgs.df <- data.frame(XL, prob=dlogseries(XL[,"x"], XL[,"p"]))
lgs.df$p = factor(lgs.df$p)
str(lgs.df)
require(lattice)
mycol <- palette()[2:4]
xyplot( prob ~ x, data=lgs.df, groups=p,
xlab=list('Number of events (k)', cex=1.25),
ylab=list('Probability', cex=1.25),
type='b', pch=15:17, lwd=2, cex=1.25, col=mycol,
key = list(
title = 'p',
points = list(pch=15:17, col=mycol, cex=1.25),
lines = list(lwd=2, col=mycol),
text = list(levels(lgs.df$p)),
x=0.9, y=0.98, corner=c(x=1, y=1)
)
)
# random numbers
hist(rlogseries(200, prob=.4), xlab='x')
hist(rlogseries(200, prob=.8), xlab='x')
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{distribution}
%\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
vcdExtra/man/loglin-utilities.Rd 0000644 0001762 0000144 00000016154 13164013351 016375 0 ustar ligges users \name{loglin-utilities}
\alias{loglin-utilities}
\alias{conditional}
\alias{joint}
\alias{loglin2formula}
\alias{loglin2string}
\alias{markov}
\alias{mutual}
\alias{saturated}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Loglinear Model Utilities
}
\description{
These functions generate lists of terms to specify a loglinear model
in a form compatible with \code{\link[stats]{loglin}} and also provide for conversion to an
equivalent \code{\link[MASS]{loglm}} specification or a shorthand character
string representation.
They allow for a more conceptual
way to specify such models by a function for their type, as opposed
to just an uninterpreted list of model terms
and also allow
easy specification of marginal models for a given contingency table.
They are intended to be used as tools in higher-level modeling and
graphics functions, but can also be used directly.
}
\usage{
conditional(nf, table = NULL, factors = 1:nf, with = nf)
joint(nf, table = NULL, factors = 1:nf, with = nf)
markov(nf, factors = 1:nf, order = 1)
mutual(nf, table = NULL, factors = 1:nf)
saturated(nf, table = NULL, factors = 1:nf)
loglin2formula(x, env = parent.frame())
loglin2string(x, brackets = c("[", "]"), sep = ",", collapse = " ", abbrev)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{nf}{
number of factors for which to generate the model
}
\item{table}{
a contingency table used only for factor names in the model, typically the output from \code{\link[base]{table}}
and possibly permuted with \code{aperm}
}
\item{factors}{
names of factors used in the model formula when \code{table} is not specified
}
\item{with}{
For \code{joint} and \code{conditional} models, \code{with} gives the
indices of the factors against which all others are considered jointly
or conditionally independent
}
\item{order}{
For \code{markov}, this gives the order of the Markov chain model for the
factors. An \code{order=1} Markov chain allows associations among
sequential pairs of factors, e.g., \code{[A,B], [B,C], [C,D]} \dots.
An \code{order=2} Markov chain allows associations among
sequential triples.
}
\item{x}{
For the \code{loglin2*} functions,
a list of terms in a loglinear model, such as returned by \code{conditional}, \code{joint},
\dots
}
\item{env}{
For \code{loglin2formula}, environment in which to evaluate the formula
}
\item{brackets}{
For \code{loglin2string},
characters to use to surround model terms.
Either a single character string containing two characters (e.g., \code{'[]'}
or a character vector of length two.
}
\item{sep}{
For \code{loglin2string},
the separator character string used for factor names within a given model term
}
\item{collapse}{
For \code{loglin2string},
the character string used between terms in the the model string
}
\item{abbrev}{
For \code{loglin2string},
whether and how to abbreviate the terms in the string representation.
This has not yet been implemented.
}
}
\details{
The main model specification functions, \code{conditional}, \code{joint},
\code{markov}, \dots, \code{saturated},
return a list of vectors indicating the marginal totals to be fit,
via the \code{margin} argument to \code{\link[stats]{loglin}}.
Each element of this list corresponds to a high-order
term in a hierarchical loglinear model, where, e.g., a term
like \code{c("A", "B")} is equivalent to the \code{\link[MASS]{loglm}}
term \code{"A:B"} and hence automatically includes all low-order terms.
Note that these can be used to supply the \code{expected} argument for
the default \code{\link[vcd]{mosaic}} function, when the data is supplied
as a contingency table.
The table below shows some typical results in terms of the standard shorthand
notation for loglinear models, with factors A, B, C, \dots, where brackets
are used to delimit the high-order terms in the loglinear model.
\tabular{llll}{
\strong{function} \tab \strong{3-way} \tab \strong{4-way} \tab \strong{5-way} \cr
\code{mutual} \tab [A] [B] [C] \tab
[A] [B] [C] [D] \tab
[A] [B] [C] [D] [E] \cr
\code{joint} \tab [AB] [C] \tab
[ABC] [D] \tab
[ABCE] [E] \cr
\code{joint (with=1)} \tab
[A] [BC] \tab
[A] [BCD] \tab
[A] [BCDE] \cr
\code{conditional} \tab
[AC] [BC] \tab
[AD] [BD] [CD] \tab
[AE] [BE] [CE] [DE] \cr
\code{condit (with=1)} \tab
[AB] [AC] \tab
[AB] [AC] [AD] \tab
[AB] [AC] [AD] [AE] \cr
\code{markov (order=1)} \tab
[AB] [BC] \tab
[AB] [BC] [CD] \tab
[AB] [BC] [CD] [DE] \cr
\code{markov (order=2)} \tab
[A] [B] [C] \tab
[ABC] [BCD] \tab
[ABC] [BCD] [CDE] \cr
\code{saturated} \tab
[ABC] \tab [ABCD] \tab [ABCDE] \cr
}
\code{loglin2formula} converts the output of one of these to a model formula
suitable as the \code{formula} for of \code{\link[MASS]{loglm}}.
\code{loglin2string} converts the output of one of these to a string
describing the loglinear model in the shorthand bracket notation,
e.g., \code{"[A,B] [A,C]"}.
}
\value{
For the main model specification functions, \code{conditional}, \code{joint},
\code{markov}, \dots, the result is
a list of vectors (terms), where the elements in each vector are the
names of the factors. The elements of the list are given names
\code{term1, term2, \dots}.
}
\references{
These functions were inspired by the original SAS implementation of
mosaic displays, described in the \emph{User's Guide},
\url{http://www.datavis.ca/mosaics/mosaics.pdf}
}
\author{
Michael Friendly
}
%\note{
%%% ~~further notes~~
%}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
\code{\link[stats]{loglin}},
\code{\link[MASS]{loglm}}
}
\examples{
joint(3, table=HairEyeColor)
# as a formula or string
loglin2formula(joint(3, table=HairEyeColor))
loglin2string(joint(3, table=HairEyeColor))
joint(2, HairEyeColor) # marginal model for [Hair] [Eye]
# other possibilities
joint(4, factors=letters, with=1)
joint(5, factors=LETTERS)
joint(5, factors=LETTERS, with=4:5)
conditional(4)
conditional(4, with=3:4)
# use in mosaic displays or other strucplots
mosaic(HairEyeColor, expected=joint(3))
mosaic(HairEyeColor, expected=conditional(3))
# use with MASS::loglm
cond3 <- loglin2formula(conditional(3, table=HairEyeColor))
cond3 <- loglin2formula(conditional(3)) # same, with factors 1,2,3
require(MASS)
loglm(cond3, data=HairEyeColor)
saturated(3, HairEyeColor)
loglin2formula(saturated(3, HairEyeColor))
loglin2string(saturated(3, HairEyeColor))
loglin2string(saturated(3, HairEyeColor), brackets='{}', sep=', ')
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{models}
%\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
vcdExtra/man/Glass.Rd 0000644 0001762 0000144 00000004164 13164013351 014147 0 ustar ligges users \name{Glass}
\alias{Glass}
\docType{data}
\title{
British Social Mobility from Glass(1954)
}
\description{
Glass(1954) gave this 5 x 5 table on the occupations of 3500 British fathers and their sons.
}
\usage{data("Glass")}
\format{
A frequency data frame with 25 observations on the following 3 variables representing a 5 x 5 table with 3500 cases.
\describe{
\item{\code{father}}{a factor with levels \code{Managerial} \code{Professional} \code{Skilled} \code{Supervisory} \code{Unskilled}}
\item{\code{son}}{a factor with levels \code{Managerial} \code{Professional} \code{Skilled} \code{Supervisory} \code{Unskilled}}
\item{\code{Freq}}{a numeric vector}
}
}
\details{
The occupational categories in order of status are:
(1) Professional \& High Administrative
(2) Managerial, Executive \& High Supervisory
(3) Low Inspectional \& Supervisory
(4) Routine Nonmanual \& Skilled Manual
(5) Semi- \& Unskilled Manual
However, to make the point that factors are ordered alphabetically by default,
Friendly \& Meyer (2016) introduce this data set in the form given here.
}
\source{
Glass, D. V. (1954), \emph{Social Mobility in Britain}. The Free Press.
}
\references{
Bishop, Y. M. M. and Fienberg, S. E. and Holland, P. W. (1975).
\emph{Discrete Multivariate Analysis: Theory and Practice}, MIT Press.
Friendly, M. and Meyer, D. (2016).
\emph{Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data}.
Boca Raton, FL: Chapman & Hall/CRC. \url{http://ddar.datavis.ca}.
}
\examples{
data(Glass)
glass.tab <- xtabs(Freq ~ father + son, data=Glass)
largs <- list(set_varnames=list(father="Father's Occupation", son="Son's Occupation"),
abbreviate=10)
gargs <- list(interpolate=c(1,2,4,8))
mosaic(glass.tab, shade=TRUE, labeling_args=largs, gp_args=gargs,
main="Alphabetic order", legend=FALSE, rot_labels=c(20,90,0,70))
# reorder by status
ord <- c(2, 1, 4, 3, 5)
mosaic(glass.tab[ord, ord], shade=TRUE, labeling_args=largs, gp_args=gargs,
main="Effect order", legend=FALSE, rot_labels=c(20,90,0,70))
}
\keyword{datasets}
vcdExtra/man/AirCrash.Rd 0000644 0001762 0000144 00000004132 13771417075 014604 0 ustar ligges users \name{AirCrash}
\alias{AirCrash}
\docType{data}
\title{
Air Crash Data
}
\description{
Data on all fatal commercial airplane crashes from 1993--2015.
Excludes small planes (less than 6 passengers) and non-commercial (cargo, military, private) aircraft.
}
\usage{data("AirCrash")}
\format{
A data frame with 439 observations on the following 5 variables.
\describe{
\item{\code{Phase}}{phase of the flight, a factor with levels \code{en route} \code{landing} \code{standing} \code{take-off} \code{unknown}}
\item{\code{Cause}}{a factor with levels \code{criminal} \code{human error} \code{mechanical} \code{unknown} \code{weather}}
\item{\code{date}}{date of crash, a Date}
\item{\code{Fatalities}}{number of fatalities, a numeric vector}
\item{\code{Year}}{year, a numeric vector}
}
}
\details{
\code{Phase} of the flight was cleaned by combining related variants, spelling, etc.
}
\source{
Originally from David McCandless,
\url{http://www.informationisbeautiful.net/visualizations/plane-truth-every-single-commercial-plane-crash-visualized/},
with the data at
\url{https://docs.google.com/spreadsheets/d/1OvDq4_BtbR6nSnnHnjD5hVC3HQ-ulZPGbo0RDGbzM3Q/edit?usp=drive_web},
downloaded April 14, 2015.
}
\references{
Rick Wicklin,
\url{http://blogs.sas.com/content/iml/2015/03/30/visualizing-airline-crashes.html}
}
\examples{
data(AirCrash)
aircrash.tab <- xtabs(~Phase + Cause, data=AirCrash)
mosaic(aircrash.tab, shade=TRUE)
# fix label overlap
mosaic(aircrash.tab, shade=TRUE,
labeling_args=list(rot_labels=c(30, 30, 30, 30)))
# reorder by Phase
phase.ord <- rev(c(3,4,1,2,5))
mosaic(aircrash.tab[phase.ord,], shade=TRUE,
labeling_args=list(rot_labels=c(30, 30, 30, 30)), offset_varnames=0.5)
# reorder by frequency
phase.ord <- order(rowSums(aircrash.tab), decreasing=TRUE)
cause.ord <- order(colSums(aircrash.tab), decreasing=TRUE)
mosaic(aircrash.tab[phase.ord,cause.ord], shade=TRUE,
labeling_args=list(rot_labels=c(30, 30, 30, 30)))
library(ca)
aircrash.ca <- ca(aircrash.tab)
plot(aircrash.ca)
}
\keyword{datasets}
vcdExtra/man/Kway.Rd 0000644 0001762 0000144 00000010376 14202244517 014017 0 ustar ligges users \name{Kway}
\Rdversion{1.1}
\alias{Kway}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Fit All K-way Models in a GLM
}
\description{
Generate and fit all 0-way, 1-way, 2-way, ... k-way terms in a glm.
This function is designed mainly for hierarchical
loglinear models (or \code{glm}s
in the poisson family), where it is desired to find the
highest-order terms necessary to achieve a satisfactory fit.
Using \code{\link[stats]{anova}} on the resulting \code{\link{glmlist}}
object will then give sequential tests of the pooled contributions of
all terms of degree \eqn{k+1} over and above those of degree \eqn{k}.
This function is also intended as an example of a generating function
for \code{\link{glmlist}} objects, to facilitate model comparison, extraction,
summary and plotting of model components, etc., perhaps using \code{lapply} or similar.
}
\usage{
Kway(formula, family=poisson, data, ..., order = nt, prefix = "kway")
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{formula}{
a two-sided formula for the 1-way effects in the model.
The LHS should be the response, and the RHS should be the first-order terms
connected by \code{+} signs.
}
\item{family}{
a description of the error distribution and link function to be used in the
model. This can be a character string naming a family function, a family
function or the result of a call to a family function.
(See \code{\link[stats]{family}} for details of family functions.)
}
\item{data}{
an optional data frame, list or environment (or object coercible by
\code{\link[base]{as.data.frame}} to a data frame) containing the variables in the model. If not
found in data, the variables are taken from \code{environment(formula)}, typically the
environment from which \code{glm} is called.
}
\item{\dots}{
Other arguments passed to \code{glm}
}
\item{order}{
Highest order interaction of the models generated. Defaults to the number
of terms in the model formula.
}
\item{prefix}{
Prefix used to label the models fit in the \code{glmlist} object.
}
}
\details{
With \code{y} as the response in the \code{formula}, the 0-way (null) model
is \code{y ~ 1}.
The 1-way ("main effects") model is that specified in the
\code{formula} argument. The k-way model is generated using the formula
\code{. ~ .^k}.
With the default \code{order = nt}, the final model is the saturated model.
As presently written, the function requires a two-sided formula with an explicit
response on the LHS. For frequency data in table form (e.g., produced by \code{xtabs})
you the \code{data} argument is coerced to a data.frame, so you
should supply the \code{formula} in the form \code{Freq ~ } \dots.
}
\value{
An object of class \code{glmlist}, of length \code{order+1}
containing the 0-way, 1-way, ...
models up to degree \code{order}.
}
%\references{
%%% ~put references to the literature/web site here ~
%}
\author{
Michael Friendly and Heather Turner
}
%\note{
%%% ~~further notes~~
%}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
\code{\link{glmlist}},
\code{\link{Summarise}} (soon to be deprecated),
\code{\link{LRstats}}
}
\examples{
## artificial data
factors <- expand.grid(A=factor(1:3), B=factor(1:2), C=factor(1:3), D=factor(1:2))
Freq <- rpois(nrow(factors), lambda=40)
df <- cbind(factors, Freq)
mods3 <- Kway(Freq ~ A + B + C, data=df, family=poisson)
LRstats(mods3)
mods4 <- Kway(Freq ~ A + B + C + D, data=df, family=poisson)
LRstats(mods4)
# JobSatisfaction data
data(JobSatisfaction, package="vcd")
modSat <- Kway(Freq ~ management+supervisor+own, data=JobSatisfaction,
family=poisson, prefix="JobSat")
LRstats(modSat)
anova(modSat, test="Chisq")
# Rochdale data: very sparse, in table form
data(Rochdale, package="vcd")
\dontrun{
modRoch <- Kway(Freq~EconActive + Age + HusbandEmployed + Child +
Education + HusbandEducation + Asian + HouseholdWorking,
data=Rochdale, family=poisson)
LRstats(modRoch)
}
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{models}
%\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
vcdExtra/man/Hoyt.Rd 0000644 0001762 0000144 00000006520 13164013351 014017 0 ustar ligges users \name{Hoyt}
\Rdversion{1.1}
\alias{Hoyt}
\docType{data}
\title{Minnesota High School Graduates}
\description{Minnesota high school graduates of June 1930 were classified with respect to
(a) \code{Rank} by thirds in their graduating class, (b) post-high school \code{Status} in April 1939 (4 levels), (c) \code{Sex},
(d) father's \code{Occupation}al status (7 levels, from 1=High to 7=Low).
The data were first presented by Hoyt et al. (1959) and have been analyzed by Fienberg(1980), Plackett(1974)
and others.
}
\usage{
data(Hoyt)
}
\format{
A 4-dimensional array resulting from cross-tabulating 4 variables for 13968 observations. The variable names and their levels are:
\tabular{rll}{
No \tab Name \tab Levels \cr
1\tab \code{Status}\tab \code{"College", "School", "Job", "Other"}\cr
2\tab \code{Rank}\tab \code{"Low", "Middle", "High"}\cr
3\tab \code{Occupation}\tab \code{"1", "2", "3", "4", "5", "6", "7"}\cr
4\tab \code{Sex}\tab \code{"Male", "Female"}\cr
}
}
\details{Post high-school \code{Status} is natural to consider as the response.
\code{Rank} and father's \code{Occupation} are ordinal variables.}
\source{
% \cite{Hoyt-etal:59}
% \cite{Fienberg:80 [pp.91-92]}
% \cite{Plackett:74}
% \cite{minn38{MASS}}
Fienberg, S. E. (1980).
\emph{The Analysis of Cross-Classified Categorical Data}.
Cambridge, MA: MIT Press, p. 91-92.
R. L. Plackett, (1974).
\emph{The Analysis of Categorical Data}. London: Griffin.
}
\references{
Hoyt, C. J., Krishnaiah, P. R. and Torrance, E. P. (1959)
Analysis of complex contingency tables,
\emph{Journal of Experimental Education} 27, 187-194.
}
\seealso{
\code{\link[MASS]{minn38}} provides the same data as a data frame.
}
\examples{
data(Hoyt)
# display the table
structable(Status+Sex ~ Rank+Occupation, data=Hoyt)
# mosaic for independence model
plot(Hoyt, shade=TRUE)
# examine all pairwise mosaics
pairs(Hoyt, shade=TRUE)
# collapse Status to College vs. Non-College
Hoyt1 <- collapse.table(Hoyt, Status=c("College", rep("Non-College",3)))
plot(Hoyt1, shade=TRUE)
#################################################
# fitting models with loglm, plotting with mosaic
#################################################
# fit baseline log-linear model for Status as response
require(MASS)
hoyt.mod0 <- loglm(~ Status + (Sex*Rank*Occupation), data=Hoyt1)
hoyt.mod0
mosaic(hoyt.mod0, gp=shading_Friendly, main="Baseline model: Status + (Sex*Rank*Occ)")
# add one-way association of Status with factors
hoyt.mod1 <- loglm(~ Status * (Sex + Rank + Occupation) + (Sex*Rank*Occupation), data=Hoyt1)
hoyt.mod1
mosaic(hoyt.mod1, gp=shading_Friendly, main="Status * (Sex + Rank + Occ)")
# can we drop any terms?
drop1(hoyt.mod1, test="Chisq")
# assess model fit
anova(hoyt.mod0, hoyt.mod1)
# what terms to add?
add1(hoyt.mod1, ~.^2, test="Chisq")
# add interaction of Sex:Occupation on Status
hoyt.mod2 <- update(hoyt.mod1, ~.+Status:Sex:Occupation)
mosaic(hoyt.mod2, gp=shading_Friendly, main="Adding Status:Sex:Occupation")
# compare model fits
anova(hoyt.mod0, hoyt.mod1, hoyt.mod2)
# Alternatively, try stepwise analysis, heading toward the saturated model
steps <- step(hoyt.mod0, direction="forward", scope=~Status*Sex*Rank*Occupation)
# display anova
steps$anova
}
\keyword{datasets}
vcdExtra/man/Accident.Rd 0000644 0001762 0000144 00000007113 13164013351 014605 0 ustar ligges users \name{Accident}
\alias{Accident}
\docType{data}
\title{
Traffic Accident Victims in France in 1958
}
\description{
Bertin (1983) used these data to illustrate the cross-classification of
data by numerous variables, each of which could have various types
and could be assigned to various visual attributes.
For modeling and visualization purposes, the data can be treated as a
4-way table using loglinear models and mosaic displays, or as a
frequency-weighted data frame using a binomial response for
\code{result} (\code{"Died"} vs. \code{"Injured"}) and plots of
predicted probabilities.
}
\usage{data(Accident)}
\format{
A data frame in frequency form (comprising a 5 x 2 x 4 x 2 table)
with 80 observations on the following 5 variables.
\describe{
\item{\code{age}}{an ordered factor with levels \code{0-9} < \code{10-19} < \code{20-29} < \code{30-49} < \code{50+}}
\item{\code{result}}{a factor with levels \code{Died} \code{Injured}}
\item{\code{mode}}{mode of transportation,
a factor with levels \code{4-Wheeled} \code{Bicycle} \code{Motorcycle} \code{Pedestrian}}
\item{\code{gender}}{a factor with levels \code{Female} \code{Male}}
\item{\code{Freq}}{a numeric vector}
}
}
\details{
\code{age} is an ordered factor, but arguably, \code{mode} should be treated as ordered, with levels
\code{Pedestrian} < \code{Bicycle} < \code{Motorcycle} < \code{4-Wheeled}
as Bertin does. This affects the parameterization in models, so we don't do this directly in the
data frame.
}
\source{
Bertin (1983), p. 30; original data from the Ministere des Travaux Publics
}
\references{
Bertin, J. (1983), \emph{Semiology of Graphics}, University of Wisconsin Press.
}
\examples{
# examples
data(Accident)
head(Accident)
# for graphs, reorder mode
Accident$mode <- ordered(Accident$mode,
levels=levels(Accident$mode)[c(4,2,3,1)])
# Bertin's table
accident_tab <- xtabs(Freq ~ gender+mode+age+result, data=Accident)
structable(mode+gender ~ age+result, data=accident_tab)
## Loglinear models
## ----------------
# mutual independence
acc.mod0 <- glm(Freq ~ age+result+mode+gender, data=Accident, family=poisson)
LRstats(acc.mod0)
mosaic(acc.mod0, ~mode+age+gender+result)
# result as a response
acc.mod1 <- glm(Freq ~ age*mode*gender + result, data=Accident, family=poisson)
LRstats(acc.mod1)
mosaic(acc.mod1, ~mode+age+gender+result,
labeling_args = list(abbreviate = c(gender=1, result=4)))
# allow two-way association of result with each explanatory variable
acc.mod2 <- glm(Freq ~ age*mode*gender + result*(age+mode+gender), data=Accident, family=poisson)
LRstats(acc.mod2)
mosaic(acc.mod2, ~mode+age+gender+result,
labeling_args = list(abbreviate = c(gender=1, result=4)))
acc.mods <- glmlist(acc.mod0, acc.mod1, acc.mod2)
LRstats(acc.mods)
## Binomial (logistic regression) models for result
## ------------------------------------------------
library(car) # for Anova()
acc.bin1 <- glm(result=='Died' ~ age+mode+gender,
weights=Freq, data=Accident, family=binomial)
Anova(acc.bin1)
acc.bin2 <- glm(result=='Died' ~ (age+mode+gender)^2,
weights=Freq, data=Accident, family=binomial)
Anova(acc.bin2)
acc.bin3 <- glm(result=='Died' ~ (age+mode+gender)^3,
weights=Freq, data=Accident, family=binomial)
Anova(acc.bin3)
# compare models
anova(acc.bin1, acc.bin2, acc.bin3, test="Chisq")
# visualize probability of death with effect plots
\dontrun{
library(effects)
plot(allEffects(acc.bin1), ylab='Pr (Died)')
plot(allEffects(acc.bin2), ylab='Pr (Died)')
}
#}
\keyword{datasets}
vcdExtra/man/vcdExtra-package.Rd 0000644 0001762 0000144 00000013215 14202252636 016251 0 ustar ligges users \name{vcdExtra-package}
\alias{vcdExtra-package}
\alias{vcdExtra}
\docType{package}
\title{
Extensions and additions to vcd: Visualizing Categorical Data
}
\description{
\if{html}{\figure{vcdExtra-logo.png}{options: align='right' alt='logo' width='100'}}
This package provides additional data sets, documentation, and
a few functions designed to extend the \code{vcd} package for Visualizing Categorical Data
and the \code{gnm} package for Generalized Nonlinear Models.
In particular, vcdExtra extends mosaic, assoc and sieve plots from vcd to handle glm() and gnm() models and
adds a 3D version in \code{\link{mosaic3d}}.
This package is now a support package for the book, \emph{Discrete Data Analysis with R} by
Michael Friendly and David Meyer, Chapman & Hall/CRC, 2016,
\url{https://www.crcpress.com/Discrete-Data-Analysis-with-R-Visualization-and-Modeling-Techniques-for/Friendly-Meyer/9781498725835}
with a number of additional data sets,
and functions.
The web site for the book is \url{http://ddar.datavis.ca}.
}
\details{
\tabular{ll}{
Package: \tab vcdExtra\cr
Type: \tab Package\cr
Version: \tab 0.7-6\cr
Date: \tab 2021-02-12\cr
License: \tab GPL version 2 or newer\cr
LazyLoad: \tab yes\cr
}
The main purpose of this package is to serve as a sandbox for
introducing extensions of
mosaic plots and related graphical methods
that apply to loglinear models fitted using
\code{glm()} and related, generalized nonlinear models fitted
with \code{gnm()} in the \code{\link[gnm]{gnm-package}} package.
A related purpose is to fill in some holes in the analysis of
categorical data in R, not provided in base R, the \pkg{vcd},
or other commonly used packages.
The method \code{\link{mosaic.glm}}
extends the \code{\link[vcd]{mosaic.loglm}} method in the \pkg{vcd}
package to this wider class of models. This method also works for
the generalized nonlinear models fit with the \code{\link[gnm]{gnm-package}} package,
including models for square tables and models with multiplicative associations.
\code{\link{mosaic3d}}
introduces a 3D generalization of mosaic displays using the
\pkg{rgl} package.
In addition, there are several new data sets, a tutorial vignette,
\describe{
\item{vcd-tutorial}{Working with categorical data with R and the vcd package, \code{vignette("vcd-tutorial", package = "vcdExtra") }}
}
and a few functions for manipulating categorical data sets and working with models for
categorical data.
A new class, \code{\link{glmlist}}, is introduced for working with
collections of \code{glm} objects, e.g., \code{\link{Kway}} for fitting
all K-way models from a basic marginal model, and \code{\link{LRstats}}
for brief statistical summaries of goodness-of-fit for a collection of
models.
For square tables with ordered factors, \code{\link{Crossings}} supplements the
specification of terms in model formulas using
\code{\link[gnm]{Symm}},
\code{\link[gnm]{Diag}},
\code{\link[gnm]{Topo}}, etc. in the \code{\link[gnm]{gnm-package}}.
Some of these extensions may be migrated into \pkg{vcd} or \pkg{gnm}.
A collection of demos is included to illustrate fitting and visualizing a wide variety of models:
\describe{
\item{mental-glm}{Mental health data: mosaics for glm() and gnm() models}
\item{occStatus}{Occupational status data: Compare mosaic using expected= to mosaic.glm}
\item{ucb-glm}{UCBAdmissions data: Conditional independence via loglm() and glm()}
\item{vision-quasi}{VisualAcuity data: Quasi- and Symmetry models}
\item{yaish-unidiff}{Yaish data: Unidiff model for 3-way table}
\item{Wong2-3}{Political views and support for women to work (U, R, C, R+C and RC(1) models)}
\item{Wong3-1}{Political views, support for women to work and national welfare spending (3-way, marginal, and conditional independence models)}
\item{housing}{Visualize glm(), multinom() and polr() models from \code{example(housing, package="MASS")}}
}
Use \code{ demo(package="vcdExtra")} for a complete current list.
The \pkg{vcdExtra} package now contains a large number of data sets illustrating various forms of categorical data analysis
and related visualizations, from simple to advanced. Use \code{data(package="vcdExtra")} for a
complete list, or \code{datasets(package="vcdExtra")} for an annotated one showing the \code{class} and
\code{dim} for each data set.
}
\author{
Michael Friendly
Maintainer: Michael Friendly || (\href{https://orcid.org/0000-0002-3237-0941}{ORCID})
}
\references{
Friendly, M. \emph{Visualizing Categorical Data}, Cary NC: SAS Institute, 2000.
Web materials: \url{http://www.datavis.ca/books/vcd/}.
Friendly, M. and Meyer, D. (2016).
\emph{Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data}.
Boca Raton, FL: Chapman & Hall/CRC. \url{http://ddar.datavis.ca}.
Meyer, D.; Zeileis, A. & Hornik, K.
The Strucplot Framework: Visualizing Multi-way Contingency Tables with vcd
\emph{Journal of Statistical Software}, 2006, \bold{17}, 1-48.
Available in R via \code{vignette("strucplot", package = "vcd")}
Turner, H. and Firth, D. \emph{Generalized nonlinear models in R: An overview of the gnm package},
2007,
\url{http://eprints.ncrm.ac.uk/472/}. Available in R via \code{vignette("gnmOverview", package = "gnm")}.
}
\keyword{ package }
\seealso{
\code{\link[gnm]{gnm-package}}, for an extended range of models for contingency tables
\code{\link[vcd]{mosaic}} for details on mosaic displays within the strucplot framework.
%~~ Optional links to other man pages, e.g. ~~
%~~ \code{\link[:-package]{}} ~~
}
\examples{
example(mosaic.glm)
demo("mental-glm")
}
vcdExtra/man/JobSat.Rd 0000644 0001762 0000144 00000002173 13164013351 014256 0 ustar ligges users \name{JobSat}
\Rdversion{1.1}
\alias{JobSat}
\docType{data}
\title{Cross-classification of job satisfaction by income}
\description{
This data set is a contingency table of job satisfaction by income for a small sample
of black males from the 1996 General Social Survey, as used by Agresti (2002) for
an example.
}
\usage{data(JobSat)}
\format{
A 4 x 4 contingency table of \code{income} by \code{satisfaction}, with the following structure:
\preformatted{
table [1:4, 1:4] 1 2 1 0 3 3 6 1 10 10 ...
- attr(*, "dimnames")=List of 2
..$ income : chr [1:4] "< 15k" "15-25k" "25-40k" "> 40k"
..$ satisfaction: chr [1:4] "VeryD" "LittleD" "ModerateS" "VeryS"
}
}
\details{
Both \code{income} and \code{satisfaction} are ordinal variables, and are so ordered in the
table. Measures of association, visualizations, and models should take ordinality into
account.
}
\source{
Agresti, A. Categorical Data Analysis John Wiley & Sons, 2002,
Table 2.8, p. 57.
}
%\references{
% ~~ possibly secondary sources and usages ~~
%}
\examples{
data(JobSat)
assocstats(JobSat)
GKgamma(JobSat)
}
\keyword{datasets}
vcdExtra/man/PhdPubs.Rd 0000644 0001762 0000144 00000003706 13771412536 014460 0 ustar ligges users \name{PhdPubs}
\alias{PhdPubs}
\docType{data}
\title{
Publications of PhD Candidates
}
\description{
A data set giving the number of publications by doctoral candidates in biochemistry
in relation to various predictors, originally from Long (1997).
There is a large number of zero counts. Is there evidence for
a separate group of non-publishers?
}
\usage{data(PhdPubs)}
\format{
A data frame with 915 observations on the following 6 variables.
\describe{
\item{\code{articles}}{number of articles published in the final three years of PhD studies}
\item{\code{female}}{dummy variable for gender, coded \code{1} for female}
\item{\code{married}}{dummy variable for marital status, coded \code{1} for married}
\item{\code{kid5}}{number of young children, age 5 and under}
\item{\code{phdprestige}}{prestige of the PhD department. The higher the number the more prestigious the program. }
\item{\code{mentor}}{number of publications by the mentor in the preceeding three years}
}
}
\details{
In this version of the data set, \code{phdprestige} had been rounded to the nearest integer.
A Stata version with the continuous values was subsequently found at
\url{https://www.stata-press.com/data/lf2/couart2.dta}
}
\source{
Long, J. S. (1997). \emph{Regression Models for Categorical and Limited Dependent Variables},
Sage.
Long, J. S. & Freese, J. (2006). \emph{Regression Models for Categorical Dependent Variables Using Stata}, 2nd Ed.,
Stata Press.
}
%\references{
%% ~~ possibly secondary sources and usages ~~
%}
\examples{
data(PhdPubs)
# very uninformative
hist(PhdPubs$articles, breaks=0:19, col="pink", xlim=c(0,20),
xlab="Number of Articles")
library(vcd)
rootogram(goodfit(PhdPubs$articles), xlab="Number of Articles")
# compare with negative binomial
rootogram(goodfit(PhdPubs$articles, type="nbinomial"),
xlab="Number of Articles", main="Negative binomial")
}
\keyword{datasets}
vcdExtra/man/collapse.table.Rd 0000644 0001762 0000144 00000007204 13164013351 015764 0 ustar ligges users \name{collapse.table}
\alias{collapse.table}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{Collapse Levels of a Table}
\description{
Collapse (or re-label) variables in a
a contingency table, array or \code{ftable} object by re-assigning levels of the table variables.
}
\usage{
collapse.table(table, ...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{table}{A \code{\link[base]{table}}, \code{\link[base]{array}} or \code{\link[stats]{ftable}} object}
\item{\dots}{ A collection of one or more assignments of factors of the table to
a list of levels }
}
\details{
Each of the \code{\dots} arguments must be of the form
\code{variable = levels}, where \code{variable} is the name of one of the table
dimensions, and \code{levels} is a character or numeric vector of length equal
to the corresponding dimension of the table.
}
\value{
A \code{xtabs} and \code{table} object, representing the original table with
one or more of its factors collapsed or rearranged into other levels.
}
%\references{ ~put references to the literature/web site here ~ }
\author{Michael Friendly}
%\note{ ~~further notes~~
%
% ~Make other sections like Warning with \section{Warning }{....} ~
%}
\seealso{
\code{\link{expand.dft}} expands a frequency data frame to case form.
\code{\link[base]{margin.table}} "collapses" a table in a different way, by
summing over table dimensions.
}
\examples{
# create some sample data in table form
sex <- c("Male", "Female")
age <- letters[1:6]
education <- c("low", 'med', 'high')
data <- expand.grid(sex=sex, age=age, education=education)
counts <- rpois(36, 100)
data <- cbind(data, counts)
t1 <- xtabs(counts ~ sex + age + education, data=data)
structable(t1)
## age a b c d e f
## sex education
## Male low 119 101 109 85 99 93
## med 94 98 103 108 84 84
## high 81 88 96 110 100 92
## Female low 107 104 95 86 103 96
## med 104 98 94 95 110 106
## high 93 85 90 109 99 86
# collapse age to 3 levels
t2 <- collapse.table(t1, age=c("A", "A", "B", "B", "C", "C"))
structable(t2)
## age A B C
## sex education
## Male low 220 194 192
## med 192 211 168
## high 169 206 192
## Female low 211 181 199
## med 202 189 216
## high 178 199 185
# collapse age to 3 levels and pool education: "low" and "med" to "low"
t3 <- collapse.table(t1, age=c("A", "A", "B", "B", "C", "C"),
education=c("low", "low", "high"))
structable(t3)
## age A B C
## sex education
## Male low 412 405 360
## high 169 206 192
## Female low 413 370 415
## high 178 199 185
# change labels for levels of education to 1:3
t4 <- collapse.table(t1, education=1:3)
structable(t4)
structable(t4)
## age a b c d e f
## sex education
## Male 1 119 101 109 85 99 93
## 2 94 98 103 108 84 84
## 3 81 88 96 110 100 92
## Female 1 107 104 95 86 103 96
## 2 104 98 94 95 110 106
## 3 93 85 90 109 99 86
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{manip}
\keyword{attribute}% __ONLY ONE__ keyword per line
vcdExtra/man/DaytonSurvey.Rd 0000644 0001762 0000144 00000004624 13164013351 015553 0 ustar ligges users \name{DaytonSurvey}
\alias{DaytonSurvey}
\docType{data}
\title{
Dayton Student Survey on Substance Use
}
\description{
This data, from Agresti (2002), Table 9.1, gives the result of a 1992
survey in Dayton Ohio of 2276 high school seniors on
whether they had ever used
alcohol, cigarettes and marijuana.
}
\usage{data(DaytonSurvey)}
\format{
A frequency data frame with 32 observations on the following 6 variables.
\describe{
\item{\code{cigarette}}{a factor with levels \code{Yes} \code{No}}
\item{\code{alcohol}}{a factor with levels \code{Yes} \code{No}}
\item{\code{marijuana}}{a factor with levels \code{Yes} \code{No}}
\item{\code{sex}}{a factor with levels \code{female} \code{male}}
\item{\code{race}}{a factor with levels \code{white} \code{other}}
\item{\code{Freq}}{a numeric vector}
}
}
\details{
Agresti uses the letters G (\code{sex}), R (\code{race}),
A (\code{alcohol}), C (\code{cigarette}), M (\code{marijuana}) to refer to the table variables,
and this usage is followed in the examples below.
Background variables include \code{sex} and \code{race} of the
respondent (GR), typically treated as explanatory, so that any
model for the full table should include the term \code{sex:race}.
Models for the reduced table, collapsed over \code{sex} and \code{race}
are not entirely unreasonable, but don't permit the estimation
of the effects of these variables on the responses.
The full 5-way table contains a number of cells with counts of 0 or 1,
as well as many cells with large counts, and even the ACM table
collapsed over GR has some small cell counts. Consequently,
residuals for these models in mosaic displays are best represented
as standardized (adjusted) residuals.
}
\source{
Agresti, A. (2002). \emph{Categorical Data Analysis}, 2nd Ed.,
New York: Wiley-Interscience, Table 9.1, p. 362.
}
\references{
Thompson, L. (2009). \emph{R (and S-PLUS) Manual to Accompany Agresti's Categorical Data},
\url{http://www.stat.ufl.edu/~aa/cda/Thompson_manual.pdf}
}
\examples{
data(DaytonSurvey)
mod.GR <- glm(Freq ~ . + sex*race, data=DaytonSurvey, family=poisson) # mutual independence + GR
mod.homog.assoc <- glm(Freq ~ .^2, data=DaytonSurvey, family=poisson) # homogeneous association
# collapse over sex and race
Dayton.ACM <- aggregate(Freq ~ cigarette+alcohol+marijuana, data=DaytonSurvey, FUN=sum)
}
\keyword{datasets}
vcdExtra/man/HLtest.Rd 0000644 0001762 0000144 00000007070 14202242025 014274 0 ustar ligges users \name{HLtest}
\alias{HosmerLemeshow}
\alias{HLtest}
\alias{plot.HLtest}
\alias{print.HLtest}
\alias{rootogram.HLtest}
\alias{summary.HLtest}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Hosmer-Lemeshow Goodness of Fit Test
}
\description{
The \code{HLtest} function computes the classical Hosmer-Lemeshow (1980) goodness of fit test
for a binomial \code{glm} object in logistic regression
The general idea is to assesses whether or not the observed event rates match expected event rates in subgroups of the
model population. The Hosmer-Lemeshow test specifically identifies subgroups as the deciles of fitted event values,
or other quantiles as determined by the \code{g} argument.
Given these subgroups, a simple chisquare test on \code{g-2} df is used.
In addition to \code{print} and \code{summary} methods, a \code{plot} method is
supplied to visualize the discrepancies between observed and fitted frequencies.
}
\usage{
HosmerLemeshow(model, g = 10)
HLtest(model, g = 10)
\method{print}{HLtest}(x, ...)
\method{summary}{HLtest}(object, ...)
\method{plot}{HLtest}(x, ...)
\method{rootogram}{HLtest}(x, ...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{model}{
A \code{glm} model object in the \code{binomial} family
}
\item{g}{
Number of groups used to partition the fitted values for the
GOF test.
}
\item{x, object}{
A \code{HLtest} object
}
\item{\dots}{
Other arguments passed down to methods
}
}
%\details{
%%% ~~ If necessary, more details than the description above ~~
%}
\value{
A class \code{HLtest} object with the following components:
%% If it is a LIST, use
\item{table}{A data.frame describing the results of partitioning the data into \code{g}
groups with the following columns: \code{cut}, \code{total}, \code{obs}, \code{exp}, \code{chi}}
\item{chisq}{The chisquare statistics}
\item{df}{Degrees of freedom}
\item{p.value}{p value}
\item{groups}{Number of groups}
\item{call}{\code{model} call}
%% ...
}
\references{
Hosmer, David W., Lemeshow, Stanley (1980).
A goodness-of-fit test for multiple logistic regression model.
\emph{Communications in Statistics, Series A}, 9, 1043-1069.
Hosmer, David W., Lemeshow, Stanley (2000).
\emph{Applied Logistic Regression}, New York: Wiley, ISBN 0-471-61553-6
Lemeshow, S. and Hosmer, D.W. (1982).
A review of goodness of fit statistics for use in the development of logistic regression models.
\emph{American Journal of Epidemiology}, 115(1), 92-106.
}
\author{
Michael Friendly
}
%\note{
%%% ~~further notes~~
%}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
\code{\link[vcd]{rootogram}}, ~~~
}
\examples{
data(birthwt, package="MASS")
# how to do this without attach?
attach(birthwt)
race = factor(race, labels = c("white", "black", "other"))
ptd = factor(ptl > 0)
ftv = factor(ftv)
levels(ftv)[-(1:2)] = "2+"
bwt <- data.frame(low = factor(low), age, lwt, race,
smoke = (smoke > 0), ptd, ht = (ht > 0), ui = (ui > 0), ftv)
detach(birthwt)
options(contrasts = c("contr.treatment", "contr.poly"))
BWmod <- glm(low ~ ., family=binomial, data=bwt)
(hlt <- HLtest(BWmod))
str(hlt)
summary(hlt)
plot(hlt)
# basic model
BWmod0 <- glm(low ~ age, family=binomial, data=bwt)
(hlt0 <- HLtest(BWmod0))
str(hlt0)
summary(hlt0)
plot(hlt0)
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{htest}
%\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
vcdExtra/man/Fungicide.Rd 0000644 0001762 0000144 00000003531 14202241547 014774 0 ustar ligges users \name{Fungicide}
\alias{Fungicide}
\docType{data}
\title{
Carcinogenic Effects of a Fungicide
}
\description{
Data from Gart (1971) on the carcinogenic effects of a certain fungicide
in two strains of mice.
Of interest is how the association between \code{group} (Control, Treated)
and \code{outcome} (Tumor, No Tumor) varies with \code{sex} and
\code{strain} of the mice.
Breslow (1976) used this data to illustrate the application of linear models
to log odds ratios.
}
\usage{data(Fungicide)}
\format{
The data comprise a set of four 2 x 2 tables classifying 403 mice, either
Control or Treated and whether or not a tumor was later observed.
The four groups represent the combinations of sex and strain of mice.
The format is:
num [1:2, 1:2, 1:2, 1:2] 5 4 74 12 3 2 84 14 10 4 ...
- attr(*, "dimnames")=List of 4
..$ group : chr [1:2] "Control" "Treated"
..$ outcome: chr [1:2] "Tumor" "NoTumor"
..$ sex : chr [1:2] "M" "F"
..$ strain : chr [1:2] "1" "2"
}
\details{
All tables have some small cells, so a continuity correction is recommended.
}
\source{
Gart, J. J. (1971). The comparison of proportions: a review of significance tests,
confidence intervals and adjustments for stratification.
\emph{International Statistical Review},
39, 148-169.
}
\references{
Breslow, N. (1976), Regression analysis of the log odds ratio:
A method for retrospective studies, \emph{Biometrics}, 32(3), 409-416.
}
\examples{
data(Fungicide)
# loddsratio was moved to vcd; requires vcd_1.3-3+
\dontrun{
if (require(vcd)) {
fung.lor <- loddsratio(Fungicide, correct=TRUE)
fung.lor
confint(fung.lor)
}
}
# visualize odds ratios in fourfold plots
cotabplot(Fungicide, panel=cotab_fourfold)
# -- fourfold() requires vcd >= 1.2-10
fourfold(Fungicide, p_adjust_method="none")
}
\keyword{datasets}
vcdExtra/man/Vietnam.Rd 0000644 0001762 0000144 00000003303 13164013351 014473 0 ustar ligges users \name{Vietnam}
\alias{Vietnam}
\docType{data}
\title{
Student Opinion about the Vietnam War
}
\description{
A survey of student opinion on the Vietnam War was taken at the
University of North Carolina at Chapel Hill in May 1967 and published
in the student newspaper. Students were asked to fill in ballot
papers stating which policy out of A,B,C or D they supported.
Responses were cross-classified by gender/year.
The response categories were:
\describe{
\item{\code{A}}{Defeat North Vietnam by widespread bombing and land invasion}
\item{\code{B}}{Maintain the present policy}
\item{\code{C}}{De-escalate military activity, stop bombing and begin negotiations}
\item{\code{D}}{Withdraw military forces Immediately}
}
}
\usage{data(Vietnam)}
\format{
A frequency data frame with 40 observations representing a 2 x 5 x 4 contingency table
on the following 4 variables.
\describe{
\item{\code{sex}}{a factor with levels \code{Female} \code{Male}}
\item{\code{year}}{year of study, an ordered factor with levels
\code{Freshmen}, \code{Sophomore}, \code{Junior}, \code{Senior}, \code{Grad student}}
\item{\code{response}}{a factor with levels \code{A} \code{B} \code{C} \code{D}}
\item{\code{Freq}}{cell frequency, a numeric vector}
}
}
\details{
For some analyses, it is useful to treat \code{year} as numeric,
and possibly assign grad students a value \code{year=7}.
}
\source{
Aitken, M. etal, 1989, \emph{Statistical Modelling in GLIM}
}
\references{
Friendly, M. (2000), \emph{Visualizing Categorical Data}, SAS Institute, Cary, NC,
Example 7.9.
}
\examples{
data(Vietnam)
## maybe str(Vietnam) ; plot(Vietnam) ...
}
\keyword{datasets}
vcdExtra/man/expand.dft.Rd 0000644 0001762 0000144 00000004457 13164013351 015136 0 ustar ligges users \name{expand.dft}
\alias{expand.dft}
\alias{expand.table}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{ Expand a frequency table to case form}
\description{
Converts a frequency table, given either as a table object or a data frame in frequency form
to a data frame representing individual observations in the table.
}
\usage{
expand.dft(x, var.names = NULL, freq = "Freq", ...)
expand.table(x, var.names = NULL, freq = "Freq", ...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{x}{A table object, or a data frame in frequency form containing factors and one numeric variable
representing the cell frequency for that combination of factors. }
\item{var.names}{A list of variable names for the factors, if you wish to override those already in the table}
\item{freq}{The name of the frequency variable in the table}
\item{\dots}{Other arguments passed down to \code{type.convert}. In particular, pay attention to
\code{na.strings} (default: \code{na.strings=NA} if there are missing cells) and \code{as.is}
(default: \code{as.is=FALSE}, converting character vectors to factors).}
}
\details{
\code{expand.table} is a synonym for \code{expand.dft}.
}
\value{
A data frame containing the factors in the table and as many observations as are
represented by the total of the \code{freq} variable.
}
\references{
Originally posted on R-Help, Jan 20, 2009,
http://tolstoy.newcastle.edu.au/R/e6/help/09/01/1873.html
Friendly, M. and Meyer, D. (2016).
\emph{Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data}.
Boca Raton, FL: Chapman & Hall/CRC. \url{http://ddar.datavis.ca}.
}
\author{ Mark Schwarz }
%\note{ ~~further notes~~
% ~Make other sections like Warning with \section{Warning }{....} ~
%}
\seealso{ \code{\link[utils]{type.convert}}, \code{\link[gnm]{expandCategorical}}}
\examples{
library(vcd)
art <- xtabs(~Treatment + Improved, data = Arthritis)
art
artdf <- expand.dft(art)
str(artdf)
# 1D case
(tab <- table(sample(head(letters), 20, replace=TRUE)))
expand.table(tab, var.names="letter")
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{manip}
\keyword{array}
% __ONLY ONE__ keyword per line
vcdExtra/man/Dyke.Rd 0000644 0001762 0000144 00000005044 13164013351 013770 0 ustar ligges users \name{Dyke}
\Rdversion{1.1}
\alias{Dyke}
\docType{data}
\title{Sources of Knowledge of Cancer}
\description{Observational data on a sample of 1729 individuals, cross-classified in a 2^5 table according to their sources of
information (read newspapers, listen to the radio, do 'solid' reading, attend lectures) and whether they have
good or poor knowledge regarding cancer. Knowledge of cancer is often treated as the response.}
\usage{
data(Dyke)
}
\format{
A 5-dimensional array resulting from cross-tabulating 5 variables for 1729 observations. The variable names and their levels are:
\tabular{rll}{
No \tab Name \tab Levels \cr
1\tab \code{Knowledge}\tab \code{"Good", "Poor"}\cr
2\tab \code{Reading}\tab \code{"No", "Yes"}\cr
3\tab \code{Radio}\tab \code{"No", "Yes"}\cr
4\tab \code{Lectures}\tab \code{"No", "Yes"}\cr
5\tab \code{Newspaper}\tab \code{"No", "Yes"}\cr
}
}
%\details{ }
\source{
% \cite{Fienberg:80 [Table 5-6]}
Fienberg, S. E. (1980).
\emph{The Analysis of Cross-Classified Categorical Data}
Cambridge, MA: MIT Press, p. 85, Table 5-6.
}
\references{
Dyke, G. V. and Patterson, H. D. (1952).
Analysis of factorial arrangements when the data are proportions.
\emph{Biometrics}, 8, 1-12.
Lindsey, J. K. (1993).
\emph{Models for Repeated Measurements}
Oxford, UK: Oxford University Press, p. 57.
}
%\seealso{ }
\examples{
data(Dyke)
# independence model
mosaic(Dyke, shade=TRUE)
# null model, Knowledge as response, independent of others
require(MASS)
dyke.mod0 <- loglm(~ Knowledge + (Reading * Radio * Lectures * Newspaper), data=Dyke)
dyke.mod0
mosaic(dyke.mod0)
# view as doubledecker plot
Dyke <- Dyke[2:1,,,,] # make Good the highlighted value of Knowledge
doubledecker(Knowledge ~ ., data=Dyke)
# better version, with some options
doubledecker(Knowledge ~ Lectures + Reading + Newspaper + Radio, data=Dyke,
margins = c(1,6, length(dim(Dyke)) + 1, 1),
fill_boxes=list(rep(c("white", gray(.90)),4))
)
# separate (conditional) plots for those who attend lectures and those who do not
doubledecker(Knowledge ~ Reading + Newspaper + Radio, data=Dyke[,,,1,],
main="Do not attend lectures",
margins = c(1,6, length(dim(Dyke)) + 1, 1),
fill_boxes=list(rep(c("white", gray(.90)),3))
)
doubledecker(Knowledge ~ Reading + Newspaper + Radio, data=Dyke[,,,2,],
main="Attend lectures",
margins = c(1,6, length(dim(Dyke)) + 1, 1),
fill_boxes=list(rep(c("white", gray(.90)),3))
)
drop1(dyke.mod0, test="Chisq")
}
\keyword{datasets}
vcdExtra/man/vcdExtra-deprecated.Rd 0000644 0001762 0000144 00000001260 13164013351 016746 0 ustar ligges users \name{vcdExtra-deprecated}
\alias{vcdExtra-deprecated}
\alias{summarise}
%\alias{summarise.glm}
%\alias{summarise.glmlist}
%\alias{summarise.loglm}
%\alias{summarise.loglmlist}
%
\title{Deprecated Functions in vcdExtra Package}
%
\description{
These functions are provided for compatibility with older versions of the \pkg{vcdExtra} package only.
They are replaced by \code{\link{LRstats}}.
}
%
\usage{
summarise(...)
%summarise.glm(...)
%summarise.glmlist(...)
%summarise.loglm(...)
%summarise.loglmlist(...)
}
%
\arguments{
\item{\dots}{pass arguments down.}
}
%
\details{
\code{summarise.*} have been replaced by \code{\link{LRstats}} functions.
}
vcdExtra/man/Mental.Rd 0000644 0001762 0000144 00000003174 14202243352 014316 0 ustar ligges users \name{Mental}
\Rdversion{1.1}
\alias{Mental}
\docType{data}
\title{ Mental impairment and parents SES}
\description{
A 6 x 4 contingency table representing the cross-classification of
mental health status (\code{mental}) of 1660 young New York residents by
their parents' socioeconomic status (\code{ses}).
}
\usage{data(Mental)}
\format{
A data frame frequency table with 24 observations on the following 3 variables.
\describe{
\item{\code{ses}}{an ordered factor with levels \code{1} < \code{2} < \code{3} < \code{4} < \code{5} < \code{6}}
\item{\code{mental}}{an ordered factor with levels \code{Well} < \code{Mild} < \code{Moderate} < \code{Impaired}}
\item{\code{Freq}}{cell frequency: a numeric vector}
}
}
\details{
Both \code{ses} and \code{mental} can be treated as ordered factors or
integer scores. For \code{ses}, 1="High" and 6="Low".
}
\source{
Haberman, S. J.
\emph{The Analysis of Qualitative Data: New Developments}, Academic Press, 1979, Vol. II,
p. 375.
Srole, L.; Langner, T. S.; Michael, S. T.; Kirkpatrick, P.; Opler, M. K. & Rennie, T. A. C.
\emph{Mental Health in the Metropolis: The Midtown Manhattan Study},
NYU Press, 1978, p. 289
}
\references{
Friendly, M. \emph{Visualizing Categorical Data}, Cary, NC: SAS Institute, 2000, Appendix B.7.
}
\examples{
data(Mental)
str(Mental)
(Mental.tab <- xtabs(Freq ~ ses+mental, data=Mental))
# mosaic and sieve plots
mosaic(Mental.tab, gp=shading_Friendly)
sieve(Mental.tab, gp=shading_Friendly)
library(ca)
plot(ca(Mental.tab), main="Mental impairment & SES")
title(xlab="Dim 1", ylab="Dim 2")
}
\keyword{datasets}
vcdExtra/man/TV.Rd 0000644 0001762 0000144 00000005015 13164013351 013423 0 ustar ligges users \name{TV}
\Rdversion{1.1}
\alias{TV}
\title{TV Viewing Data}
\description{
This data set \code{TV} comprises a 5 x 11 x 3 contingency table based on
audience viewing data from Neilsen Media Research for the week starting
November 6, 1995.
}
\usage{data(TV)}
\format{
A 5 x 11 x 3 array of cell frequencies with the following structure:
\preformatted{
int [1:5, 1:11, 1:3] 146 244 233 174 294 151 181 161 183 281 ...
- attr(*, "dimnames")=List of 3
..$ Day : chr [1:5] "Monday" "Tuesday" "Wednesday" "Thursday" ...
..$ Time : chr [1:11] "8:00" "8:15" "8:30" "8:45" ...
..$ Network: chr [1:3] "ABC" "CBS" "NBC"
}
}
\details{
The original data, \code{tv.dat}, contains two additional networks: "Fox" and "Other", with small
frequencies. These levels were removed in the current version.
There is also a fourth factor, transition State
transition (turn the television Off, Switch channels, or Persist in viewing the current channel).
The \code{TV} data here includes only the Persist observations.
}
\source{
The original data, \code{tv.dat}, came from the initial implementation of
mosaic displays in R by Jay Emerson (1998). Similar data had been used by
Hartigan and Kleiner (1984) as an illustration.
}
\references{
Friendly, M. and Meyer, D. (2016).
\emph{Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data}.
Boca Raton, FL: Chapman & Hall/CRC. \url{http://ddar.datavis.ca}.
Emerson, John W. Mosaic Displays in S-PLUS: A General Implementation and a Case Study.
\emph{Statistical Graphics and Computing Newsletter}, 1998, 9(1), 17--23,
\url{http://www.stat.yale.edu/~jay/R/mosaic/v91.pdf}
Hartigan, J. A. & Kleiner, B. A Mosaic of Television Ratings. \emph{The American Statistician}, 1984, 38, 32-35.
}
\examples{
data(TV)
structable(TV)
doubledecker(TV)
# reduce number of levels of Time
TV.df <- as.data.frame.table(TV)
levels(TV.df$Time) <- rep(c("8:00-8:59", "9:00-9:59", "10:00-10:44"), c(4, 4, 3))
TV2 <- xtabs(Freq ~ Day + Time + Network, TV.df)
# re-label for mosaic display
levels(TV.df$Time) <- c("8", "9", "10")
# fit mode of joint independence, showing association of Network with Day*Time
mosaic(~ Day + Network + Time, data = TV.df, expected = ~ Day:Time + Network, legend = FALSE)
# with doubledecker arrangement
mosaic(~ Day + Network + Time, data = TV.df, expected = ~ Day:Time + Network,
split = c(TRUE, TRUE, FALSE), spacing = spacing_highlighting, legend = FALSE)
}
\keyword{datasets}
vcdExtra/man/GSS.Rd 0000644 0001762 0000144 00000001745 13164013351 013534 0 ustar ligges users \name{GSS}
\alias{GSS}
\docType{data}
\title{General Social Survey-- Sex and Party affiliation}
\description{
Data from the General Social Survey, 1991, on the relation between
sex and party affiliation.
}
\usage{data(GSS)}
\format{
A data frame in frequency form with 6 observations on the following 3 variables.
\describe{
\item{\code{sex}}{a factor with levels \code{female} \code{male}}
\item{\code{party}}{a factor with levels \code{dem} \code{indep} \code{rep}}
\item{\code{count}}{a numeric vector}
}
}
%\details{
% ~~ If necessary, more details than the __description__ above ~~
%}
\source{
Agresti, A. Categorical Data Analysis John Wiley & Sons, 2002,
Table 3.11, p. 106.
}
%\references{
% ~~ possibly secondary sources and usages ~~
%}
\examples{
data(GSS)
## maybe str(GSS) ; plot(GSS) ...
(GSStab <- xtabs(count ~ sex + party, data=GSS))
mod.glm <- glm(count ~ sex + party, family = poisson, data = GSS)
}
\keyword{datasets}
vcdExtra/man/LRstats.Rd 0000644 0001762 0000144 00000006257 13164013351 014477 0 ustar ligges users \name{LRstats}
\alias{LRstats}
\alias{LRstats.glmlist}
\alias{LRstats.loglmlist}
\alias{LRstats.default}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Brief Summary of Model Fit for glm and loglm Models
}
\description{
For \code{glm} objects, the \code{print} and \code{summary} methods give
too much information if all one wants to see is a brief summary of model
goodness of fit, and there is no easy way to display a compact comparison of
model goodness of fit for a collection of models fit to the same data.
All \code{loglm} models have equivalent glm forms, but the
\code{print} and \code{summary} methods give quite different results.
\code{LRstats} provides a brief summary for one or more models
fit to the same dataset
for which \code{logLik} and \code{nobs} methods exist
(e.g., \code{glm} and \code{loglm} models).
%This implementation is experimental, and is subject to change.
}
\usage{
LRstats(object, ...)
\method{LRstats}{glmlist}(object, ..., saturated = NULL, sortby = NULL)
\method{LRstats}{loglmlist}(object, ..., saturated = NULL, sortby = NULL)
\method{LRstats}{default}(object, ..., saturated = NULL, sortby = NULL)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{object}{
a fitted model object for which there exists a logLik method to extract the corresponding log-likelihood}
\item{\dots}{
optionally more fitted model objects
}
\item{saturated}{
saturated model log likelihood reference value (use 0 if deviance is not available)
}
\item{sortby}{
either a numeric or character string specifying
the column in the result by which the rows are sorted (in decreasing order)}
}
\details{
The function relies on residual degrees of freedom for the LR chisq test being available
in the model object. This is true for objects inheriting from
\code{lm}, \code{glm}, \code{loglm}, \code{polr}
and \code{negbin}.
}
\value{
A data frame (also of class \code{anova}) with columns
\code{c("AIC", "BIC", "LR Chisq", "Df", "Pr(>Chisq)")}.
Row names are taken from the names of the model object(s).
}
%\references{
%% ~put references to the literature/web site here ~
%}
\author{
Achim Zeileis
}
%\note{
%% ~~further notes~~
%}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
\code{\link[stats]{logLik}}, \code{\link[stats]{glm}}, \code{\link[MASS]{loglm}},
\code{\link{logLik.loglm}}, \code{\link{modFit}}
}
\examples{
data(Mental)
indep <- glm(Freq ~ mental+ses,
family = poisson, data = Mental)
LRstats(indep)
Cscore <- as.numeric(Mental$ses)
Rscore <- as.numeric(Mental$mental)
coleff <- glm(Freq ~ mental + ses + Rscore:ses,
family = poisson, data = Mental)
roweff <- glm(Freq ~ mental + ses + mental:Cscore,
family = poisson, data = Mental)
linlin <- glm(Freq ~ mental + ses + Rscore:Cscore,
family = poisson, data = Mental)
# compare models
LRstats(indep, coleff, roweff, linlin)
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{models}
%\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
vcdExtra/man/mosaic.glm.Rd 0000644 0001762 0000144 00000020065 14202260745 015132 0 ustar ligges users \name{mosaic.glm}
\alias{mosaic.glm}
\alias{sieve.glm}
\alias{assoc.glm}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{Mosaic plots for fitted generalized linear and generalized nonlinear models }
\description{
Produces mosaic plots (and other plots in the \code{\link[vcd]{strucplot}} framework)
for a log-linear model fitted with \code{\link[stats]{glm}}
or for a generalized nonlinear model fitted with \code{\link[gnm]{gnm}}.
These methods extend the range of strucplot
visualizations well beyond the models that can
be fit with \code{\link[MASS]{loglm}}.
They are intended for models for counts using the Poisson family (or quasi-poisson),
but should be sensible as long as (a) the response variable is non-negative and (b) the
predictors visualized in the \code{strucplot} are discrete factors.
}
\usage{
\method{mosaic}{glm}(x, formula = NULL, panel = mosaic,
type = c("observed", "expected"),
residuals = NULL,
residuals_type = c("pearson", "deviance", "rstandard"),
gp = shading_hcl, gp_args = list(), ...)
\method{sieve}{glm}(x, ...)
\method{assoc}{glm}(x, ...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{x}{ A \code{glm} or \code{gnm} object. The response variable, typically a
cell frequency, should be non-negative. }
\item{formula}{ A one-sided formula with the indexing factors of the plot
separated by '+', determining the order in which the variables are used in the mosaic.
A formula must be provided unless \code{x$data}
inherits from class \code{"table"} -- in which case the indexing
factors of this table are used, or the factors in \code{x$data}
(or model.frame(x) if \code{x$data} is an environment) exactly
cross-classify the data -- in which case this set of
cross-classifying factors are used. }
\item{panel}{Panel function used to draw the plot for visualizing the observed values, residuals
and expected values. Currently, one of \code{"mosaic"}, \code{"assoc"}, or \code{"sieve"}
in \code{vcd}.}
\item{type}{A character string indicating whether the
\code{"observed"} or the \code{"expected"} values of the table should be visualized
by the area of the tiles or bars.}
\item{residuals}{ An optional array or vector of residuals corresponding to the cells in the
data, for example, as calculated by \code{residuals.glm(x)}, \code{residuals.gnm(x)}.}
\item{residuals_type}{If the \code{residuals} argument is \code{NULL}, residuals are calculated internally
and used in the display. In this case, \code{residual_type} can be \code{"pearson"},
\code{"deviance"} or \code{"rstandard"}. Otherwise (when \code{residuals} is supplied),
\code{residuals_type} is used as a label for the legend in the
plot.
}
\item{gp}{Object of class \code{"gpar"}, shading function or a corresponding generating function
(see \code{\link[vcd]{strucplot}} Details and \code{\link[vcd]{shadings}}).
Ignored if shade = FALSE.}
\item{gp_args}{A list of arguments for the shading-generating function, if specified.}
\item{\dots}{ Other arguments passed to the \code{panel} function e.g., \code{\link[vcd]{mosaic}} }
}
\details{
For both poisson family generalized linear models and loglinear models, standardized residuals
provided by \code{rstandard}
(sometimes called adjusted residuals) are often preferred because they have
constant unit asymptotic variance.
The \code{sieve} and \code{assoc} methods are simple convenience interfaces to this plot method, setting the panel argument accordingly.
}
%\note{
%In the current version, the \code{glm} or \code{gnm} object \emph{must} have been fit using
%the \code{data} argument to supply a data.frame or table, rather than with variables
%in the global environment.
%}
\value{
The \code{structable} visualized by \code{\link[vcd]{strucplot}} is returned invisibly.
}
%\references{ ~put references to the literature/web site here ~ }
\author{Heather Turner, Michael Friendly, with help from Achim Zeileis}
%\note{
%}
\seealso{
\code{\link[stats]{glm}}, \code{\link[gnm]{gnm}}, \code{\link[vcd]{plot.loglm}}, \code{\link[vcd]{mosaic}}
}
\examples{
GSStab <- xtabs(count ~ sex + party, data=GSS)
# using the data in table form
mod.glm1 <- glm(Freq ~ sex + party, family = poisson, data = GSStab)
res <- residuals(mod.glm1)
std <- rstandard(mod.glm1)
# For mosaic.default(), need to re-shape residuals to conform to data
stdtab <- array(std, dim=dim(GSStab), dimnames=dimnames(GSStab))
mosaic(GSStab, gp=shading_Friendly, residuals=stdtab, residuals_type="Std\nresiduals",
labeling = labeling_residuals)
# Using externally calculated residuals with the glm() object
mosaic.glm(mod.glm1, residuals=std, labeling = labeling_residuals, shade=TRUE)
# Using residuals_type
mosaic.glm(mod.glm1, residuals_type="rstandard", labeling = labeling_residuals, shade=TRUE)
## Ordinal factors and structured associations
data(Mental)
xtabs(Freq ~ mental+ses, data=Mental)
long.labels <- list(set_varnames = c(mental="Mental Health Status", ses="Parent SES"))
# fit independence model
# Residual deviance: 47.418 on 15 degrees of freedom
indep <- glm(Freq ~ mental+ses,
family = poisson, data = Mental)
long.labels <- list(set_varnames = c(mental="Mental Health Status",
ses="Parent SES"))
mosaic(indep,residuals_type="rstandard", labeling_args = long.labels, labeling=labeling_residuals)
# or, show as a sieve diagram
mosaic(indep, labeling_args = long.labels, panel=sieve, gp=shading_Friendly)
# fit linear x linear (uniform) association. Use integer scores for rows/cols
Cscore <- as.numeric(Mental$ses)
Rscore <- as.numeric(Mental$mental)
linlin <- glm(Freq ~ mental + ses + Rscore:Cscore,
family = poisson, data = Mental)
mosaic(linlin,residuals_type="rstandard",
labeling_args = long.labels, labeling=labeling_residuals, suppress=1, gp=shading_Friendly,
main="Lin x Lin model")
## Goodman Row-Column association model fits even better (deviance 3.57, df 8)
if (require(gnm)) {
Mental$mental <- C(Mental$mental, treatment)
Mental$ses <- C(Mental$ses, treatment)
RC1model <- gnm(Freq ~ ses + mental + Mult(ses, mental),
family = poisson, data = Mental)
mosaic(RC1model,residuals_type="rstandard",
labeling_args = long.labels, labeling=labeling_residuals, suppress=1, gp=shading_Friendly,
main="RC1 model")
}
############# UCB Admissions data, fit using glm()
structable(Dept ~ Admit+Gender,UCBAdmissions)
berkeley <- as.data.frame(UCBAdmissions)
berk.glm1 <- glm(Freq ~ Dept * (Gender+Admit), data=berkeley, family="poisson")
summary(berk.glm1)
mosaic(berk.glm1, gp=shading_Friendly, labeling=labeling_residuals, formula=~Admit+Dept+Gender)
# the same, displaying studentized residuals; note use of formula to reorder factors in the mosaic
mosaic(berk.glm1, residuals_type="rstandard", labeling=labeling_residuals, shade=TRUE,
formula=~Admit+Dept+Gender, main="Model: [DeptGender][DeptAdmit]")
## all two-way model
berk.glm2 <- glm(Freq ~ (Dept + Gender + Admit)^2, data=berkeley, family="poisson")
summary(berk.glm2)
mosaic.glm(berk.glm2, residuals_type="rstandard", labeling = labeling_residuals, shade=TRUE,
formula=~Admit+Dept+Gender, main="Model: [DeptGender][DeptAdmit][AdmitGender]")
anova(berk.glm1, berk.glm2, test="Chisq")
# Add 1 df term for association of [GenderAdmit] only in Dept A
berkeley <- within(berkeley, dept1AG <- (Dept=='A')*(Gender=='Female')*(Admit=='Admitted'))
berkeley[1:6,]
berk.glm3 <- glm(Freq ~ Dept * (Gender+Admit) + dept1AG, data=berkeley, family="poisson")
summary(berk.glm3)
mosaic.glm(berk.glm3, residuals_type="rstandard", labeling = labeling_residuals, shade=TRUE,
formula=~Admit+Dept+Gender, main="Model: [DeptGender][DeptAdmit] + DeptA*[GA]")
anova(berk.glm1, berk.glm3, test="Chisq")
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{hplot}
\keyword{models}
\keyword{multivariate}
% __ONLY ONE__ keyword per line
vcdExtra/man/CMHtest.Rd 0000644 0001762 0000144 00000020461 14202261165 014405 0 ustar ligges users \name{CMHtest}
\alias{CMHtest}
\alias{CMHtest.formula}
\alias{CMHtest.default}
\alias{Cochran Mantel Haenszel test}
\alias{print.CMHtest}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Generalized Cochran-Mantel-Haenszel Tests
}
\description{
Provides generalized Cochran-Mantel-Haenszel tests of association of two possibly ordered factors,
optionally stratified other factor(s). With strata, \code{CMHtest} calculates these tests for
each level of the stratifying variables and also provides overall tests controlling for the
strata.
For ordinal factors, more powerful tests than the test for general association
(independence) are obtained by assigning scores to the row and column categories.
}
\usage{
CMHtest(x, ...)
\method{CMHtest}{formula}(formula, data = NULL, subset = NULL, na.action = NULL, ...)
\method{CMHtest}{default}(x, strata = NULL,
rscores = 1:R, cscores = 1:C,
types = c("cor", "rmeans", "cmeans", "general"),
overall=FALSE, details=overall, ...)
\method{print}{CMHtest}(x, digits = max(getOption("digits") - 2, 3), ...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{x}{
A 2+ way contingency table in array form, or a class \code{"table"} object
with optional category labels specified in the dimnames(x) attribute.
}
\item{formula}{a formula specifying the variables used to create a contingency table from \code{data}.
This should be a one-sided formula when \code{data} is in array form, and a two-sided formula
with a response \code{Freq} if \code{data} is a data frame with a cell frequency variable.
For convenience, conditioning formulas can be specified indicating strata.
}
\item{data}{either a data frame, or an object of class \code{"table"} or \code{"ftable"}.
}
\item{subset}{an optional vector specifying a subset of observations to be used.
}
\item{na.action}{a function which indicates what should happen when the data contain \code{NA}s.
Ignored if \code{data} is a contingency table
}
\item{strata}{
For a 3- or higher-way table, the names or numbers of the factors
to be treated as strata. By default, the first 2 factors are treated
as the main table variables, and all others considered stratifying factors.
}
\item{rscores}{
Row scores. Either a set of numbers (typically integers, \code{1:R})
or the string \code{"midrank"} for standardized midrank scores,
or \code{NULL} to exclude tests that depend on row scores.
}
\item{cscores}{
Column scores. Same as for row scores.
}
\item{types}{
Types of CMH tests to compute: Any one or more of
\code{c("cor", "cmeans", "rmeans", "general")},
or \code{"ALL"} for all of these.
}
\item{overall}{
logical. Whether to calculate overall tests, controlling for
the stratifying factors.
}
\item{details}{
logical. Whether to include computational details in the result
}
\item{\dots}{
Other arguments passed to default method.
}
\item{digits}{
Digits to print.
}
}
\details{
The standard \eqn{\chi^2} tests for association in a two-way table
treat both table factors as nominal (unordered) categories.
When one or both factors of a two-way table are
quantitative or ordinal, more powerful tests of association
may be obtained by taking ordinality into account using
row and or column scores to test for linear trends or differences
in row or column means.
The CMH analysis for a two-way table produces generalized Cochran-Mantel-Haenszel statistics
(Landis etal., 1978).
These include the CMH \bold{correlation} statistic (\code{"cor"}),
treating both factors as ordered.
For a given statum, with equally spaced row and column scores,
this CMH statistic reduces to \eqn{(n-1) r^2},
where \eqn{r} is the Pearson correlation between X and Y.
With \code{"midrank"} scores, this CMH statistic is analogous
to \eqn{(n-1) r_S^2}, using the Spearman rank correlation.
The \bold{ANOVA} (row mean scores and column mean scores) statistics,
treat the columns and rows respectively as ordinal,
and are sensitive to mean shifts over columns or rows.
These are transforms of the \eqn{F} statistics from one-way ANOVAs
with equally spaced scores and to Kruskal-Wallis tests with
\code{"midrank"} scores.
The CMH \bold{general} association statistic treat both factors as unordered,
and give a test closely related to the Pearson \eqn{\chi^2} test.
When there is more than one stratum, the overall general CMH statistic
gives a stratum-adjusted Pearson \eqn{\chi^2},
equivalent to what is calculated by \code{\link[stats]{mantelhaen.test}}.
For a 3+ way table, one table of CMH tests is produced for each
combination of the factors identified as \code{strata}.
If \code{overall=TRUE}, an additional table is calculated for
the same two primary variables, controlling for (pooling over)
the \code{strata} variables.
These overall tests implicitly assume no interactions between the
primary variables and the strata and they will have low power in the
presence of interactions.
}
\value{
An object of class \code{"CMHtest"} , a list with the following 4 components:
\item{table}{A matrix containing the test statistics, with columns
\code{Chisq}, \code{Df} and \code{Prob} }
\item{names}{The names of the table row and column variables}
\item{rscore}{Row scores}
\item{cscore}{Column scores}
If \code{details==TRUE}, additional components are included.
If there are strata, the result is a list of \code{"CMHtest"} objects.
If \code{overall=TRUE} another component, labeled \code{ALL}
is appended to the list.
}
\references{
Stokes, M. E. & Davis, C. S. & Koch, G., (2000).
\emph{Categorical Data Analysis using the SAS System}, 2nd Ed.,
Cary, NC: SAS Institute,
pp 74-75, 92-101, 124-129.
Details of the computation are given at:
\url{http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_freq_a0000000648.htm
}
Cochran, W. G. (1954), Some Methods for Strengthening the Common \eqn{\chi^2} Tests,
\emph{Biometrics}, 10, 417-451.
Landis, R. J., Heyman, E. R., and Koch, G. G. (1978).
Average Partial Association in Three-way Contingency Tables:
A Review and Discussion of Alternative Tests,
\emph{International Statistical Review}, \bold{46}, 237-254.
Mantel, N. (1963), Chi-square Tests with One Degree of Freedom: Extensions of the Mantel-Haenszel Procedure,"
\emph{Journal of the American Statistical Association}, 58, 690-700.
}
\author{
Michael Friendly
}
%\note{
%%% ~~further notes~~
%}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
\code{\link[coin]{cmh_test}} provides the CMH test of general association;
\code{\link[coin]{lbl_test}} provides the CMH correlation test of linear by linear association.
\code{\link[stats]{mantelhaen.test}} provides the overall general
Cochran-Mantel-Haenszel chi-squared test of the null that two nominal variables are conditionally independent
in each stratum, assuming that there is no three-way interaction
}
\examples{
data(JobSat, package="vcdExtra")
CMHtest(JobSat)
CMHtest(JobSat, rscores="midrank", cscores="midrank")
# formula interface
CMHtest(~ ., data=JobSat)
# A 3-way table (both factors ordinal)
data(MSPatients, package="vcd")
CMHtest(MSPatients)
# also calculate overall tests, controlling for Patient
CMHtest(MSPatients, overall=TRUE)
# compare with mantelhaen.test
mantelhaen.test(MSPatients)
# formula interface
CMHtest(~ ., data=MSPatients, overall=TRUE)
# using a frequency data.frame
CMHtest(xtabs(Freq~ses+mental, data=Mental))
# or, more simply
CMHtest(Freq~ses+mental, data=Mental)
# conditioning formulae
CMHtest(Freq~right+left|gender, data=VisualAcuity)
CMHtest(Freq ~ attitude+memory|education+age, data=Punishment)
# Stokes etal, Table 5.1, p 92: two unordered factors
parties <- matrix(
c(221, 160, 360, 140,
200, 291, 160, 311,
208, 106, 316, 97), nrow=3, ncol=4, byrow=TRUE)
dimnames(parties) <- list(party=c("Dem", "Indep", "Rep"),
neighborhood=c("Bayside", "Highland", "Longview", "Sheffield"))
CMHtest(parties, rscores=NULL, cscores=NULL)
# compare with Pearson chisquare
chisq.test(parties)
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{htest}
%\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
vcdExtra/man/Titanicp.Rd 0000644 0001762 0000144 00000004026 13771414014 014653 0 ustar ligges users \name{Titanicp}
\alias{Titanicp}
\docType{data}
\title{
Passengers on the Titanic
}
\description{
Data on passengers on the RMS Titanic, excluding the Crew and some
individual identifier variables.
}
\usage{data(Titanicp)}
\format{
A data frame with 1309 observations on the following 6 variables.
\describe{
\item{\code{pclass}}{a factor with levels \code{1st} \code{2nd} \code{3rd}}
\item{\code{survived}}{a factor with levels \code{died} \code{survived}}
\item{\code{sex}}{a factor with levels \code{female} \code{male}}
\item{\code{age}}{passenger age in years (or fractions of a year, for children), a numeric vector; age is missing for 263 of the passengers}
\item{\code{sibsp}}{number of siblings or spouses aboard, integer: \code{0:8}}
\item{\code{parch}}{number of parents or children aboard, integer: \code{0:6}}
}
}
\details{
There are a number of related versions of the Titanic data, in various formats.
This version was derived from \code{ptitanic} in the \pkg{rpart.plot} package,
modifying it to remove the \code{Class 'labelled'} attributes for some variables
(inherited from Frank Harrell's \code{titanic3} version)
which caused problems with some applications, notably \code{ggplot2}.
Other versions:
\code{\link[datasets]{Titanic}} is the 4-way frequency table of all 2201 people
aboard the Titanic, including passengers and crew.
}
\source{
The original R source for this
dataset was compiled by Frank Harrell and Robert Dawson:
\url{https://biostat.app.vumc.org/wiki/pub/Main/DataSets/titanic.html},
described in more detail in
\url{https://biostat.app.vumc.org/wiki/pub/Main/DataSets/titanic3info.txt}
For this version of the Titanic data, passenger details were deleted,
survived was cast as a factor, and the name changed to \code{Titanicp} to minimize confusion
with other versions.
}
%\references{
%%% ~~ possibly secondary sources and usages ~~
%}
\examples{
data(Titanicp)
## maybe str(Titanicp) ; plot(Titanicp) ...
}
\keyword{datasets}
vcdExtra/man/Cancer.Rd 0000644 0001762 0000144 00000002116 13164013351 014264 0 ustar ligges users \name{Cancer}
\Rdversion{1.1}
\alias{Cancer}
\docType{data}
\title{Survival of Breast Cancer Patients}
\description{Three year survival of 474 breast cancer patients according to nuclear grade and diagnostic center.}
\usage{
data(Cancer)
}
\format{
A 3-dimensional array resulting from cross-tabulating 3 variables for 474 observations. The variable names and their levels are:
\tabular{rll}{
No \tab Name \tab Levels \cr
1\tab \code{Survival}\tab \code{"Died", "Surv"}\cr
2\tab \code{Grade}\tab \code{"Malignant", "Benign"}\cr
3\tab \code{Center}\tab \code{"Boston", "Glamorgan"}\cr
}
}
%\details { }
\source{
% \cite{Lindsey:95 [p38]}
% \cite{Whittaker:90}
Lindsey, J. K. (1995).
Analysis of Frequency and Count Data
Oxford, UK: Oxford University Press. p. 38, Table 2.5.
Whittaker, J. (1990)
Graphical Models in Applied Multivariate Statistics
New York: John Wiley and Sons, p. 220.
}
%\references{
% \cite{Morrison etal}
%}
%\seealso { }
\examples{
data(Cancer)
# example goes here
}
\keyword{datasets}
vcdExtra/man/datasets.Rd 0000644 0001762 0000144 00000004242 13164013351 014703 0 ustar ligges users \name{datasets}
\alias{datasets}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Information on Data Sets in Packages
}
\description{
The \code{\link[utils]{data}} function is used both to load data sets from packages,
and give a display of the names and titles of data sets in one or more packages,
however it does not return a result that can be easily used to get additional
information about the nature of data sets in packages.
The \code{datasets()} function is designed to produce a more useful summary
display of data sets in one or more packages. It extracts the \code{class}
and dimension information (\code{dim} or code{length}) of each item,
and formats these to provide additional descriptors.
}
\usage{
datasets(package, allClass=FALSE,
incPackage=length(package) > 1,
maxTitle=NULL)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{package}{
a character vector giving the package(s) to look in
}
\item{allClass}{
include all classes of the item (\code{TRUE}) or just the last class (\code{FALSE})?
}
\item{incPackage}{
include the package name in result?
}
\item{maxTitle}{
maximum length of data set Title
}
}
\details{
The requested packages must be installed, and are silently loaded in order
to extract \code{class} and size information.
}
\value{
A \code{data.frame} whose rows correspond to data sets found in \code{package}.
The columns (for a single package) are:
\item{Item}{data set name}
\item{class}{class}
\item{dim}{an abbreviation of the dimensions of the data set}
\item{Title}{data set title}
}
%\references{
%%% ~put references to the literature/web site here ~
%}
\author{
Michael Friendly, with R-help from Curt Seeliger
}
%\note{
%%% ~~further notes~~
%}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
\code{\link[utils]{data}},
}
\examples{
datasets("vcdExtra")
datasets(c("vcd", "vcdExtra"))
datasets("datasets", maxTitle=50)
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{package}
\keyword{data}% __ONLY ONE__ keyword per line
vcdExtra/man/zero.test.Rd 0000644 0001762 0000144 00000004720 14202266761 015042 0 ustar ligges users \name{zero.test}
\alias{zero.test}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Score test for zero inflation in Poisson data
}
\description{
Carries out a simple score test (van den Broek, 1995) for excess zeros in
an otherwise Poisson distribution of counts. It gives a \eqn{\chi^2_1}
statistic on one degree of freedom.
}
\usage{
zero.test(x)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{x}{
A vector of non-negative counts, or a one-way frequency table
of such counts.
}
}
\details{
The test first calculates the rate estimate from the mean,
\eqn{\hat{\lambda} = \bar{x}}.
The number of observed zeros, \eqn{n_0} is then compared with the expected
number, \eqn{n \hat{p_0}}, where \eqn{\hat{p}_0=\exp[-\hat{\lambda}]}.
Then the test statistic is calculated by the formula:
\deqn{\frac{(n_0 - n\hat{p}_0)^2}{n\hat{p}_0(1-\hat{p}_0) - n\bar{x}\hat{p}_0^2}}
This test statistic
has a \eqn{\chi^2_1} distribution.
}
\value{
Returns invisibly a list of three elements:
\item{\code{statistic}}{Description of 'comp1'}
\item{\code{df}}{Description of 'comp2'}
\item{\code{pvalue}}{Upper tail p-value}
}
\references{
The original R code came from a Stackexchange question,
\url{https://stats.stackexchange.com/questions/118322/how-to-test-for-zero-inflation-in-a-dataset}
Van den Broek, J. (1995).
A Score Test for Zero Inflation in a Poisson Distribution.
\emph{Biometrics}, \bold{51}(2), 738-743. https://www.jstor.org/stable/2532959
Yang, Zhao, James W. Hardin, and Cheryl L. Addy (2010).
Score Tests for Zero-Inflation in Overdispersed Count Data.
\emph{Communications in Statistics - Theory and Methods} \bold{39} (11) 2008-2030.
\doi{10.1080/03610920902948228}
}
\author{
Michael Friendly
}
%\note{
%%% ~~further notes~~
%}
%% ~Make other sections like Warning with \section{Warning }{....} ~
%\seealso{
%%% ~~objects to See Also as \code{\link{help}}, ~~~
%}
\examples{
# synthetic tests
zero.test(rpois(100, 1))
zero.test(rpois(100, 5))
# add some extra zeros
zero.test(c(rep(0, 20), rpois(100, 5)))
# Articles by Phd candidates
data(PhdPubs, package="vcdExtra")
zero.test(PhdPubs$articles)
phd.tab <- table(PhdPubs$articles)
zero.test(phd.tab)
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{htest}% use one of RShowDoc("KEYWORDS")
%\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
vcdExtra/man/Hauser79.Rd 0000644 0001762 0000144 00000007376 13164013351 014515 0 ustar ligges users \name{Hauser79}
\alias{Hauser79}
\docType{data}
\title{
Hauser (1979) Data on Social Mobility
}
\description{
Hauser (1979) presented this two-way frequency table,
cross-classifying occupational categories of sons and fathers
in the United States.
}
\usage{data(Hauser79)}
\format{
A frequency data frame with 25 observations on the following 3 variables, representing the
cross-classification of 19912 individuals by father's occupation and son's first occupation.
\describe{
\item{\code{Son}}{a factor with levels \code{UpNM} \code{LoNM} \code{UpM} \code{LoM} \code{Farm}}
\item{\code{Father}}{a factor with levels \code{UpNM} \code{LoNM} \code{UpM} \code{LoM} \code{Farm}}
\item{\code{Freq}}{a numeric vector}
}
}
%\details{
%%% ~~ If necessary, more details than the __description__ above ~~
%}
\source{
R.M. Hauser (1979), Some exploratory methods for modeling mobility tables and other cross-classified data.
In: K.F. Schuessler (Ed.), \emph{Sociological Methodology}, 1980, Jossey-Bass, San Francisco, pp. 413-458.
}
\references{
Powers, D.A. and Xie, Y. (2008). \emph{Statistical Methods for Categorical Data Analysis},
Bingley, UK: Emerald.
}
\examples{
data(Hauser79)
str(Hauser79)
# display table
structable(~Father+Son, data=Hauser79)
#Examples from Powers & Xie, Table 4.15
# independence model
mosaic(Freq ~ Father + Son, data=Hauser79, shade=TRUE)
hauser.indep <- gnm(Freq ~ Father + Son, data=Hauser79, family=poisson)
mosaic(hauser.indep, ~Father+Son, main="Independence model", gp=shading_Friendly)
hauser.quasi <- update(hauser.indep, ~ . + Diag(Father,Son))
mosaic(hauser.quasi, ~Father+Son, main="Quasi-independence model", gp=shading_Friendly)
hauser.qsymm <- update(hauser.indep, ~ . + Diag(Father,Son) + Symm(Father,Son))
mosaic(hauser.qsymm, ~Father+Son, main="Quasi-symmetry model", gp=shading_Friendly)
#mosaic(hauser.qsymm, ~Father+Son, main="Quasi-symmetry model")
# numeric scores for row/column effects
Sscore <- as.numeric(Hauser79$Son)
Fscore <- as.numeric(Hauser79$Father)
# row effects model
hauser.roweff <- update(hauser.indep, ~ . + Father*Sscore)
LRstats(hauser.roweff)
# uniform association
hauser.UA <- update(hauser.indep, ~ . + Fscore*Sscore)
LRstats(hauser.UA)
# uniform association, omitting diagonals
hauser.UAdiag <- update(hauser.indep, ~ . + Fscore*Sscore + Diag(Father,Son))
LRstats(hauser.UAdiag)
# Levels for Hauser 5-level model
levels <- matrix(c(
2, 4, 5, 5, 5,
3, 4, 5, 5, 5,
5, 5, 5, 5, 5,
5, 5, 5, 4, 4,
5, 5, 5, 4, 1
), 5, 5, byrow=TRUE)
hauser.topo <- update(hauser.indep, ~ . + Topo(Father, Son, spec=levels))
mosaic(hauser.topo, ~Father+Son, main="Topological model", gp=shading_Friendly)
hauser.RC <- update(hauser.indep, ~ . + Mult(Father, Son), verbose=FALSE)
mosaic(hauser.RC, ~Father+Son, main="RC model", gp=shading_Friendly)
LRstats(hauser.RC)
# crossings models
hauser.CR <- update(hauser.indep, ~ . + Crossings(Father,Son))
mosaic(hauser.topo, ~Father+Son, main="Crossings model", gp=shading_Friendly)
LRstats(hauser.CR)
hauser.CRdiag <- update(hauser.indep, ~ . + Crossings(Father,Son) + Diag(Father,Son))
LRstats(hauser.CRdiag)
# compare model fit statistics
modlist <- glmlist(hauser.indep, hauser.roweff, hauser.UA, hauser.UAdiag,
hauser.quasi, hauser.qsymm, hauser.topo,
hauser.RC, hauser.CR, hauser.CRdiag)
sumry <- LRstats(modlist)
sumry[order(sumry$AIC, decreasing=TRUE),]
# or, more simply
LRstats(modlist, sortby="AIC")
mods <- substring(rownames(sumry),8)
with(sumry,
{plot(Df, AIC, cex=1.3, pch=19, xlab='Degrees of freedom', ylab='AIC')
text(Df, AIC, mods, adj=c(0.5,-.5), col='red', xpd=TRUE)
})
}
\keyword{datasets}
vcdExtra/man/Cormorants.Rd 0000644 0001762 0000144 00000006120 14202255013 015214 0 ustar ligges users \name{Cormorants}
\alias{Cormorants}
\docType{data}
\title{
Advertising Behavior by Males Cormorants
}
\description{
Male double-crested cormorants use advertising behavior to attract females for breeding.
In this study by Meagan McRae (2015), cormorants were observed two or three times a week
at six stations in a tree-nesting colony
for an entire season, April 10, 2014-July 10, 2014.
The number of advertising birds was counted and these observations were classified
by characteristics of the trees and nests.
The goal is to determine how this behavior varies temporally over the season and spatially,
as well as with characteristics of nesting sites.
}
\usage{data("Cormorants")}
\format{
A data frame with 343 observations on the following 8 variables.
\describe{
\item{\code{category}}{Time of season, divided into 3 categories based on breeding chronology, an ordered factor with levels \code{Pre} < \code{Incubation} < \code{Chicks Present}}
\item{\code{week}}{Week of the season}
\item{\code{station}}{Station of observations on two different peninsulas in a park, a factor with levels \code{B1} \code{B2} \code{C1} \code{C2} \code{C3} \code{C4}}
\item{\code{nest}}{Type of nest, an ordered factor with levels \code{no} < \code{partial} < \code{full}}
\item{\code{height}}{Relative height of bird in the tree, an ordered factor with levels \code{low} < \code{mid} < \code{high}}
\item{\code{density}}{Number of other nests in the tree, an ordered factor with levels \code{zero} < \code{few} < \code{moderate} < \code{high}}
\item{\code{tree_health}}{Health of the tree the bird is advertising in, a factor with levels \code{dead} \code{healthy}}
\item{\code{count}}{Number of birds advertising, a numeric vector}
}
}
\details{
Observations were made on only 2 days in weeks 3 and 4, but 3 days in all other weeks.
One should use log(days) as an offset, so that the response measures rate.
\code{Cormorants$days <- ifelse(Cormorants$week \%in\% 3:4, 2, 3)}
}
\source{
McRae, M. (2015).
Spatial, Habitat and Frequency Changes in Double-crested Cormorant Advertising Display in a Tree-nesting Colony.
Unpublished MA project, Environmental Studies, York University.
}
%\references{
%%% ~~ possibly secondary sources and usages ~~
%}
\examples{
data(Cormorants)
str(Cormorants)
if (require("ggplot2")) {
print(ggplot(Cormorants, aes(count)) + geom_histogram(binwidth=0.5) +
labs(x="Number of birds advertising"))
# Quick look at the data, on the log scale, for plots of `count ~ week`,
# stratified by something else.
print(ggplot(Cormorants, aes(week, count, color=height)) + geom_jitter() +
stat_smooth(method="loess", size=2) + scale_y_log10(breaks=c(1,2,5,10)) +
geom_vline(xintercept=c(4.5, 9.5)))
}
# ### models using week
fit1 <-glm(count ~ week + station + nest + height + density + tree_health, data=Cormorants,
family = poisson)
if (requireNamespace("car"))
car::Anova(fit1)
# plot fitted effects
if (requireNamespace("effects"))
plot(effects::allEffects(fit1))
}
\keyword{datasets}
vcdExtra/man/Mice.Rd 0000644 0001762 0000144 00000003035 14202242706 013751 0 ustar ligges users \name{Mice}
\alias{Mice}
\docType{data}
\title{
Mice Depletion Data
}
\description{
Data from Kastenbaum and Lamphiear (1959).
The table gives the number of depletions (deaths) in 657 litters of mice, classified by
litter size and treatment. This data set has become a classic in the analysis of
contingency tables, yet unfortunately little information on the details of the
experiment has been published.
}
\usage{data("Mice")}
\format{
A frequency data frame with 30 observations on the following 4 variables, representing a 5 x 2 x 3 contingency table.
\describe{
\item{\code{litter}}{litter size, a numeric vector}
\item{\code{treatment}}{treatment, a factor with levels \code{A} \code{B}}
\item{\code{deaths}}{number of depletions, a factor with levels \code{0} \code{1} \code{2+}}
\item{\code{Freq}}{cell frequency, a numeric vector}
}
}
%\details{
%%% ~~ If necessary, more details than the __description__ above ~~
%}
\source{
Goodman, L. A. (1983)
The analysis of dependence in cross-classifications having ordered categories, using log-linear models for frequencies and log-linear models for odds.
\emph{Biometrics}, 39, 149-160.
}
\references{
Kastenbaum, M. A. & Lamphiear, D. E. (1959)
Calculation of chi-square to calculate the no three-factor interaction hypothesis. \emph{Biometrics}, 15, 107-115.
}
\examples{
data(Mice)
# make a table
ftable(mice.tab <- xtabs(Freq ~ litter + treatment + deaths, data=Mice))
library(vcd)
mosaic(mice.tab, shade=TRUE)
}
\keyword{datasets}
vcdExtra/man/modFit.Rd 0000644 0001762 0000144 00000003733 13164013351 014321 0 ustar ligges users \name{modFit}
\Rdversion{1.1}
\alias{modFit}
\alias{modFit.loglm}
\alias{modFit.glm}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Brief Summary of Model Fit for a glm or loglm Object
}
\description{
Formats a brief summary of model fit for a \code{glm} or \code{loglm} object, showing
the likelihood ratio Chisq (df) value and or AIC. Useful for inclusion in a plot
title or annotation.
}
\usage{
modFit(x, ...)
\method{modFit}{glm}(x, stats="chisq", digits=2, ...)
\method{modFit}{loglm}(x, stats="chisq", digits=2, ...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{x}{
A \code{glm} or \code{loglm} object
}
\item{\dots}{
Arguments passed down
}
\item{stats}{
One or more of \code{chisq} or \code{aic}, determining the statistics displayed.
}
\item{digits}{
Number of digits after the decimal point in displayed statistics.
}
}
%\details{
%%% ~~ If necessary, more details than the description above ~~
%}
\value{
A character string containing the formatted values of the chosen statistics.
%% ~Describe the value returned
%% If it is a LIST, use
%% \item{comp1 }{Description of 'comp1'}
%% \item{comp2 }{Description of 'comp2'}
%% ...
}
%\references{
%%% ~put references to the literature/web site here ~
%}
\author{
Michael Friendly
}
%\note{
%%% ~~further notes~~
%}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
\code{\link{Summarise}} (soon to be deprecated),
\code{\link{LRstats}}
}
\examples{
data(Mental)
require(MASS)
(Mental.tab <- xtabs(Freq ~ ses+mental, data=Mental))
(Mental.mod <- loglm(~ses+mental, Mental.tab))
Mental.mod
modFit(Mental.mod)
# use to label mosaic()
mosaic(Mental.mod, main=paste("Independence model,", modFit(Mental.mod)))
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{utilities}
\keyword{models}% __ONLY ONE__ keyword per line
vcdExtra/man/Draft1970.Rd 0000644 0001762 0000144 00000004445 14202242540 014457 0 ustar ligges users \name{Draft1970}
\alias{Draft1970}
\docType{data}
\title{
USA 1970 Draft Lottery Data
}
\description{
This data set gives the results of the 1970 US draft lottery, in the form of
a data frame.
}
\usage{data(Draft1970)}
\format{
A data frame with 366 observations on the following 3 variables.
\describe{
\item{\code{Day}}{day of the year, 1:366}
\item{\code{Rank}}{draft priority rank of people born on that day}
\item{\code{Month}}{an ordered factor with levels \code{Jan} < \code{Feb} \dots < \code{Dec}}
}
}
\details{
The draft lottery was used to determine the order in which eligible men would
be called to the Selective Service draft.
The days of the year (including February 29) were represented by the numbers 1
through 366 written on slips of paper. The slips were placed in separate plastic
capsules that were mixed in a shoebox and then dumped into a deep glass jar.
Capsules were drawn from the jar one at a time.
The first number drawn was 258 (September 14), so all registrants with that
birthday were assigned lottery number \code{Rank} 1. The second number drawn corresponded to
April 24, and so forth. All men of draft age (born 1944 to 1950) who shared a
birthdate would be called to serve at once. The first 195 birthdates drawn were
later called to serve in the order they were drawn; the last of these was
September 24.
}
\source{
Starr, N. (1997).
Nonrandom Risk: The 1970 Draft Lottery,
\emph{Journal of Statistics Education}, v.5, n.2
\url{http://jse.amstat.org/v5n2/datasets.starr.html}
}
\references{
Fienberg, S. E. (1971),
"Randomization and Social Affairs: The 1970 Draft Lottery," \emph{Science}, 171, 255-261.
\url{https://en.wikipedia.org/wiki/Draft_lottery_(1969)}
}
\seealso{\code{\link{Draft1970table}}
}
\examples{
data(Draft1970)
# scatterplot
plot(Rank ~ Day, data=Draft1970)
with(Draft1970, lines(lowess(Day, Rank), col="red", lwd=2))
abline(lm(Rank ~ Day, data=Draft1970), col="blue")
# boxplots
plot(Rank ~ Month, data=Draft1970, col="bisque")
lm(Rank ~ Month, data=Draft1970)
anova(lm(Rank ~ Month, data=Draft1970))
# make the table version
Draft1970$Risk <- cut(Draft1970$Rank, breaks=3, labels=c("High", "Med", "Low"))
with(Draft1970, table(Month, Risk))
}
\keyword{datasets}
vcdExtra/man/mosaic.glmlist.Rd 0000644 0001762 0000144 00000012671 14202244734 016032 0 ustar ligges users \name{mosaic.glmlist}
\alias{mosaic.glmlist}
\alias{mosaic.loglmlist}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Mosaic Displays for \code{glmlist} and \code{logllmlist} Objects
}
\description{
This function provides a convenient interface for viewing mosaic displays
associated with a collection of glm models for frequency tables that have
been stored in a \code{glmlist} or \code{loglmlist} object. You can plot either selected models
individually, or mosaics for all models in an array of viewports.
}
\usage{
\method{mosaic}{glmlist}(x, selection,
panel=mosaic,
type=c("observed", "expected"),
legend=ask | !missing(selection),
main=NULL,
ask=TRUE, graphics=TRUE, rows, cols, newpage=TRUE,
...)
\method{mosaic}{loglmlist}(x, selection,
panel=mosaic,
type=c("observed", "expected"),
legend=ask | !missing(selection),
main=NULL,
ask=TRUE, graphics=TRUE, rows, cols, newpage=TRUE,
...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{x}{
a \code{glmlist} or \code{loglmlist} object
}
\item{selection}{
the index or name of one \code{glm} or \code{loglm} object in \code{x}. If no selection is specified,
a menu of models is presented or all models are plotted.
}
\item{panel}{
a \code{\link[vcd]{strucplot}} panel function, typically \code{\link[vcd]{mosaic}} or \code{\link[vcd]{sieve}}
}
\item{type}{
a character string indicating whether the \code{"observed"} or the \code{"expected"} values of the table should be visualized
}
\item{legend}{
logical: show a legend for residuals in the mosaic display(s)? The default behavior is to include a legend
when only a single plot is shown, i.e., if \code{ask} is \code{TRUE} or a \code{selection} has been
specified.
}
\item{main}{
either a logical, or a vector of character strings used for plotting the main title.
If main is a logical and \code{TRUE}, the name of the selected glm object is used.
}
\item{ask}{
logical: should the function display a menu of models, when one is not specified in \code{selection}?
If \code{selection} is not supplied and \code{ask} is \code{TRUE} (the default),
a menu of model names is presented;
if \code{ask} is \code{FALSE}, mosaics for all models are plotted in an array.
}
\item{graphics}{
logical: use a graphic dialog box when \code{ask=TRUE}?
}
\item{rows,cols}{
when \code{ask=FALSE}, the number of rows and columns in which to plot the mosaics.
}
\item{newpage}{
start a new page? (only applies to \code{ask=FALSE})
}
\item{\dots}{
other arguments passed to \code{\link{mosaic.glm}} and ultimately to \code{\link[vcd]{mosaic}}.
}
}
\details{
Most details of the plots produced can be controlled via \dots arguments
as shown in some of the examples below. In particular, with \code{panel=sieve}
you need to also pass \code{gp=shading_Friendly} to get a color version.
}
\value{
Returns the result of \code{\link{mosaic.glm}}.
%% ~Describe the value returned
%% If it is a LIST, use
%% \item{comp1 }{Description of 'comp1'}
%% \item{comp2 }{Description of 'comp2'}
%% ...
}
\references{
David Meyer, Achim Zeileis, and Kurt Hornik (2006). The Strucplot Framework: Visualizing Multi-Way
Contingency Tables with vcd. \emph{Journal of Statistical Software}, 17(3), 1-48.
% \url{https://www.jstatsoft.org/v17/i03/},
\doi{10.18637/jss.v017.i03},
available as \code{vignette("strucplot", package="vcd")}.
}
\author{
Michael Friendly
}
%\note{
%%% ~~further notes~~
%}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
\code{\link{glmlist}}, \code{\link{loglmlist}},
\code{\link{Kway}}
\code{\link{mosaic.glm}}, \code{\link[vcd]{mosaic}},
\code{\link[vcd]{strucplot}}, for the many parameters that control the details of mosaic plots.
}
\examples{
data(JobSatisfaction, package="vcd")
# view all pairwise mosaics
pairs(xtabs(Freq~management+supervisor+own, data=JobSatisfaction),
shade=TRUE, diag_panel=pairs_diagonal_mosaic)
modSat <- Kway(Freq ~ management+supervisor+own, data=JobSatisfaction,
family=poisson, prefix="JobSat")
names(modSat)
\dontrun{
mosaic(modSat) # uses menu, if interactive()
}
mosaic(modSat, "JobSat.1") # model label
mosaic(modSat, 2) # model index
# supply a formula to determine the order of variables in the mosaic
mosaic(modSat, 2, formula=~own+supervisor+management)
mosaic(modSat, ask=FALSE) # uses viewports
# use a different panel function, label the observed valued in the cells
mosaic(modSat, 1, main=TRUE, panel=sieve, gp=shading_Friendly, labeling=labeling_values)
data(Mental)
indep <- glm(Freq ~ mental+ses,
family = poisson, data = Mental)
Cscore <- as.numeric(Mental$ses)
Rscore <- as.numeric(Mental$mental)
coleff <- glm(Freq ~ mental + ses + Rscore:ses,
family = poisson, data = Mental)
roweff <- glm(Freq ~ mental + ses + mental:Cscore,
family = poisson, data = Mental)
linlin <- glm(Freq ~ mental + ses + Rscore:Cscore,
family = poisson, data = Mental)
# assign names for the plot labels
modMental <- glmlist(Indep=indep, ColEff=coleff, RowEff=roweff, `Lin x Lin`=linlin)
mosaic(modMental, ask=FALSE, margins=c(3,1,1,2), labeling_args=list(abbreviate_labs=5))
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{hplot}
vcdExtra/man/Heart.Rd 0000644 0001762 0000144 00000001465 13164013351 014142 0 ustar ligges users \name{Heart}
\Rdversion{1.1}
\alias{Heart}
\docType{data}
\title{Sex, Occupation and Heart Disease}
\description{Classification of individuals by gender, occupational category and occurrence of heart disease}
\usage{
data(Heart)
}
\format{
A 3-dimensional array resulting from cross-tabulating 3 variables for 21522 observations. The variable names and their levels are:
\tabular{rll}{
No \tab Name \tab Levels \cr
1\tab \code{Disease}\tab \code{"Disease", "None"}\cr
2\tab \code{Gender}\tab \code{"Male", "Female"}\cr
3\tab \code{Occup}\tab \code{"Unempl", "WhiteCol", "BlueCol"}\cr
}
}
%\details{ }
\source{
% \cite{Karger, 1980}
Karger, (1980).
}
%\references{
%}
%\seealso{ }
\examples{
data(Heart)
# example goes here
}
\keyword{datasets}
vcdExtra/man/Mammograms.Rd 0000644 0001762 0000144 00000002207 13164013351 015172 0 ustar ligges users \name{Mammograms}
\alias{Mammograms}
\docType{data}
\title{
Mammogram Ratings
}
\description{
Kundel & Polansky (2003) give (possibly contrived) data on a
set of 110 mammograms rated by two readers.
}
\usage{data(Mammograms)}
\format{
A frequency table in matrix form. The format is:
num [1:4, 1:4] 34 6 2 0 10 8 5 1 2 8 ...
- attr(*, "dimnames")=List of 2
..$ Reader2: chr [1:4] "Absent" "Minimal" "Moderate" "Severe"
..$ Reader1: chr [1:4] "Absent" "Minimal" "Moderate" "Severe"
}
%\details{
%%% ~~ If necessary, more details than the __description__ above ~~
%}
\source{
Kundel, H. L. & Polansky, M. (2003),
"Measurement of Observer Agreement", \emph{Radiology}, \bold{228}, 303-308, Table A1
}
%\references{
%%% ~~ possibly secondary sources and usages ~~
%}
\examples{
data(Mammograms)
B <- agreementplot(Mammograms, main="Mammogram ratings")
# agreement measures
B
Kappa(Mammograms)
## other displays
mosaic(Mammograms, shade=TRUE)
sieve(Mammograms, pop = FALSE, shade = TRUE)
labeling_cells(text = Mammograms, gp_text = gpar(fontface = 2, cex=1.75))(as.table(Mammograms))
}
\keyword{datasets}
vcdExtra/man/Depends.Rd 0000644 0001762 0000144 00000003142 13771411575 014471 0 ustar ligges users \name{Depends}
\alias{Depends}
\docType{data}
\title{
Dependencies of R Packages
}
\description{
This one-way table gives the type-token distribution of the number of
dependencies declared in 4983 packages listed on CRAN on January 17, 2014.
}
\usage{data(Depends)}
\format{
The format is:
'table' int [1:15(1d)] 986 1347 993 685 375 298 155 65 32 19 ...
- attr(*, "dimnames")=List of 1
..$ Depends: chr [1:15] "0" "1" "2" "3" ...
}
%\details{
%%% ~~ If necessary, more details than the __description__ above ~~
%}
\source{
Using code from
\url{https://blog.revolutionanalytics.com/2013/12/a-look-at-the-distribution-of-r-package-dependencies.html}
}
%\references{
%%% ~~ possibly secondary sources and usages ~~
%}
\examples{
data(Depends)
plot(Depends, xlab="Number of Dependencies", ylab="Number of R Packages", lwd=8)
\dontrun{
# The code below, from Joseph Rickert, downloads and tabulates the data
p <- as.data.frame(available.packages(),stringsAsFactors=FALSE)
names(p)
pkgs <- data.frame(p[,c(1,4)]) # Pick out Package names and Depends
row.names(pkgs) <- NULL # Get rid of row names
pkgs <- pkgs[complete.cases(pkgs[,2]),] # Remove NAs
pkgs$Depends2 <-strsplit(pkgs$Depends,",") # split list of Depends
pkgs$numDepends <- as.numeric(lapply(pkgs$Depends2,length)) # Count number of dependencies in list
zeros <- c(rep(0,dim(p)[1] - dim(pkgs)[1])) # Account for packages with no dependencies
Deps <- as.vector(c(zeros,pkgs$numDepends)) # Set up to tablate
Depends <- table(Deps)
}
}
\keyword{datasets}
vcdExtra/man/mosaic3d.Rd 0000644 0001762 0000144 00000016605 14202243677 014615 0 ustar ligges users \name{mosaic3d}
\Rdversion{1.1}
\alias{mosaic3d}
\alias{mosaic3d.default}
\alias{mosaic3d.loglm}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
3D Mosaic Plots
}
\description{
Produces a 3D mosaic plot for a contingency table (or a \code{link[MASS]{loglm}} model)
using the \code{\link[rgl]{rgl-package}}.
Generalizing the 2D mosaic plot, this begins
with a given 3D shape
(a unit cube), and successively sub-divides it along the X, Y, Z dimensions
according to the table margins, generating a nested set of 3D tiles.
The volume of the resulting tiles
is therefore proportional to the frequency represented in the
table cells.
Residuals from a given loglinear model are then used to color or shade each of the tiles.
This is a developing implementation. The arguments and details are subject to change.
}
\usage{
mosaic3d(x, ...)
\method{mosaic3d}{loglm}(x, type = c("observed", "expected"),
residuals_type = c("pearson", "deviance"), ...)
\method{mosaic3d}{default}(x, expected = NULL, residuals = NULL,
type = c("observed", "expected"), residuals_type = NULL,
shape = rgl::cube3d(alpha = alpha), alpha = 0.5,
spacing = 0.1, split_dir = 1:3, shading = shading_basic, interpolate=c(2,4),
zero_size=.05,
label_edge,
labeling_args = list(), newpage = TRUE, box=FALSE, ...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{x}{
A \code{link[MASS]{loglm}} model object. Alternatively,
a multidimensional \code{array} or \code{table} or \code{\link[vcd]{structable}}
of frequencies in a contingency table.
In the present implementation, the dimensions are taken in sequential order.
Use \code{link[base]{aperm}} or \code{\link[vcd]{structable}} to change this.
}
\item{expected}{
optionally, for contingency tables,
an array of expected frequencies of the same dimension as \code{x}, or alternatively
the corresponding loglinear model specification as used by \code{link[stats]{loglin}}
or \code{link[MASS]{loglm}} (see \code{\link[vcd]{structable}} for details).}
\item{residuals}{
optionally, an array of residuals of the same dimension as \code{x} (see details).
}
\item{type}{
a character string indicating whether the \code{"observed"} or the \code{"expected"}
frequencies in the table should be visualized by the volume of the 3D tiles.
}
\item{residuals_type}{
a character string indicating the type of residuals to be computed when none are
supplied. If residuals is \code{NULL}, \code{residuals_type} must be one of
\code{"pearson"} (default; giving components of Pearson's chi-squared),
\code{"deviance"} (giving components of the likelihood ratio chi-squared),
or \code{"FT"} for the Freeman-Tukey residuals.
The value of this argument can be abbreviated.
}
\item{shape}{
The initial 3D shape on which the mosaic is based. Typically this is a
call to an rgl function, and must produce a \code{shape3d} object.
The default is a "unit cube" on (-1, +1), with transparency specified by
\code{alpha}.
}
\item{alpha}{
Specifies the transparency of the 3D tiles used to compose the 3D mosaic.
}
\item{spacing}{
A number or vector giving the total amount of space used to separate the
3D tiles along each of the dimensions of the table. The values specified
are re-cycled to the number of table dimensions.
}
\item{split_dir}{
A numeric vector composed of the integers \code{1:3} or a character vector
composed of \code{c("x", "y", "z")}, where \code{split_dir[i]} specifies the
axis along which the tiles should be split for dimension \code{i} of the
table. The values specified are re-cycled to the number of table dimensions.
}
\item{shading}{
A function, taking an array or vector of residuals for the given model,
returning a vector of colors. At present, only the default
\code{shading=shading_basic} is provided. This is roughly equivalent
to the use of the \code{shade} argument in \code{\link[graphics]{mosaicplot}}
or to the use of \code{gp=shading_Friendly} in \code{\link[vcd]{mosaic}}.
}
\item{interpolate}{a vector of interpolation values for the \code{shading}
function.
}
\item{zero_size}{
The radius of a small sphere used to mark zero cells in the display.
}
\item{label_edge}{
A character vector composed of \code{c("-", "+")} indicating whether the
labels for a given table dimension are to be written at the minima (\code{"-"})
or maxima (\code{"+"}) of the \emph{other} dimensions in the plot.
The default is \code{rep( c('-', '+'), each=3, length=ndim)}, meaning
that the first three table variables are labeled at the minima,
and successive ones at the maxima.
}
\item{labeling_args}{
This argument is intended to be used to specify details of the rendering
of labels for the table dimensions, but at present has no effect.
}
\item{newpage}{
logical indicating whether a new page should be created for the plot or not.
}
\item{box}{
logical indicating whether a bounding box should be drawn around the plot.
}
\item{\dots}{
Other arguments passed down to \code{mosaic.default} or 3D functions.
}
}
\details{
Friendly (1995), Friendly [Sect. 4.5](2000) and Theus and Lauer (1999)
have all used the idea of 3D mosaic displays to explain various aspects
of loglinear models (the iterative proportional fitting algorithm,
the structure of various models for 3-way and n-way tables, etc.),
but no implementation of 3D mosaics was previously available.
For the default method,
residuals, used to color and shade the 3D tiles, can be passed explicitly,
or, more typically,
are computed as needed from observed and expected frequencies.
In this case, the expected frequencies are optionally computed for a specified
loglinear model given by the \code{expected} argument.
For the loglm method, residuals and observed frequencies are calculated from the model
object.
}
\value{
Invisibly, the list of \code{shape3d} objects used to draw the 3D mosaic,
with names corresponding to the concatenation of the level labels, separated
by ":".
}
\references{
Friendly, M. (1995).
Conceptual and Visual Models for Categorical Data,
\emph{The American Statistician}, \bold{49}, 153-160.
Friendly, M. \emph{Visualizing Categorical Data}, Cary NC: SAS Institute, 2000.
Web materials: \url{http://www.datavis.ca/books/vcd/}.
Theus, M. & Lauer, S. R. W. (1999)
Visualizing Loglinear Models.
\emph{Journal of Computational and Graphical Statistics}, \bold{8}, 396-412.
}
\author{
Michael Friendly, with the help of Duncan Murdoch and Achim Zeileis
}
%\note{
%%% ~~further notes~~
%}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
\code{\link[vcd]{strucplot}},
\code{\link[vcd]{mosaic}},
\code{\link[graphics]{mosaicplot}}
\code{\link[stats]{loglin}},
\code{\link[MASS]{loglm}} for details on fitting loglinear models
}
\examples{
# 2 x 2 x 2
if(requireNamespace("rgl")){
mosaic3d(Bartlett, box=TRUE)
# compare with expected frequencies under model of mutual independence
mosaic3d(Bartlett, type="expected", box=TRUE)
# 2 x 2 x 3
mosaic3d(Heart, box=TRUE)
}
\dontrun{
# 2 x 2 x 2 x 3
# illustrates a 4D table
mosaic3d(Detergent)
# compare 2D and 3D mosaics
demo("mosaic-hec")
}
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{hplot }
%\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
vcdExtra/man/logLik.loglm.Rd 0000644 0001762 0000144 00000006170 13164013351 015427 0 ustar ligges users \name{logLik.loglm}
\alias{logLik.loglm}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Log-Likelihood of a loglm Object
}
\description{
Calculates the log-likelihood value of the \code{loglm} model represented by \code{object}
evaluated at the estimated coefficients.
It allows the use of \code{\link[stats]{AIC}} and \code{\link[stats]{BIC}}, which require
that a \code{logLik} method exists to extract the corresponding log-likelihood for the
model.
}
\usage{
\method{logLik}{loglm}(object, ..., zero=1E-10)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{object}{
A \code{loglm} object
}
\item{\dots}{
For compatibility with the S3 generic; not used here
}
\item{zero}{value used to replace zero frequencies in calculating the log-likelihood}
}
\details{
If cell frequencies have not been stored with the \code{loglm} object
(via the argument \code{keep.frequencies = TRUE}), they are obtained
using \code{update}.
This function calculates the log-likelihood in a way that allows for non-integer
frequencies, such as the case where 0.5 has been added to all cell frequencies
to allow for sampling zeros. If the frequencies still contain zero values,
those are replaced by the value of \code{start}.
For integer frequencies, it gives the same result as the corresponding
model fit using \code{\link[stats]{glm}}, whereas \code{\link[stats]{glm}}
returns \code{-Inf} if there are any non-integer frequencies.
}
\value{
Returns an object of class \code{logLik}.
This is a number with one attribute, \code{"df"} (degrees of freedom),
giving the number of (estimated) parameters in the model. }
%\references{
%%% ~put references to the literature/web site here ~
%}
\author{
Achim Zeileis
}
%\note{
%%% ~~further notes~~
%}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
\code{\link[MASS]{loglm}}, \code{\link[stats]{AIC}}, \code{\link[stats]{BIC}},
}
\examples{
data(Titanic, package="datasets")
require(MASS)
titanic.mod1 <- loglm(~ (Class * Age * Sex) + Survived, data=Titanic)
titanic.mod2 <- loglm(~ (Class * Age * Sex) + Survived*(Class + Age + Sex), data=Titanic)
titanic.mod3 <- loglm(~ (Class * Age * Sex) + Survived*(Class + Age * Sex), data=Titanic)
logLik(titanic.mod1)
AIC(titanic.mod1, titanic.mod2, titanic.mod3)
BIC(titanic.mod1, titanic.mod2, titanic.mod3)
# compare with models fit using glm()
titanic <- as.data.frame(Titanic)
titanic.glm1 <- glm(Freq ~ (Class * Age * Sex) + Survived,
data=titanic, family=poisson)
titanic.glm2 <- glm(Freq ~ (Class * Age * Sex) + Survived*(Class + Age + Sex),
data=titanic, family=poisson)
titanic.glm3 <- glm(Freq ~ (Class * Age * Sex) + Survived*(Class + Age * Sex),
data=titanic, family=poisson)
logLik(titanic.glm1)
AIC(titanic.glm1, titanic.glm2, titanic.glm3)
BIC(titanic.glm1, titanic.glm2, titanic.glm3)
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{models}
\keyword{htest}% __ONLY ONE__ keyword per line
vcdExtra/man/cutfac.Rd 0000644 0001762 0000144 00000006060 13164013351 014340 0 ustar ligges users \name{cutfac}
\alias{cutfac}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Cut a Numeric Variable to a Factor
}
\description{
\code{cutfac} acts like \code{\link[base]{cut}},
dividing the range of \code{x} into intervals and
coding the values in \code{x} according in which interval they fall.
However, it gives nicer labels for the factor levels and
by default chooses convenient breaks among the values based on deciles.
It is particularly useful for plots in which one wants to make a
numeric variable discrete for the purpose of getting boxplots,
spinograms or mosaic plots.
}
\usage{
cutfac(x, breaks = NULL, q = 10)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{x}{a numeric vector which is to be converted to a factor by cutting
}
\item{breaks}{
either a numeric vector of two or more unique cut points or a single number (greater than or equal to 2) giving the number of intervals into which \code{x} is to be cut.
}
\item{q}{
the number of quantile groups used to define \code{breaks}, if that has not been specified.
}
}
\details{
By default, \code{\link[base]{cut}} chooses breaks by equal lengths of the range of \code{x},
whereas \code{cutfac} uses \code{\link[stats]{quantile}} to choose breaks of roughly equal count.
}
\value{
A \code{\link[base]{factor}} corresponding to \code{x} is returned
%% If it is a LIST, use
%% \item{comp1 }{Description of 'comp1'}
%% \item{comp2 }{Description of 'comp2'}
%% ...
}
\references{
Friendly, M. and Meyer, D. (2016).
\emph{Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data}.
Boca Raton, FL: Chapman & Hall/CRC. \url{http://ddar.datavis.ca}.
}
\author{
Achim Zeileis
}
%\note{
%%% ~~further notes~~
%}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
\code{\link[base]{cut}},
\code{\link[stats]{quantile}}
}
\examples{
if (require(AER)) {
data("NMES1988", package="AER")
nmes <- NMES1988[, c(1, 6:8, 13, 15, 18)]
plot(log(visits+1) ~ cutfac(chronic), data = nmes,
ylab = "Physician office visits (log scale)",
xlab = "Number of chronic conditions", main = "chronic")
plot(log(visits+1) ~ cutfac(hospital, c(0:2, 8)), data = nmes,
ylab = "Physician office visits (log scale)",
xlab = "Number of hospital stays", main = "hospital")
}
%\donttest{
%# countreg not yet on CRAN
%if (require(countreg)) {
%data("CrabSatellites", package = "countreg")
%
%# jittered scatterplot
%plot(jitter(satellites) ~ width, data=CrabSatellites,
% ylab="Number of satellites (jittered)", xlab="Carapace width",
% cex.lab=1.25)
%with(CrabSatellites, lines(lowess(width, satellites), col="red", lwd=2))
%
%# boxplot, using deciles
%plot(satellites ~ cutfac(width), data=CrabSatellites,
% ylab="Number of satellites", xlab="Carapace width (deciles)")
%}
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{manip}
%\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
vcdExtra/man/Heckman.Rd 0000644 0001762 0000144 00000005053 13164013351 014442 0 ustar ligges users \name{Heckman}
\Rdversion{1.1}
\alias{Heckman}
\docType{data}
\title{Labour Force Participation of Married Women 1967-1971}
\description{1583 married women were surveyed over the years 1967-1971,
recording whether or not they were employed in the labor force.
The data, originally from Heckman & Willis (1977) provide an example
of modeling longitudinal categorical data, e.g., with markov chain
models for dependence over time.
}
\usage{
data(Heckman)
}
\format{
A 5-dimensional array resulting from cross-tabulating 5 variables for 1583 observations. The variable names and their levels are:
\tabular{rll}{
No \tab Name \tab Levels \cr
1\tab \code{e1971}\tab \code{"71Yes", "No"}\cr
2\tab \code{e1970}\tab \code{"70Yes", "No"}\cr
3\tab \code{e1969}\tab \code{"69Yes", "No"}\cr
4\tab \code{e1968}\tab \code{"68Yes", "No"}\cr
5\tab \code{e1967}\tab \code{"67Yes", "No"}\cr
}
}
\details{
Lindsey (1993) fits an initial set of logistic regression models examining the dependence of
employment in 1971 (\code{e1971}) on successive subsets of the previous years,
\code{e1970}, \code{e1969}, \dots \code{e1967}.
Alternatively, one can examine markov chain models of first-order (dependence on previous
year), second-order (dependence on previous two years), etc.
}
\source{
% \cite{Lindsey:93 [p. 185]}
Lindsey, J. K. (1993).
\emph{Models for Repeated Measurements}
Oxford, UK: Oxford University Press, p. 185.
}
\references{
% \cite{HeckmanWillis:77}
Heckman, J.J. & Willis, R.J. (1977).
"A beta-logistic model for the analysis of sequential labor force participation by married women."
\emph{Journal of Political Economy}, 85: 27-58
}
%\seealso{ }
\examples{
data(Heckman)
# independence model
mosaic(Heckman, shade=TRUE)
# same, as a loglm()
require(MASS)
(heckman.mod0 <- loglm(~ e1971+e1970+e1969+e1968+e1967, data=Heckman))
mosaic(heckman.mod0, main="Independence model")
# first-order markov chain: bad fit
(heckman.mod1 <- loglm(~ e1971*e1970 + e1970*e1969 +e1969*e1968 + e1968*e1967, data=Heckman))
mosaic(heckman.mod1, main="1st order markov chain model")
# second-order markov chain: bad fit
(heckman.mod2 <- loglm(~ e1971*e1970*e1969 + e1970*e1969*e1968 +e1969*e1968*e1967, data=Heckman))
mosaic(heckman.mod2, main="2nd order markov chain model")
# third-order markov chain: fits OK
(heckman.mod3 <- loglm(~ e1971*e1970*e1969*e1968 + e1970*e1969*e1968*e1967, data=Heckman))
mosaic(heckman.mod2, main="3rd order markov chain model")
}
\keyword{datasets}
vcdExtra/man/Bartlett.Rd 0000644 0001762 0000144 00000002753 13164013351 014661 0 ustar ligges users \name{Bartlett}
\Rdversion{1.1}
\alias{Bartlett}
\docType{data}
\title{Bartlett data on plum root cuttings}
\description{In an experiment to investigate the effect of cutting length (two levels) and planting time (two levels)
on the survival of plum root cuttings, 240 cuttings were planted for each of the 2 x 2 combinations of these factors,
and their survival was later recorded.
Bartlett (1935) used these data to illustrate a method for testing for no three-way interaction in a contingency table.}
\usage{
data(Bartlett)
}
\format{
A 3-dimensional array resulting from cross-tabulating 3 variables for 960 observations. The variable names and their levels are:
\tabular{rll}{
No \tab Name \tab Levels \cr
1\tab \code{Alive}\tab \code{"Alive", "Dead"}\cr
2\tab \code{Time}\tab \code{"Now", "Spring"}\cr
3\tab \code{Length}\tab \code{"Long", "Short"}\cr
}
}
%\details { }
\source{
% \cite{Hand-etal:94 [p.15 #19]}
Hand, D. and Daly, F. and Lunn, A. D.and McConway, K. J. and Ostrowski, E. (1994).
\emph{A Handbook of Small Data Sets}. London: Chapman & Hall, p. 15, # 19.
}
\references{
% \cite{Bartlett:35}
Bartlett, M. S. (1935).
Contingency Table Interactions
\emph{Journal of the Royal Statistical Society}, Supplement, 1935, 2, 248-252.
}
%\seealso { }
\examples{
data(Bartlett)
fourfold(Bartlett, mfrow=c(1,2))
mosaic(Bartlett, shade=TRUE)
pairs(Bartlett, gp=shading_Friendly)
}
\keyword{datasets}
vcdExtra/man/Geissler.Rd 0000644 0001762 0000144 00000004735 13164013351 014657 0 ustar ligges users \name{Geissler}
\alias{Geissler}
\docType{data}
\title{
Geissler's Data on the Human Sex Ratio
}
\description{
Geissler (1889) published data on the distributions of boys and girls in families in
Saxony, collected for the period 1876-1885. The \code{Geissler} data tabulates
the family composition of 991,958 families by the number of boys and girls
listed in the table supplied by Edwards (1958, Table 1).
}
\usage{data(Geissler)}
\format{
A data frame with 90 observations on the following 4 variables. The rows represent the
non-NA entries in Edwards' table.
\describe{
\item{\code{boys}}{number of boys in the family, \code{0:12}}
\item{\code{girls}}{number of girls in the family, \code{0:12}}
\item{\code{size}}{family size: \code{boys+girls}}
\item{\code{Freq}}{number of families with this sex composition}
}
}
\details{
The data on family composition was available because, on the birth of a child,
the parents had to state the sex of all their children on the birth certificate.
These family records are not necessarily independent, because a given family
may have had several children during this 10 year period, included as
multiple records.
}
\source{
Edwards, A. W. F. (1958).
An Analysis Of Geissler's Data On The Human Sex Ratio.
\emph{Annals of Human Genetics},
23, 6-15.
}
\references{
Friendly, M. and Meyer, D. (2016).
\emph{Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data}.
Boca Raton, FL: Chapman & Hall/CRC. \url{http://ddar.datavis.ca}.
Geissler, A. (1889).
\emph{Beitrage zur Frage des Geschlechts verhaltnisses der Geborenen}
Z. K. Sachsischen Statistischen Bureaus, 35, n.p.
Lindsey, J. K. & Altham, P. M. E. (1998).
Analysis of the human sex ratio by using overdispersion models.
\emph{Journal of the Royal Statistical Society: Series C (Applied Statistics)},
47, 149-157.
}
\seealso{
\code{\link[vcd]{Saxony}}, containing the data for families of size 12.
}
\examples{
data(Geissler)
## maybe str(Geissler) ; plot(Geissler) ...
# reproduce Saxony data, families of size 12
Saxony12<-subset(Geissler, size==12, select=c(boys, Freq))
rownames(Saxony12)<-NULL
# make a 1-way table
xtabs(Freq~boys, Saxony12)
# extract data for other family sizes
Saxony11<-subset(Geissler, size==11, select=c(boys, Freq))
rownames(Saxony11)<-NULL
Saxony10<-subset(Geissler, size==10, select=c(boys, Freq))
rownames(Saxony10)<-NULL
}
\keyword{datasets}
vcdExtra/man/Yamaguchi87.Rd 0000644 0001762 0000144 00000007565 13164013351 015174 0 ustar ligges users \name{Yamaguchi87}
\alias{Yamaguchi87}
\docType{data}
\title{
Occupational Mobility in Three Countries
}
\description{
Yamaguchi (1987) presented this three-way frequency table,
cross-classifying occupational categories of sons and fathers
in the United States, United Kingdom and Japan. This data set has
become a classic for models comparing two-way mobility tables across
layers corresponding to countries, groups or time (e.g.,
Goodman and Hout, 1998; Xie, 1992).
The US data were derived from the 1973 OCG-II survey; those
for the UK from the 1972 Oxford Social Mobility Survey; those
for Japan came from the 1975 Social Stratification and Mobility
survey. They pertain to men aged 20-64.
}
\usage{data(Yamaguchi87)}
\format{
A frequency data frame with 75 observations on the following 4 variables. The total sample size is 28887.
\describe{
\item{\code{Son}}{a factor with levels \code{UpNM} \code{LoNM} \code{UpM} \code{LoM} \code{Farm}}
\item{\code{Father}}{a factor with levels \code{UpNM} \code{LoNM} \code{UpM} \code{LoM} \code{Farm}}
\item{\code{Country}}{a factor with levels \code{US} \code{UK} \code{Japan}}
\item{\code{Freq}}{a numeric vector}
}
}
\details{
Five status categories -- upper and lower
nonmanuals (\code{UpNM}, \code{LoNM}),
upper and lower manuals (\code{UpM}, \code{LoM}),
and \code{Farm}) are used for both fathers' occupations and
sons' occupations.
Upper nonmanuals are professionals,
managers, and officials; lower
nonmanuals are proprietors, sales workers, and
clerical workers; upper manuals are skilled
workers; lower manuals are semi-skilled and
unskilled nonfarm workers; and farm workers
are farmers and farm laborers.
Some of the models from Xie (1992), Table 1, are fit in \code{demo(yamaguchi-xie)}.
}
\source{
Yamaguchi, K. (1987).
Models for comparing mobility tables: toward parsimony and substance,
\emph{American Sociological Review}, vol. 52 (Aug.), 482-494, Table 1
}
\references{
Goodman, L. A. and Hout, M. (1998).
Statistical Methods and Graphical Displays for Analyzing How the Association Between Two Qualitative Variables Differs Among Countries, Among Groups, Or Over Time:
A Modified Regression-Type Approach.
\emph{Sociological Methodology}, 28 (1), 175-230.
Xie, Yu (1992).
The log-multiplicative layer effect model for comparing mobility tables.
\emph{American Sociological Review}, 57 (June), 380-395.
}
\examples{
data(Yamaguchi87)
# reproduce Table 1
structable(~ Father + Son + Country, Yamaguchi87)
# create table form
Yama.tab <- xtabs(Freq ~ Son + Father + Country, data=Yamaguchi87)
# define mosaic labeling_args for convenient reuse in 3-way displays
largs <- list(rot_labels=c(right=0), offset_varnames = c(right = 0.6),
offset_labels = c(right = 0.2),
set_varnames = c(Son="Son's status", Father="Father's status")
)
###################################
# Fit some models & display mosaics
# Mutual independence
yama.indep <- glm(Freq ~ Son + Father + Country, data=Yamaguchi87, family=poisson)
anova(yama.indep)
mosaic(yama.indep, ~Son+Father, main="[S][F] ignoring country")
mosaic(yama.indep, ~Country + Son + Father, condvars="Country",
labeling_args=largs,
main='[S][F][C] Mutual independence')
# no association between S and F given country ('perfect mobility')
# asserts same associations for all countries
yama.noRC <- glm(Freq ~ (Son + Father) * Country, data=Yamaguchi87, family=poisson)
anova(yama.noRC)
mosaic(yama.noRC, ~~Country + Son + Father, condvars="Country",
labeling_args=largs,
main="[SC][FC] No [SF] (perfect mobility)")
# ignore diagonal cells
yama.quasi <- update(yama.noRC, ~ . + Diag(Son,Father):Country)
anova(yama.quasi)
mosaic(yama.quasi, ~Son+Father, main="Quasi [S][F]")
## see also:
# demo(yamaguchi-xie)
##
}
\keyword{datasets}
vcdExtra/man/glmlist.Rd 0000644 0001762 0000144 00000006632 13164013351 014553 0 ustar ligges users \name{glmlist}
\Rdversion{1.1}
\alias{glmlist}
\alias{loglmlist}
\alias{coef.glmlist}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Create a Model List Object
}
\description{
\code{glmlist} creates a \code{glmlist} object containing a list of fitted \code{glm} objects with their names.
\code{loglmlist} does the same for \code{loglm} objects.
The intention is to provide object classes to facilitate model comparison,
extraction, summary and plotting of model components, etc., perhaps using
\code{\link[base]{lapply}} or similar.
There exists a \code{\link[stats]{anova.glm}} method for \code{glmlist} objects. Here,
a \code{coef} method is also defined, collecting the coefficients from all models in
a single object of type determined by \code{result}.
}
\usage{
glmlist(...)
loglmlist(...)
\method{coef}{glmlist}(object, result=c("list", "matrix", "data.frame"), ...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{\dots}{
One or more model objects, as appropriate to the function,
optionally assigned names as in \code{list}.
}
\item{object}{a \code{glmlist} object}
\item{result}{type of the result to be returned}
}
\details{
The arguments to \code{glmlist} or \code{loglmlist} are of the form \code{value} or \code{name=value}.
Any objects which do not inherit the appropriate class \code{glm} or \code{loglm} are excluded, with a warning.
In the \code{coef} method, coefficients from the different models are matched by name in the list of
unique names across all models.
}
\value{
An object of class \code{glmlist} \code{loglmlist}, just like a \code{list},
except that each model is given a \code{name} attribute.
%% If it is a LIST, use
%% \item{comp1 }{Description of 'comp1'}
%% \item{comp2 }{Description of 'comp2'}
%% ...
}
%\references{
%%% ~put references to the literature/web site here ~
%}
\author{
Michael Friendly;
\code{coef} method by John Fox
}
%\note{
%%% ~~further notes~~
%}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
The function \code{\link[Hmisc]{llist}} in package \code{Hmisc} is similar, but perplexingly
more general.
The function \code{\link[stats]{anova.glm}} also handles \code{glmlist objects}
\code{\link{LRstats}} gives LR statistics and tests for a \code{glmlist} object.
}
\examples{
data(Mental)
indep <- glm(Freq ~ mental+ses,
family = poisson, data = Mental)
Cscore <- as.numeric(Mental$ses)
Rscore <- as.numeric(Mental$mental)
coleff <- glm(Freq ~ mental + ses + Rscore:ses,
family = poisson, data = Mental)
roweff <- glm(Freq ~ mental + ses + mental:Cscore,
family = poisson, data = Mental)
linlin <- glm(Freq ~ mental + ses + Rscore:Cscore,
family = poisson, data = Mental)
# use object names
mods <- glmlist(indep, coleff, roweff, linlin)
names(mods)
# assign new names
mods <- glmlist(Indep=indep, Col=coleff, Row=roweff, LinxLin=linlin)
names(mods)
LRstats(mods)
coef(mods, result='data.frame')
#extract model components
unlist(lapply(mods, deviance))
res <- lapply(mods, residuals)
boxplot(as.data.frame(res), main="Residuals from various models")
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{utilities}
\keyword{models}% __ONLY ONE__ keyword per line
vcdExtra/man/GKgamma.Rd 0000644 0001762 0000144 00000004305 14202242352 014376 0 ustar ligges users \name{GKgamma}
\alias{GKgamma}
\alias{print.GKgamma}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{Calculate Goodman-Kruskal Gamma for ordered tables}
\description{
The Goodman-Kruskal \eqn{\gamma}{gamma} statistic is a measure of
association for ordinal factors in a two-way table proposed by
Goodman and Kruskal (1954).
}
\usage{
GKgamma(x, level = 0.95)
%\method{print}{GKgamma}{x, digits = 3, ...}
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{x}{A two-way frequency table, in matrix or table form. The rows and columns
are considered to be ordinal factors}
\item{level}{Confidence level for a significance test of \eqn{\gamma \ne =}{gamma !=0}}
% \item{digits}{Number of digits printed by the print method}
% \item{...}{Other arguments}
}
%\details{
% ~~ If necessary, more details than the description above ~~
%}
\value{
Returns an object of class \code{"GKgamma"} with 6 components, as follows
% If it is a LIST, use
%\describe{
\item{gamma}{The gamma statistic}
\item{C}{Total number of concordant pairs in the table}
\item{D}{Total number of discordant pairs in the table}
\item{sigma}{Standard error of gamma }
\item{CIlevel}{Confidence level}
\item{CI}{Confidence interval}
% }
}
\references{
Agresti, A. \emph{Categorical Data Analysis}. John Wiley & Sons, 2002,
pp. 57--59.
Goodman, L. A., & Kruskal, W. H. (1954). Measures of
association for cross classifications. \emph{Journal of the
American Statistical Association}, 49, 732-764.
Goodman, L. A., & Kruskal, W. H. (1963). Measures of
association for cross classifications III: Approximate
sampling theory. \emph{Journal of the American Statistical
Association}, 58, 310-364.
}
\author{Michael Friendly; original version by Laura Thompson}
%\note{ ~~further notes~~
%
% ~Make other sections like Warning with \section{Warning }{....} ~
%}
\seealso{\code{\link[vcd]{assocstats}}, \link[vcd]{Kappa}}
\examples{
data(JobSat)
GKgamma(JobSat)
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{htest}
\keyword{category}
%\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
vcdExtra/man/Gilby.Rd 0000644 0001762 0000144 00000003427 13164013351 014145 0 ustar ligges users \name{Gilby}
\Rdversion{1.1}
\alias{Gilby}
\docType{data}
\title{Clothing and Intelligence Rating of Children}
\description{Schoolboys were classified according to their clothing and to their teachers rating of "dullness"
(lack of intelligence), in a 5 x 7 table originally from
Gilby (1911).
Anscombe (1981) presents a slightly collapsed 4 x 6 table, used here, where
the last two categories of clothing were pooled as were the first two categories of dullness due to small counts.
Both \code{Dullnes} and \code{Clothing} are ordered categories, so models and methods
that examine their association in terms of ordinal categories are profitable.
}
\usage{
data(Gilby)
}
\format{
A 2-dimensional array resulting from cross-tabulating 2 variables for 1725 observations. The variable names and their levels are:
\tabular{rll}{
No \tab Name \tab Levels \cr
1\tab \code{Dullness}\tab \code{"Ment. defective", "Slow", "Slow Intell", "Fairly Intell", "Capable", "V.Able"}\cr
2\tab \code{Clothing}\tab \code{"V.Well clad", "Well clad", "Passable", "Insufficient"}\cr
}
}
%\details{ }
\source{
Anscombe, F. J. (1981).
\emph{Computing in Statistical Science Through APL}.
New York: Springer-Verlag, p. 302
}
\references{
% \cite{Gilby & Pearson 1911, from Anscombe 1981, p 302}
Gilby, W. H. (1911).
On the significance of the teacher's appreciation of
general intelligence. \emph{Biometrika}, 8, 93-108 (esp. p. 94).
[Quoted by Kendall (1943,..., 1953) Table 13.1, p 320.]
}
%\seealso{ }
\examples{
data(Gilby)
mosaic(Gilby, shade=TRUE)
# correspondence analysis to see relations among categories
if(require(ca)){
ca(Gilby)
plot(ca(Gilby))
title(xlab="Dimension 1", ylab="Dimension 2")
}
}
\keyword{datasets}
vcdExtra/man/Abortion.Rd 0000644 0001762 0000144 00000002637 13164013351 014656 0 ustar ligges users \name{Abortion}
\Rdversion{1.1}
\alias{Abortion}
\docType{data}
\title{Abortion Opinion Data}
\description{Opinions about abortion classified by gender and SES}
\usage{
data(Abortion)
}
\format{
A 3-dimensional array resulting from cross-tabulating 3 variables for 1100 observations. The variable names and their levels are:
\tabular{rll}{
No \tab Name \tab Levels \cr
1\tab \code{Sex}\tab \code{"Female", "Male"}\cr
2\tab \code{Status}\tab \code{"Lo", "Hi"}\cr
3\tab \code{Support_Abortion}\tab \code{"Yes", "No"}\cr
}
}
\details{
The combinations of \code{Sex} and \code{Status} represent four independent samples, having
fixed \code{Sex}-\code{Status} marginal totals.
Thus the \code{Sex:Status} association must be included in any loglinear model.
\code{Support_Abortion} is a natural response variable. }
\source{
% \cite{Christensen:90 [p. 92]}
Christensen, R. (1990).
\emph{Log-Linear Models},
New York, NY: Springer-Verlag, p. 92, Example 3.5.2.
Christensen, R. (1997).
\emph{Log-Linear Models and Logistic Regression},
New York, NY: Springer, p. 100, Example 3.5.2.
}
%\references{
%}
%\seealso { }
\examples{
data(Abortion)
# example goes here
ftable(Abortion)
mosaic(Abortion, shade=TRUE)
# stratified by Sex
fourfold(aperm(Abortion, 3:1))
# stratified by Status
fourfold(aperm(Abortion, c(3,1,2)))
}
\keyword{datasets}
vcdExtra/man/mcaplot.Rd 0000644 0001762 0000144 00000007153 13164013351 014536 0 ustar ligges users \name{mcaplot}
\alias{mcaplot}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Simple and enhanced plot of MCA solutions
}
\description{
This function is intended as an alternative to \code{\link[ca]{plot.mjca}}
for plotting multiple correspondence analysis solutions.
It provides more flexibility for labeling factor levels and connecting
them with lines. It does not support some features of
\code{plot.mjca} (centroids, supplementary points, arrows, etc.)
}
\usage{
mcaplot(obj, map = "symmetric", dim = 1:2,
col = c("blue", "red", "brown", "black", "green3", "purple"),
pch = 15:20, cex = 1.2, pos = 3, lines = TRUE, lwd = 2,
legend = FALSE, legend.pos = "topright",
xlab = "_auto_", ylab = "_auto_",
rev.axes = c(FALSE, FALSE),
...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{obj}{An \code{"mjca"} object}
\item{map}{
Character string specifying the map type, i.e., the scaling applied to coordinates
for different types of MCA representations.
Allowed options include:
\code{"symmetric"} (default),
\code{"rowprincipal"}, \code{"colprincipal"}, \code{"symbiplot"},
\code{"rowgab"}, \code{"colgab"}, \code{"rowgreen"}, \code{"colgreen"}.
See \code{\link[ca]{mjca}} for details.
}
\item{dim}{Dimensions to plot, an integer vector of length 2}
\item{col}{Vector of colors, one for each factor in the MCA}
\item{pch}{Vector of point symbols for the category levels, one for each factor}
\item{cex}{Character size for points and level labels}
\item{pos}{Position of level labels relative to the category points; either a single number
or a vector of length equal to the number of category points.}
\item{lines}{A logical or an integer vector indicating which factors are to be
joined with lines using \code{\link{multilines}}}
\item{lwd}{Line width(s) for the lines}
\item{legend}{Logical; draw a legend for the factor names?}
\item{legend.pos}{Position of the legend in the plot, as in \code{\link[graphics]{legend}}}
\item{xlab,ylab}{Labels for horizontal and vertical axes.
The default, \code{"_auto_"} means that the function auto-generates a label of
the form \code{"Dimension X (xx.x \%)"}
}
\item{rev.axes}{A logical vector of length 2, where TRUE reverses the direction of the
corresponding axis}
\item{\dots}{Arguments passed down to \code{plot}}
}
%\details{
%% ~~ If necessary, more details than the description above ~~
%}
\value{
Returns the coordinates of the category points invisibly
%% ~Describe the value returned
%% If it is a LIST, use
%% \item{comp1 }{Description of 'comp1'}
%% \item{comp2 }{Description of 'comp2'}
%% ...
}
%\references{
%% ~put references to the literature/web site here ~
%}
\author{
Michael Friendly
}
%\note{
%% ~~further notes~~
%}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
\code{\link[ca]{mjca}},
\code{\link[ca]{plot.mjca}}
\code{\link[ca]{cacoord}} returns CA and MCA coordinates,
\code{\link[ca]{multilines}} draw multiple lines according to a factor,
}
\examples{
require(ca)
data(Titanic)
titanic.mca <- mjca(Titanic)
mcaplot(titanic.mca, legend=TRUE, legend.pos="topleft")
data(HairEyeColor)
haireye.mca <- mjca(HairEyeColor)
mcaplot(haireye.mca, legend=TRUE, cex.lab=1.3)
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{hplot}
%\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
vcdExtra/man/ICU.Rd 0000644 0001762 0000144 00000012006 14202243452 013511 0 ustar ligges users \name{ICU}
\alias{ICU}
\docType{data}
\title{
ICU data set
}
\description{
The ICU data set consists of a sample of 200 subjects who were part of
a much larger study on survival of patients following admission to an adult
intensive care unit (ICU), derived from
Hosmer, Lemeshow and Sturdivant (2013) and Friendly (2000).
The major goal of this study was to develop a
logistic regression model to predict the probability of survival to hospital
discharge of these patients and to study the risk factors associated with
ICU mortality. The clinical details of the study are described in
Lemeshow, Teres, Avrunin, and Pastides (1988).
This data set is often used to illustrate model selection methods for
logistic regression.
}
\usage{data(ICU)}
\format{
A data frame with 200 observations on the following 22 variables.
\describe{
% \item{\code{id}}{Patient id code, a numeric vector}
\item{\code{died}}{Died before discharge?, a factor with levels \code{No} \code{Yes}}
\item{\code{age}}{Patient age, a numeric vector}
\item{\code{sex}}{Patient sex, a factor with levels \code{Female} \code{Male}}
\item{\code{race}}{Patient race, a factor with levels \code{Black} \code{Other} \code{White}. Also represented here as \code{white}.}
\item{\code{service}}{Service at ICU Admission, a factor with levels \code{Medical} \code{Surgical}}
\item{\code{cancer}}{Cancer part of present problem?, a factor with levels \code{No} \code{Yes}}
\item{\code{renal}}{History of chronic renal failure?, a factor with levels \code{No} \code{Yes}}
\item{\code{infect}}{Infection probable at ICU admission?, a factor with levels \code{No} \code{Yes}}
\item{\code{cpr}}{Patient received CPR prior to ICU admission?, a factor with levels \code{No} \code{Yes}}
\item{\code{systolic}}{Systolic blood pressure at admission (mm Hg), a numeric vector}
\item{\code{hrtrate}}{Heart rate at ICU Admission (beats/min), a numeric vector}
\item{\code{previcu}}{Previous admission to an ICU within 6 Months?, a factor with levels \code{No} \code{Yes}}
\item{\code{admit}}{Type of admission, a factor with levels \code{Elective} \code{Emergency}}
\item{\code{fracture}}{Admission with a long bone, multiple, neck, single area, or hip fracture?
a factor with levels \code{No} \code{Yes}}
\item{\code{po2}}{PO2 from initial blood gases, a factor with levels \code{>60} \code{<=60}}
\item{\code{ph}}{pH from initial blood gases, a factor with levels \code{>=7.25} \code{<7.25}}
\item{\code{pco}}{PCO2 from initial blood gases, a factor with levels \code{<=45} \code{>45}}
\item{\code{bic}}{Bicarbonate (HCO3) level from initial blood gases, a factor with levels \code{>=18} \code{<18}}
\item{\code{creatin}}{Creatinine, from initial blood gases, a factor with levels \code{<=2} \code{>2}}
\item{\code{coma}}{Level of unconsciousness at admission to ICU, a factor with levels \code{None} \code{Stupor} \code{Coma}}
\item{\code{white}}{a recoding of \code{race}, a factor with levels \code{White} \code{Non-white}}
\item{\code{uncons}}{a recoding of \code{coma} a factor with levels \code{No} \code{Yes}}
}
}
\details{
Patient ID numbers are the rownames of the data frame.
Note that the last two variables \code{white} and \code{uncons} are a recoding of
respectively \code{race} and \code{coma} to binary variables.
}
\source{
M. Friendly (2000), \emph{Visualizing Categorical Data}, Appendix B.4. SAS Institute, Cary, NC.
Hosmer, D. W. Jr., Lemeshow, S. and Sturdivant, R. X. (2013)
\emph{Applied Logistic Regression}, NY: Wiley,
Third Edition.
}
\references{
Lemeshow, S., Teres, D., Avrunin, J. S., Pastides, H. (1988). Predicting
the Outcome of Intensive Care Unit Patients. \emph{Journal of the American
Statistical Association}, 83, 348-356.
}
\examples{
data(ICU)
# remove redundant variables (race, coma)
ICU1 <- ICU[,-c(4,20)]
# fit full model
icu.full <- glm(died ~ ., data=ICU1, family=binomial)
summary(icu.full)
# simpler model (found from a "best" subsets procedure)
icu.mod1 <- glm(died ~ age + sex + cancer + systolic + admit + uncons, data=ICU1, family=binomial)
summary(icu.mod1)
# even simpler model
icu.mod2 <- glm(died ~ age + cancer + admit + uncons, data=ICU1, family=binomial)
summary(icu.mod2)
anova(icu.mod2, icu.mod1, icu.full, test="Chisq")
## Reproduce Fig 6.12 from VCD
icu.fit <- data.frame(ICU, prob=predict(icu.mod2, type="response"))
# combine categorical risk factors to a single string
risks <- ICU[, c("cancer", "admit", "uncons")]
risks[,1] <- ifelse(risks[,1]=="Yes", "Cancer", "")
risks[,2] <- ifelse(risks[,2]=="Emergency", "Emerg", "")
risks[,3] <- ifelse(risks[,3]=="Yes", "Uncons", "")
risks <- apply(risks, 1, paste, collapse="")
risks[risks==""] <- "(none)"
icu.fit$risks <- risks
library(ggplot2)
ggplot(icu.fit, aes(x=age, y=prob, color=risks)) +
geom_point(size=2) +
geom_line(size=1.25, alpha=0.5) +
theme_bw() + ylab("Probability of death")
}
\keyword{datasets}
vcdExtra/man/seq_mosaic.Rd 0000644 0001762 0000144 00000006413 14202244754 015227 0 ustar ligges users \name{seq_mosaic}
\alias{seq_mosaic}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Sequential Mosaics and Strucplots for an N-way Table
}
\description{
This function takes an n-way contingency table and plots mosaics for series of sequential
models to the 1-, 2-, ... n-way marginal tables, corresponding to a variety of
types of loglinear models.
}
\usage{
seq_mosaic(x, panel = mosaic,
type = c("joint", "conditional", "mutual", "markov", "saturated"),
plots = 1:nf, vorder = 1:nf,
k = NULL, ...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{x}{
a contingency table in array form, with optional category labels specified in the
dimnames(x) attribute,
or else a data.frame in frequency form, with the frequency variable named \code{"Freq"}.
}
\item{panel}{
a \code{\link[vcd]{strucplot}} panel function,
typically \code{\link[vcd]{mosaic}} or \code{\link[vcd]{sieve}. NOT yet implemented.}
}
\item{type}{
type of sequential model to fit, a character string.
One of \code{"joint"}, \code{"conditional"}, \code{"mutual"}, \code{"markov"},
or \code{"saturated"}.
}
\item{plots}{
which marginal sub-tables to plot? A vector of a (sub)set of the integers, \code{1:nf}
where \code{nf} is the number of factors in the full n-way table.
}
\item{vorder}{
order of variables, a permutation of the integers \code{1:nf},
used to reorder the variables in the original table for the purpose
of fitting sequential marginal models.
}
\item{k}{
conditioning variable(s) for \code{type} = \code{"joint"}, \code{"conditional"}
or Markov chain order for \code{type} = \code{"markov"}
}
\item{\dots}{
other arguments passed to \code{\link[vcd]{mosaic}}.
}
}
\details{
This function produces
similar plots to the use of \code{\link{mosaic.loglmlist}},
called with the result of \code{\link{seq_loglm}}.
}
\value{
None. Used for its side-effect of producing plots
%% If it is a LIST, use
%% \item{comp1 }{Description of 'comp1'}
%% \item{comp2 }{Description of 'comp2'}
%% ...
}
\references{
These functions were inspired by the original SAS implementation of
mosaic displays, described in the \emph{User's Guide},
\url{http://www.datavis.ca/mosaics/mosaics.pdf}
}
\author{
Michael Friendly
}
%\note{
%%% ~~further notes~~
%}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
\code{\link{loglin-utilities}} for descriptions of sequential models,
\code{\link{conditional}},
\code{\link{joint}},
\code{\link{mutual}}, \dots
\code{\link{loglmlist}}, \code{\link{mosaic.loglmlist}},
\code{\link{seq_loglm}}
\code{\link{mosaic.glm}}, \code{\link[vcd]{mosaic}},
\code{\link[vcd]{strucplot}}, for the many parameters that control the details of mosaic plots.
}
\examples{
data(Titanic, package="datasets")
seq_mosaic(Titanic) # models of joint independence, Survived last
seq_mosaic(Titanic, type="condit")
seq_mosaic(Titanic, type="mutual")
# other panel functions and options: presently BUGGED
\dontrun{
seq_mosaic(Titanic, type="mutual", panel=sieve,
gp=shading_Friendly, labeling=labeling_values)
}
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{hplots}
vcdExtra/man/update.xtabs.Rd 0000644 0001762 0000144 00000003220 13164013351 015470 0 ustar ligges users \name{update.xtabs}
\alias{update.xtabs}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Update method for a \code{xtabs} object
}
\description{
Provides an \code{update} method for \code{"xtabs"} objects,
typically by removing terms from the formula to collapse over them.
}
\usage{
\method{update}{xtabs}(object, formula., ..., evaluate = TRUE)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{object}{An existing \code{"xtabs"} object}
\item{formula.}{Changes to the formula ? see \code{\link[stats]{update.formula}} for details}
\item{\dots}{Additional arguments to the call, or arguments with changed values.
}
\item{evaluate}{If \code{TRUE}, evaluate the new call else return the call}
}
%\details{
%%% ~~ If necessary, more details than the description above ~~
%}
\value{
If \code{evaluate == TRUE}, the new \code{"xtabs"} object, otherwise the updated call
}
%\references{
%%% ~put references to the literature/web site here ~
%}
\author{
Michael Friendly
}
%\note{
%%% ~~further notes~~
%}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
\code{\link[stats]{update.formula}} for details on updates to model formulae
\code{\link[base]{margin.table}} does something similar,
\code{\link{collapse.table}} collapses category levels
}
\examples{
vietnam.tab <- xtabs(Freq ~ sex + year + response, data=Vietnam)
update(vietnam.tab, formula = ~ . -year)
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{models}
%\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
vcdExtra/man/Alligator.Rd 0000644 0001762 0000144 00000004105 13164013351 015007 0 ustar ligges users \name{Alligator}
\alias{Alligator}
\docType{data}
\title{
Alligator Food Choice
}
\description{
The Alligator data, from Agresti (2002), comes from a study of the primary food choices of alligators
in four Florida lakes. Researchers classified the stomach contents of 219 captured alligators into five categories:
Fish (the most common primary food choice), Invertebrate (snails, insects, crayfish, etc.),
Reptile (turtles, alligators), Bird, and Other (amphibians, plants, household pets, stones, and other debris).
}
\usage{data(Alligator)}
\format{
A frequency data frame with 80 observations on the following 5 variables.
\describe{
\item{\code{lake}}{a factor with levels \code{George} \code{Hancock} \code{Oklawaha} \code{Trafford}}
\item{\code{sex}}{a factor with levels \code{female} \code{male}}
\item{\code{size}}{alligator size, a factor with levels \code{large} (>2.3m) \code{small} (<=2.3m)}
\item{\code{food}}{primary food choice, a factor with levels \code{bird} \code{fish} \code{invert} \code{other} \code{reptile}}
\item{\code{count}}{cell frequency, a numeric vector}
}
}
\details{
The table contains a fair number of 0 counts.
\code{food} is the response variable. \code{fish} is the most frequent choice, and often
taken as a baseline category in multinomial response models.
}
\source{
Agresti, A. (2002). \emph{Categorical Data Analysis}, New York: Wiley, 2nd Ed., Table 7.1
}
%\references{
%%% ~~ possibly secondary sources and usages ~~
%}
\examples{
data(Alligator)
# change from frequency data.frame to table
allitable <- xtabs(count~lake+sex+size+food, data=Alligator)
# Agresti's Table 7.1
structable(food~lake+sex+size, allitable)
plot(allitable, shade=TRUE)
# mutual independence model
mosaic(~food+lake+size, allitable, shade=TRUE)
# food jointly independent of lake and size
mosaic(~food+lake+size, allitable, shade=TRUE, expected=~lake:size+food)
if (require(nnet)) {
# multinomial logit model
mod1 <- multinom(food ~ lake+size+sex, data=Alligator, weights=count)
}
}
\keyword{datasets}
vcdExtra/man/Crossings.Rd 0000644 0001762 0000144 00000003666 13164013351 015056 0 ustar ligges users \name{Crossings}
\alias{Crossings}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Crossings Interaction of Factors
}
\description{
Given two ordered factors in a square, n x n frequency table, \code{Crossings}
creates an n-1 column matrix corresponding to different degrees of difficulty
in crossing from one level to the next, as described by Goodman (1972).
}
\usage{
Crossings(...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{\dots}{
Two factors
}
}
%\details{
%% ~~ If necessary, more details than the description above ~~
%}
\value{
For two factors of \code{n} levels, returns a binary indicator matrix
of \code{n*n} rows and \code{n-1} columns.
}
\references{
Goodman, L. (1972). Some multiplicative models for the analysis of cross-classified data.
In: \emph{Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability},
Berkeley, CA: University of California Press, pp. 649-696.
}
\author{
Michael Friendly and Heather Turner
}
%\note{
%% ~~further notes~~
%}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
\code{\link[stats]{glm}},
\code{\link[gnm]{gnm}} for model fitting functions for frequency tables
\code{\link[gnm]{Diag}},
\code{\link[gnm]{Mult}},
\code{\link[gnm]{Symm}},
\code{\link[gnm]{Topo}}
for similar extensions to terms in model formulas.
}
\examples{
data(Hauser79)
# display table
structable(~Father+Son, data=Hauser79)
hauser.indep <- gnm(Freq ~ Father + Son, data=Hauser79, family=poisson)
hauser.CR <- update(hauser.indep, ~ . + Crossings(Father,Son))
LRstats(hauser.CR)
hauser.CRdiag <- update(hauser.indep, ~ . + Crossings(Father,Son) + Diag(Father,Son))
LRstats(hauser.CRdiag)
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{models}
\keyword{manip}% __ONLY ONE__ keyword per line
vcdExtra/man/HospVisits.Rd 0000644 0001762 0000144 00000002616 13164013351 015211 0 ustar ligges users \name{HospVisits}
\alias{HospVisits}
\docType{data}
\title{
Hospital Visits Data
}
\description{
Length of stay in hospital for 132 schizophrenic patients, classified by
visiting patterns, originally from Wing (1962).
}
\usage{data("HospVisits")}
\format{
A 3 by 3 frequency table, with format:
table [1:3, 1:3] 43 6 9 16 11 18 3 10 16
- attr(*, "dimnames")=List of 2
..$ visit: chr [1:3] "Regular" "Infrequent" "Never"
..$ stay : chr [1:3] "2-9" "10-19" "20+"
}
\details{
Both table variables can be considered ordinal.
The variable \code{visit} refers to visiting patterns recorded hospital.
The category labels are abbreviations of those given by Goodman (1983);
e.g., \code{"Regular"} is short for \dQuote{received visitors regularly or patient went home}.
The variable \code{stay} refers to length of stay in hospital, in year groups.
}
\source{
Goodman, L. A. (1983) The analysis of dependence in cross-classifications having ordered categories, using log-linear models for frequencies and log-linear models for odds.
\emph{Biometrics}, 39, 149-160.
}
\references{
Wing, J. K. (1962). Institutionalism in Mental Hospitals,
\emph{British Journal of Social and Clinical Psychology}, 1 (1), 38-51.
}
\examples{
data(HospVisits)
mosaic(HospVisits, gp=shading_Friendly)
library(ca)
ca(HospVisits)
# surprisingly 1D !
plot(ca(HospVisits))
}
\keyword{datasets}
vcdExtra/man/Vote1980.Rd 0000644 0001762 0000144 00000003030 13164013351 014324 0 ustar ligges users \name{Vote1980}
\alias{Vote1980}
\docType{data}
\title{
Race and Politics in the 1980 Presidential Vote
}
\description{
Data from the 1982 General Social Survey on votes in the 1980 U.S.
presidential election in relation to race and political
conservatism.
}
\usage{data(Vote1980)}
\format{
A frequency data frame representing a 2 x 7 x 2 table, with 28 observations on the following 4 variables.
\describe{
\item{\code{race}}{a factor with levels \code{NonWhite} \code{White}}
\item{\code{conservatism}}{
a factor with levels \code{1} \code{2} \code{3} \code{4} \code{5} \code{6} \code{7},
\code{1}=most liberal, \code{7}=most conservative}
\item{\code{votefor}}{a factor with levels \code{Carter} \code{Reagan}; \code{Carter}
represents Jimmy Carter or other.}
\item{\code{Freq}}{a numeric vector}
}
}
\details{
The data contains a number of sampling zeros in the frequencies of
NonWhites voting for Ronald Reagan.
}
\source{
Clogg, C. & Shockey, J. W. (1988).
In Nesselroade, J. R. & Cattell, R. B. (ed.)
Multivariate Analysis of Discrete Data,
\emph{Handbook of Multivariate Experimental Psychology},
New York: Plenum Press.
}
\references{
Agresti, A. (1990)
\emph{Categorical Data Analysis}, Table 4.12
New York: Wiley-Interscience.
Friendly, M. (2000)
\emph{Visualizing Categorical Data}, Example 7.5
Cary, NC: SAS Institute.
}
\examples{
data(Vote1980)
fourfold(xtabs(Freq ~ race + votefor + conservatism, data=Vote1980), mfrow=c(2,4))
}
\keyword{datasets}
vcdExtra/DESCRIPTION 0000644 0001762 0000144 00000003604 14202500462 013536 0 ustar ligges users Package: vcdExtra
Type: Package
Title: 'vcd' Extensions and Additions
Version: 0.7-6
Date: 2022-02-12
Language: en-US
Authors@R: c(person(given = "Michael", family = "Friendly", role=c("aut", "cre"), email="friendly@yorku.ca"),
person(given = "Heather", family = "Turner", role="ctb"),
person(given = "Achim", family = "Zeileis", role="ctb"),
person(given = "Duncan", family = "Murdoch", role="ctb"),
person(given = "David", family = "Firth", role="ctb"),
person(given = "Matt", family = "Kumar", role="ctb"),
person(given = "Shuguang", family = "Sun", role="ctb")
)
Author: Michael Friendly [aut, cre],
Heather Turner [ctb],
Achim Zeileis [ctb],
Duncan Murdoch [ctb],
David Firth [ctb],
Matt Kumar [ctb],
Shuguang Sun [ctb]
Maintainer: Michael Friendly
Depends: R (>= 2.10), vcd, gnm (>= 1.0-3), grid
Suggests: gmodels, Fahrmeir, effects, VGAM, plyr, lmtest, nnet,
ggplot2, Sleuth2, car, lattice, stats4, rgl, AER
Imports: MASS, grDevices, stats, utils, ca
Description: Provides additional data sets, methods and documentation to complement the 'vcd' package for Visualizing Categorical Data
and the 'gnm' package for Generalized Nonlinear Models.
In particular, 'vcdExtra' extends mosaic, assoc and sieve plots from 'vcd' to handle 'glm()' and 'gnm()' models and
adds a 3D version in 'mosaic3d'. Additionally, methods are provided for comparing and visualizing lists of
'glm' and 'loglm' objects. This package is now a support package for the book, "Discrete Data Analysis with R" by
Michael Friendly and David Meyer.
License: GPL (>= 2)
URL: https://CRAN.R-project.org/package=vcdExtra
BugReports: https://github.com/friendly/vcdExtra
LazyLoad: yes
LazyData: yes
NeedsCompilation: no
Packaged: 2022-02-14 15:45:07 UTC; friendly
Repository: CRAN
Date/Publication: 2022-02-14 16:20:02 UTC
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{];U(-
}+s ;k!]'~\pqWN?$מU%nvC$dC`쉽 m*ȩּj vcdExtra/vignettes/ 0000755 0001762 0000144 00000000000 14202474400 014037 5 ustar ligges users vcdExtra/vignettes/vcdExtra.bib 0000644 0001762 0000144 00000001137 13164013351 016277 0 ustar ligges users @ARTICLE{Cohen:60,
author = {J. Cohen},
title = {A coefficient of agreement for nominal scales},
journal = {Educational and Psychological Measurement},
year = {1960},
volume = {20},
pages = {37--46},
owner = {Michael},
timestamp = {2009.01.21}
}
@BOOK{Agresti:2013,
title = {Categorical Data Analysis},
publisher = {Wiley-Interscience [John Wiley \& Sons]},
year = {2013},
author = {Agresti, Alan},
series = {Wiley Series in Probability and Statistics},
address = {New York},
edition = {Third},
isbn = {978-0-470-46363-5},
lccn = {QA278.A353 2013}
}
vcdExtra/vignettes/vcd-tutorial.Rnw 0000644 0001762 0000144 00000234347 14202271213 017156 0 ustar ligges users % !Rnw weave = Sweave
%\VignetteEngine{Sweave}
%\VignetteIndexEntry{Tutorial: Working with categorical data with R and the vcd package}
%\VignetteDepends{vcd,gmodels,ca}
%\VignetteKeywords{contingency tables, mosaic plots, sieve plots, categorical data, independence, conditional independence, R}
%\VignettePackage{vcdExtra}
\documentclass[10pt,twoside]{article}
\usepackage{Sweave}
\usepackage{bm}
\usepackage[toc]{multitoc} % for table of contents
% from Z.cls
\usepackage[authoryear,round,longnamesfirst]{natbib}
\bibpunct{(}{)}{;}{a}{}{,}
\bibliographystyle{jss}
\usepackage{hyperref}
\usepackage{color}
%% colors
\definecolor{Red}{rgb}{0.7,0,0}
\definecolor{Blue}{rgb}{0,0,0.8}
\hypersetup{%
hyperindex = {true},
colorlinks = {true},
% linktocpage = {true},
plainpages = {false},
linkcolor = {Blue},
citecolor = {Blue},
urlcolor = {Red},
pdfstartview = {Fit},
pdfpagemode = {UseOutlines},
pdfview = {XYZ null null null}
}
%\AtBeginDocument{
% \hypersetup{%
% pdfauthor = {Michael Friendly},
% pdftitle = {Tutorial: Working with categorical data with R and the vcd package},
% pdfkeywords = {contingency tables, mosaic plots, sieve plots, categorical data, independence, conditional independence, R}
% }
%}
% math stuff
\newcommand*{\given}{\ensuremath{\, | \,}}
\renewcommand*{\vec}[1]{\ensuremath{\bm{#1}}}
\newcommand{\mat}[1]{\ensuremath{\bm{#1}}}
\newcommand{\trans}{\ensuremath{^\mathsf{T}}}
\newcommand{\diag}[1]{\ensuremath{\mathrm{diag} (#1)}}
\def\binom#1#2{{#1 \choose #2}}%
\newcommand{\implies}{ \ensuremath{\mapsto} }
\newenvironment{equation*}{\displaymath}{\enddisplaymath}%
\newcommand{\tabref}[1]{Table~\ref{#1}}
\newcommand{\figref}[1]{Figure~\ref{#1}}
\newcommand{\secref}[1]{Section~\ref{#1}}
\newcommand{\loglin}{loglinear }
%\usepackage{thumbpdf}
% page dimensions
\addtolength{\hoffset}{-1.5cm}
\addtolength{\textwidth}{3cm}
\addtolength{\voffset}{-1cm}
\addtolength{\textheight}{2cm}
% Vignette examples
\newcommand*{\Example}{\fbox{\textbf{\emph{Example}}:} }
% R stuff
\newcommand{\var}[1]{\textit{\texttt{#1}}}
\newcommand{\data}[1]{\texttt{#1}}
\newcommand{\class}[1]{\textsf{"#1"}}
%% \code without `-' ligatures
\def\nohyphenation{\hyphenchar\font=-1 \aftergroup\restorehyphenation}
\def\restorehyphenation{\hyphenchar\font=`-}
{\catcode`\-=\active%
\global\def\code{\bgroup%
\catcode`\-=\active \let-\codedash%
\Rd@code}}
\def\codedash{-\discretionary{}{}{}}
\def\Rd@code#1{\texttt{\nohyphenation#1}\egroup}
\newcommand{\codefun}[1]{\code{#1()}}
\let\proglang=\textsf
\newcommand{\pkg}[1]{{\normalfont\fontseries{b}\selectfont #1}}
\newcommand{\Rpackage}[1]{{\textsf{#1}}}
%% almost as usual
\author{Michael Friendly\\York University, Toronto}
\title{Working with categorical data with \proglang{R} and the \pkg{vcd} and \pkg{vcdExtra} packages}
\date{\footnotesize{Using \Rpackage{vcdExtra} version \Sexpr{packageDescription("vcdExtra")[["Version"]]}
and \Rpackage{vcd} version \Sexpr{packageDescription("vcd")[["Version"]]}; Date: \Sexpr{Sys.Date()}}}
%% for pretty printing and a nice hypersummary also set:
%\Plainauthor{Michael Friendly} %% comma-separated
%\Shorttitle{vcd tutorial} %% a short title (if necessary)
%\Plaintitle{Tutorial: Working with categorical data with R and the vcd package}
%\SweaveOpts{engine=R,eps=TRUE,height=6,width=7,results=hide,fig=FALSE,echo=TRUE}
\SweaveOpts{engine=R,height=6,width=7,results=hide,fig=FALSE,echo=TRUE}
\SweaveOpts{prefix.string=fig/vcd-tut,eps=FALSE}
\SweaveOpts{keep.source=TRUE}
%\SweaveOpts{concordance=TRUE}
\setkeys{Gin}{width=0.7\textwidth}
<>=
set.seed(1071)
#library(vcd)
library(vcdExtra)
library(ggplot2)
#data(Titanic)
data(HairEyeColor)
data(PreSex)
data(Arthritis)
art <- xtabs(~Treatment + Improved, data = Arthritis)
if(!file.exists("fig")) dir.create("fig")
@
%% end of declarations %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{document}
\SweaveOpts{concordance=TRUE}
\maketitle
%% an abstract and keywords
\begin{abstract}
This tutorial describes the creation and manipulation of frequency and contingency tables
from categorical variables, along with tests of independence, measures
of association, and methods for graphically displaying results.
The framework is provided by the \proglang{R} package \pkg{vcd},
but other packages are used to help with various tasks.
The \pkg{vcdExtra} package extends the graphical and statistical methods provided by
\pkg{vcd}.
This package is now the main support package for the book
\emph{Discrete Data Analysis with R: Visualizing and Modeling Techniques for Categorical
and Count Data} \citep{FriendlyMeyer:2016:DDAR}. The web page for the book,
\href{http://ddar.datavis.ca}{ddar.datavis.ca}, gives further details.
\end{abstract}
%\keywords{contingency tables, mosaic plots, sieve plots,
%categorical data, independence, conditional independence, generalized linear models,
%\proglang{R}}
%\Plainkeywords{contingency tables, mosaic plots,
% sieve plots, categorical data, independence,
% conditional independence, generalized linear models, R}
{\small
% \sloppy
% \begin{multicols}{2}
\tableofcontents
% \end{multicols}
}
\section[Introduction]{Introduction}\label{sec:intro}
%% Note: If there is markup in \(sub)section, then it has to be escape as above.
This tutorial, part of the \pkg{vcdExtra} package,
describes how to work with categorical data in the context of
fitting statistical models in \proglang{R} and visualizing
the results using the \pkg{vcd} and \pkg{vcdExtra} packages.
It focuses first on methods and tools for creating
and manipulating \proglang{R} data objects which represent
frequency and contingency tables involving
categorical variables.
Further sections describe some simple methods for calculating
tests of independence and measures of association among
categorical variables, and also
methods for graphically displaying results.
There is much more to the analysis of categorical data than is described here,
where the emphasis is on cross-tabulated tables of frequencies (``contingency tables''),
statistical tests, associated \loglin\ models, and visualization of \emph{how}
variables are related.
A more general treatment of graphical methods for categorical data is contained
in the book, \emph{Discrete Data Analysis with R: Visualizing and Modeling Techniques for Categorical
and Count Data} \citep{FriendlyMeyer:2016:DDAR}.
An earlier book using SAS is
\emph{Visualizing Categorical Data} \citep{vcd:Friendly:2000}, for which
\pkg{vcd} is a partial \proglang{R} companion, covering topics not otherwise
available in \proglang{R}.
On the other hand, the implementation of graphical
methods in \pkg{vcd} is more general in many respects than what I provided in
\proglang{SAS}. Statistical models for categorical data in \proglang{R} have
been extended considerably with the \pkg{gnm} package for generalized \emph{nonlinear}
models. The \pkg{vcdExtra} package extends \pkg{vcd} methods to models fit using
\codefun{glm} and \codefun{gnm}.
A more complete theoretical description of these statistical
methods is provided in Agresti's \citeyearpar{vcd:Agresti:2002,Agresti:2013}
\emph{Categorical Data Analysis}. For this, see
the \proglang{Splus/R} companion by Laura Thompson,
\url{http://www.stat.ufl.edu/~aa/cda/Thompson_manual.pdf}
and Agresti's support web page,
\url{http://www.stat.ufl.edu/~aa/cda/cda.html}.
\section[Creating frequency tables]{Creating and manipulating frequency tables}\label{sec:creating}
\proglang{R} provides many methods for creating frequency and contingency tables. Several are
described below. In the examples below, we use some real examples and some anonymous
ones, where the variables \code{A}, \code{B}, and \code{C} represent
categorical variables, and \code{X} represents an arbitrary \proglang{R} data object.
The first thing you need to know is that categorical data can be represented in
three different forms in \proglang{R}, and it is sometimes necessary to convert
from one form to another, for carrying out statistical tests, fitting models
or visualizing the results. Once a data object exists in \proglang{R},
you can examine its complete structure with
the \codefun{str} function, or view the names of its components with the
\codefun{names} function.
\begin{description}
\item[case form] a data frame containing individual observations, with one or
more factors, used as the classifying variables. In case form, there may also
be numeric covariates.
The total number of observations
is \code{nrow(X)}, and the number of variables is \code{ncol(X)}.
\Example
The \data{Arthritis} data is available in case form in the \pkg{vcd} package.
There are two explanatory factors: \code{Treatment} and \code{Sex}. \code{Age}
is a numeric covariate, and \code{Improved} is the response--- an ordered factor,
with levels
\code{\Sexpr{paste(levels(Arthritis$Improved),collapse=' < ')}}.
Excluding \code{Age}, we would have
a $2 \times 2 \times 3$ contingency table for \code{Treatment}, \code{Sex} and \code{Improved}.
%\code{"None" < "Some" < "Marked"}.
<>=
names(Arthritis) # show the variables
str(Arthritis) # show the structure
head(Arthritis,5) # first 5 observations, same as Arthritis[1:5,]
@
\item[frequency form] a data frame containing one or more factors, and a frequency
variable, often called \code{Freq} or \code{count}. The total number of observations
is
\verb|sum(X$Freq)|,
\code{sum(X[,"Freq"])} or some equivalent form.
The number of cells in the table is \code{nrow(X)}.
\Example
For small frequency tables, it is often convenient to enter them in frequency form
using \codefun{expand.grid} for the factors and \codefun{c} to list the counts in a vector.
The example below, from \cite{vcd:Agresti:2002} gives results for the 1991 General Social Survey,
with respondents classified by sex and party identification.
<>=
# Agresti (2002), table 3.11, p. 106
GSS <- data.frame(
expand.grid(sex=c("female", "male"),
party=c("dem", "indep", "rep")),
count=c(279,165,73,47,225,191))
GSS
names(GSS)
str(GSS)
sum(GSS$count)
@
\item[table form] a matrix, array or table object, whose elements are the frequencies
in an $n$-way table. The variable names (factors) and their levels are given by
\code{dimnames(X)}. The total number of observations
is \code{sum(X)}. The number of dimensions of the table is \code{length(dimnames(X))},
and the table sizes are given by \code{sapply(dimnames(X), length)}.
\Example
The \data{HairEyeColor} is stored in table form in \pkg{vcd}.
<>=
str(HairEyeColor) # show the structure
sum(HairEyeColor) # number of cases
sapply(dimnames(HairEyeColor), length) # table dimension sizes
@
\Example
Enter frequencies in a matrix, and assign \code{dimnames},
giving the variable names and category labels. Note that, by default,
\codefun{matrix} uses the elements supplied by \emph{columns} in the
result, unless you specify \code{byrow=TRUE}.
<>=
## A 4 x 4 table Agresti (2002, Table 2.8, p. 57) Job Satisfaction
JobSat <- matrix(c(1,2,1,0, 3,3,6,1, 10,10,14,9, 6,7,12,11), 4, 4)
dimnames(JobSat) = list(income=c("< 15k", "15-25k", "25-40k", "> 40k"),
satisfaction=c("VeryD", "LittleD", "ModerateS", "VeryS"))
JobSat
@
\data{JobSat} is a matrix, not an object of \code{class("table")}, and some functions
are happier with tables than matrices.
You can coerce it to a table with \codefun{as.table},
<>=
JobSat <- as.table(JobSat)
str(JobSat)
@
\end{description}
\subsection[Ordered factors]{Ordered factors and reordered tables}\label{sec:ordered-factors}
In table form, the values of the table factors are ordered by their position in the table.
Thus in the \data{JobSat} data, both \code{income} and \code{satisfaction} represent ordered
factors, and the \emph{positions} of the values in the rows and columns reflects their
ordered nature.
Yet, for analysis, there are time when you need \emph{numeric} values for the levels
of ordered factors in a table, e.g., to treat a factor as a quantitative variable.
In such cases, you can simply re-assign the \code{dimnames} attribute of the table
variables. For example, here, we assign numeric values to \code{income} as the middle of their
ranges, and treat \code{satisfaction} as equally spaced with integer scores.
<>=
dimnames(JobSat)$income<-c(7.5,20,32.5,60)
dimnames(JobSat)$satisfaction<-1:4
@
For the \data{HairEyeColor} data, hair color and eye color are ordered arbitrarily.
For visualizing the data using mosaic plots and other methods described below, it
turns out to be more useful to assure that both hair color and eye color are
ordered from dark to light.
Hair colors are actually ordered this way already, and it is easiest to re-order
eye colors by indexing. Again \codefun{str} is your friend.
<>=
HairEyeColor <- HairEyeColor[, c(1,3,4,2), ]
str(HairEyeColor)
@
This is also the order for both hair color and eye color shown in
the result of a correspondence analysis (\figref{fig:ca-haireye}) below.
With data in case form or frequency form, when you have ordered factors
represented with character values, you must ensure that they are treated
as ordered in \proglang{R}.%
\footnote{In \proglang{SAS}, many procedures offer the option
\code{order = data | internal | formatted} to allow character values
to be ordered according to (a) their order in the data set, (b)
sorted internal value, or (c) sorted formatted representation
provided by a \proglang{SAS} format.
}
Imagine that the \data{Arthritis} data was read from a text file.
By default the \code{Improved} will be ordered alphabetically:
\code{Marked},
\code{None},
\code{Some}--- not what we want. In this case, the function
\codefun{ordered} (and others) can be useful.
<>=
Arthritis <- read.csv("arthritis.txt",header=TRUE)
Arthritis$Improved <- ordered(Arthritis$Improved, levels=c("None", "Some", "Marked"))
@
With this order of \code{Improved}, the response in this data,
a mosaic display of \code{Treatment} and \code{Improved} (\figref{fig:arthritis})shows a clearly
interpretable pattern.
<>=
mosaic(art, gp = shading_max, split_vertical = TRUE, main="Arthritis: [Treatment] [Improved]")
@
%\setkeys{Gin}{width=0.7\textwidth}
\begin{figure}[htb]
\begin{center}
%<>=
%mosaic(art, gp = shading_max, split_vertical = TRUE, main="Arthritis: [Treatment] [Improved]")
%@
\includegraphics[width=0.7\textwidth]{fig/vcd-tut-Arthritis}
\caption{Mosaic plot for the \data{Arthritis} data, showing the marginal model of independence
for Treatment and Improved. Age, a covariate, and Sex are ignored here.}
\label{fig:arthritis}
\end{center}
\end{figure}
Finally, there are situations where, particularly for display purposes, you
want to re-order the \emph{dimensions} of an $n$-way table, or change the
labels for the variables or levels.
This is easy when the data are in table form: \codefun{aperm} permutes
the dimensions, and assigning to \code{names} and \code{dimnames}
changes variable names and level labels respectively.
We will use the following version of \data{UCBAdmissions} in
\secref{sec:mantel} below.%
\footnote{
Changing \code{Admit} to \code{Admit?} might be useful for display purposes, but is
dangerous--- because it is then
difficult to use that variable name in a model formula.
See \secref{sec:tips} for options \code{labeling\_args} and \code{set\_labels}
to change variable and level names
for displays in the \code{strucplot} framework.
}
<>=
UCB <- aperm(UCBAdmissions, c(2, 1, 3))
dimnames(UCB)[[2]] <- c("Yes", "No")
names(dimnames(UCB)) <- c("Sex", "Admit?", "Department")
ftable(UCB)
@
%There is one subtle ``gotcha'' here: \codefun{aperm} returns an object of class \class{"array"},
%whereas \data{UCBAdmissions} is of class \class{"table"}, so methods defined for \code{table}
%objects will not work on the permuted array.
%The solution is to reassign the \code{class} of the result of \codefun{aperm}.
%
%<>=
%class(UCBAdmissions)
%class(UCB)
%str(as.data.frame(UCBAdmissions)) # OK
%str(as.data.frame(UCB)) # wrong
%
%class(UCB) <- "table"
%str(as.data.frame(UCB)) # now OK
%@
%
\subsection[structable()]{\codefun{structable}}\label{sec:structable}
For 3-way and larger tables
the \codefun{structable} function in \pkg{vcd} provides a convenient and flexible tabular display.
The variables assigned to the rows and columns of a two-way display can be specified
by a model formula.
<>=
structable(HairEyeColor) # show the table: default
structable(Hair+Sex ~ Eye, HairEyeColor) # specify col ~ row variables
@
It also returns an object of class \code{"structable"} which may be plotted with
\codefun{mosaic} (not shown here).
<>=
HSE < - structable(Hair+Sex ~ Eye, HairEyeColor) # save structable object
mosaic(HSE) # plot it
@
\subsection[table() and friends]{\codefun{table} and friends}\label{sec:table}
You can generate frequency tables from factor variables using the \codefun{table} function, tables of
proportions using the \codefun{prop.table} function, and marginal frequencies using
\codefun{margin.table}.
<>=
n=500
A <- factor(sample(c("a1","a2"), n, rep=TRUE))
B <- factor(sample(c("b1","b2"), n, rep=TRUE))
C <- factor(sample(c("c1","c2"), n, rep=TRUE))
mydata <- data.frame(A,B,C)
@
<>=
# 2-Way Frequency Table
attach(mydata)
mytable <- table(A,B) # A will be rows, B will be columns
mytable # print table
margin.table(mytable, 1) # A frequencies (summed over B)
margin.table(mytable, 2) # B frequencies (summed over A)
prop.table(mytable) # cell percentages
prop.table(mytable, 1) # row percentages
prop.table(mytable, 2) # column percentages
@
\codefun{table} can also generate multidimensional tables based on 3 or more
categorical variables. In this case, use the \codefun{ftable} or \codefun{structable}
function to print the
results more attractively.
<>=
# 3-Way Frequency Table
mytable <- table(A, B, C)
ftable(mytable)
@
\codefun{table} ignores missing values by default.
To include \code{NA} as a category in counts, include the
table option \code{exclude=NULL} if the variable is a vector. If the variable is a
factor you have to create a new factor using \code{newfactor <- factor(oldfactor,
exclude=NULL)}.
\subsection[xtabs()]{\codefun{xtabs}}\label{sec:xtabs}
The \codefun{xtabs} function allows you to create cross-tabulations of data using formula style input.
This typically works with case-form data supplied in a data frame or a matrix.
The result is a contingency table in array format, whose dimensions are determined by
the terms on the right side of the formula.
<>=
# 3-Way Frequency Table
mytable <- xtabs(~A+B+C, data=mydata)
ftable(mytable) # print table
summary(mytable) # chi-square test of indepedence
@
If a variable is included on the left side of the formula, it is assumed to be a
vector of frequencies (useful if the data have already been tabulated in frequency form).
<>=
(GSStab <- xtabs(count ~ sex + party, data=GSS))
summary(GSStab)
@
\subsection[Collapsing over factors]{Collapsing over table factors: \codefun{aggregate}, \codefun{margin.table} and \codefun{apply}}
It sometimes happens that we have a data set with more variables or factors than
we want to analyse, or else, having done some initial analyses, we decide that
certain factors are not important, and so should be excluded from graphic displays
by collapsing (summing) over them. For example, mosaic plots and fourfold displays
are often simpler to construct from versions of the data collapsed over
the factors which are not shown in the plots.
The appropriate tools to use again depend on
the form in which the data are represented--- a case-form data frame, a
frequency-form data frame (\codefun{aggregate}), or a table-form array or
table object (\codefun{margin.table} or \codefun{apply}).
When the data are in frequency form, and we want to produce another
frequency data frame, \codefun{aggregate} is a handy tool, using
the argument \code{FUN=sum} to sum the frequency variable over the
factors \emph{not} mentioned in the formula.
\Example
The data frame \data{DaytonSurvey} in the \pkg{vcdExtra} package represents a
$2^5$ table giving the frequencies of reported use (``ever used?'') of
alcohol, cigarettes and marijuana in a sample of high school seniors,
also classified by sex and race.
<>=
str(DaytonSurvey)
head(DaytonSurvey)
@
To focus on the associations among the
substances, we want to collapse over sex and race. The right-hand side of the formula
used in the call to \codefun{aggregate} gives the factors to be retained in the
new frequency data frame, \code{Dayton.ACM.df}.
<>=
# data in frequency form
# collapse over sex and race
Dayton.ACM.df <- aggregate(Freq ~ cigarette+alcohol+marijuana,
data=DaytonSurvey, FUN=sum)
Dayton.ACM.df
@
When the data are in table form, and we want to produce another
table, \codefun{apply} with \code{FUN=sum} can be used in a similar way
to sum the table over dimensions not mentioned in the \code{MARGIN}
argument. \codefun{margin.table} is just a wrapper for \codefun{apply}
using the \codefun{sum} function.
\Example
To illustrate, we first convert the \data{DaytonSurvey} to a 5-way
table using \codefun{xtabs}, giving \code{Dayton.tab}.
<>==
# in table form
Dayton.tab <- xtabs(Freq~cigarette+alcohol+marijuana+sex+race, data=DaytonSurvey)
structable(cigarette+alcohol+marijuana ~ sex+race, data=Dayton.tab)
@
Then, use \codefun{apply} on \code{Dayton.tab} to give the
3-way table \code{Dayton.ACM.tab} summed over sex and race.
The elements in this new table are the column sums for
\code{Dayton.tab} shown by \codefun{structable} just above.
<>==
# collapse over sex and race
Dayton.ACM.tab <- apply(Dayton.tab, MARGIN=1:3, FUN=sum)
Dayton.ACM.tab <- margin.table(Dayton.tab, 1:3) # same result
structable(cigarette+alcohol ~ marijuana, data=Dayton.ACM.tab)
@
Many of these operations can be performed using the \verb|**ply()| functions
in the \pkg{plyr} package.
For example, with the data in a frequency form data frame, use \codefun{ddply}
to collapse over unmentioned factors, and \codefun{plyr::summarise}%
\footnote{
Ugh. This \pkg{plyr} function clashes with a function of the same name in \pkg{vcdExtra}.
In this document I will use the explicit double-colon notation to keep them
separate.
}
as the function to be applied to each piece.
<>==
Dayton.ACM.df <- ddply(DaytonSurvey, .(cigarette, alcohol, marijuana),
plyr::summarise, Freq=sum(Freq))
@
\subsection[Collapsing levels]{Collapsing table levels: \codefun{collapse.table}}
A related problem arises when we have a table or array and for some purpose
we want to reduce the number of levels of some factors by summing subsets
of the frequencies. For example, we may have initially coded Age in 10-year
intervals, and decide that, either for analysis or display purposes, we
want to reduce Age to 20-year intervals. The \codefun{collapse.table} function
in \pkg{vcdExtra} was designed for this purpose.
\Example
Create a 3-way table, and collapse Age from 10-year to 20-year intervals.
First, we generate a $2 \times 6 \times 3$ table of random counts from a
Poisson distribution with mean of 100.
<>=
# create some sample data in frequency form
sex <- c("Male", "Female")
age <- c("10-19", "20-29", "30-39", "40-49", "50-59", "60-69")
education <- c("low", 'med', 'high')
data <- expand.grid(sex=sex, age=age, education=education)
counts <- rpois(36, 100) # random Possion cell frequencies
data <- cbind(data, counts)
# make it into a 3-way table
t1 <- xtabs(counts ~ sex + age + education, data=data)
structable(t1)
@
Now collapse \code{age} to 20-year intervals, and \code{education}
to 2 levels. In the arguments, levels of \code{age} and \code{education}
given the same label are summed in the resulting smaller table.
<>=
# collapse age to 3 levels, education to 2 levels
t2 <- collapse.table(t1,
age=c("10-29", "10-29", "30-49", "30-49", "50-69", "50-69"),
education=c(">=
as.data.frame(GSStab)
@
\Example Convert the \code{Arthritis} data in case form to a 3-way table of
\code{Treatment} $\times$ \code{Sex} $\times$ \code{Improved}.
Note the use of \codefun{with} to avoid having to use \code{Arthritis\$Treatment} etc. within the call to \codefun{table}.%
\footnote{
\codefun{table} does not allow a \code{data} argument to provide
an environment in which the table variables are to be found. In the
examples in \secref{sec:table} I used \code{attach(mydata)} for this purpose,
but \codefun{attach} leaves the variables in the global environment,
while \codefun{with} just evaluates the \codefun{table} expression in a
temporary environment of the data.
}
<>=
Art.tab <-with(Arthritis, table(Treatment, Sex, Improved))
str(Art.tab)
ftable(Art.tab)
@
There may also be times that you will need an equivalent case form \code{data.frame}
with factors representing the table variables
rather than the frequency table.
For example, the \codefun{mca} function in package \pkg{MASS}
only operates on data in this format.
Marc Schwartz provided code for \codefun{expand.dft} on the Rhelp
mailing list for converting a table back into a case form \code{data.frame}.
This function is included in \pkg{vcdExtra}.
\Example Convert the \data{Arthritis} data in table form (\code{Art.tab}) back to a \code{data.frame}
in case form, with factors
\code{Treatment}, \code{Sex} and \code{Improved}.
<>=
Art.df <- expand.dft(Art.tab)
str(Art.df)
@
\subsection{A complex example}\label{sec:complex}
If you've followed so far, you're ready for a more complicated example.
The data file, \code{tv.dat} represents a 4-way table of size
$5 \times 11 \times 5 \times 3$ where the table variables (unnamed in the file)
are read as \code{V1} -- \code{V4}, and the cell frequency is read
as \code{V5}. The file, stored in the \code{doc/extdata} directory
of \pkg{vcdExtra}, can be read as follows:
<>=
tv.data<-read.table(system.file("extdata","tv.dat", package="vcdExtra"))
head(tv.data,5)
@
For a local file, just use \codefun{read.table} in this form:
<>=
tv.data<-read.table("C:/R/data/tv.dat")
@
The data \code{tv.dat} came from the initial implementation of
mosaic displays in \proglang{R} by Jay Emerson.
In turn, they came from the initial development of mosaic displays
\citep{vcd:Hartigan+Kleiner:1984}
that illustrated the method with data on a large sample of TV viewers
whose behavior had been recorded for the Neilson ratings.
This data set contains sample television audience data from Neilsen
Media Research for the week starting November 6, 1995.
\begin{flushleft}
The table variables are:\\
~~~\code{V1}-- values 1:5 correspond to the days Monday--Friday;\\
~~~\code{V2}-- values 1:11 correspond to the quarter hour times 8:00PM through 10:30PM;\\
~~~\code{V3}-- values 1:5 correspond to ABC, CBS, NBC, Fox, and non-network choices;\\
~~~\code{V4}-- values 1:3 correspond to transition states: turn the television Off, Switch channels,
or Persist in viewing the current channel.
\end{flushleft}
We are interested just the cell frequencies, and rely on the facts that the
(a) the table is complete--- there are no missing cells,
so \code{nrow(tv.data)}=\Sexpr{nrow(tv.data)};
(b) the observations are ordered so that \code{V1} varies most rapidly and
\code{V4} most slowly. From this, we can just extract the frequency column
and reshape it into an array.
<>=
TV <- array(tv.data[,5], dim=c(5,11,5,3))
dimnames(TV) <- list(c("Monday","Tuesday","Wednesday","Thursday","Friday"),
c("8:00","8:15","8:30","8:45","9:00","9:15","9:30",
"9:45","10:00","10:15","10:30"),
c("ABC","CBS","NBC","Fox","Other"), c("Off","Switch","Persist"))
names(dimnames(TV))<-c("Day", "Time", "Network", "State")
@
More generally (even if there are missing cells), we can
use \codefun{xtabs} (or \codefun{plyr::daply})
to do the cross-tabulation, using \code{V5} as the
frequency variable. Here's how to do this same operation with \codefun{xtabs}:
<>=
TV <- xtabs(V5 ~ ., data=tv.data)
dimnames(TV) <- list(Day=c("Monday","Tuesday","Wednesday","Thursday","Friday"),
Time=c("8:00","8:15","8:30","8:45","9:00","9:15","9:30",
"9:45","10:00","10:15","10:30"),
Network=c("ABC","CBS","NBC","Fox","Other"),
State=c("Off","Switch","Persist"))
@
But this 4-way table is too large and awkward to work with. Among the networks,
Fox and Other occur infrequently.
We can also cut it down to a 3-way table by considering only viewers who persist
with the current station.%
\footnote{This relies on the fact that that indexing
an array drops dimensions of length 1 by default,
using the argument \code{drop=TRUE};
the result is coerced to the lowest possible dimension.
}
<>=
TV <- TV[,,1:3,] # keep only ABC, CBS, NBC
TV <- TV[,,,3] # keep only Persist -- now a 3 way table
structable(TV)
@
Finally, for some purposes, we might want to collapse the 11 times into a smaller number.
Here, we use \codefun{as.data.frame.table} to convert the table back to a data frame,
\codefun{levels} to re-assign the values of \code{Time},
and finally, \codefun{xtabs} to give a new, collapsed frequency table.
<>=
TV.df <- as.data.frame.table(TV)
levels(TV.df$Time) <- c(rep("8:00-8:59",4),rep("9:00-9:59",4), rep("10:00-10:44",3))
TV2 <- xtabs(Freq ~ Day + Time + Network, TV.df)
structable(Day ~ Time+Network,TV2)
@
Whew! See \figref{fig:TV-mosaic} for a mosaic plot of the \code{TV2} data.
\section{Tests of Independence}
\subsection{CrossTable}
OK, now we're ready to do some analyses. For tabular displays,
the \codefun{CrossTable} function in the \pkg{gmodels} package produces cross-tabulations
modeled after \code{PROC FREQ} in \proglang{SAS} or \code{CROSSTABS} in \proglang{SPSS}.
It has a wealth of options for the quantities that can be shown in each cell.
<>=
# 2-Way Cross Tabulation
library(gmodels)
CrossTable(GSStab,prop.t=FALSE,prop.r=FALSE,prop.c=FALSE)
@
There are options to report percentages (row, column, cell), specify decimal
places, produce Chi-square, Fisher, and McNemar tests of independence, report
expected and residual values (pearson, standardized, adjusted standardized),
include missing values as valid, annotate with row and column titles, and format
as \proglang{SAS} or \proglang{SPSS} style output! See \code{help(CrossTable)} for details.
\subsection{Chi-square test}
For 2-way tables you can use \codefun{chisq.test} to test independence of the row
and column variable. By default, the $p$-value is calculated from the asymptotic
chi-squared distribution of the test statistic. Optionally, the $p$-value can be
derived via Monte Carlo simulation.
<>=
(HairEye <- margin.table(HairEyeColor, c(1, 2)))
chisq.test(HairEye)
@
\subsection{Fisher Exact Test}\label{sec:Fisher}
\code{fisher.test(X)} provides an exact test of independence. \code{X} must be a two-way
contingency table in table form. Another form,
\code{fisher.test(X, Y)} takes two
categorical vectors of the same length.
For tables larger than $2 \times 2$ the method can be computationally intensive (or can fail) if
the frequencies are not small.
<>=
fisher.test(GSStab)
@
But this does not work because \data{HairEye} data has $n$=592 total frequency.
An exact test is unnecessary in this case.
<>=
fisher.test(HairEye)
@
%# <>=
%# #cat(try(fisher.test(HairEye)))
%# @
\begin{Soutput}
Error in fisher.test(HairEye) : FEXACT error 6.
LDKEY is too small for this problem.
Try increasing the size of the workspace.
\end{Soutput}
\subsection[Mantel-Haenszel test]{Mantel-Haenszel test and conditional association}\label{sec:mantel}
Use the \code{mantelhaen.test(X)} function to perform a Cochran-Mantel-Haenszel
$\chi^2$ chi
test of the null hypothesis that two nominal variables are
\emph{conditionally independent}, $A \perp B \given C$, in each stratum, assuming that there is no three-way
interaction. \code{X} is a 3 dimensional contingency table, where the last dimension
refers to the strata.
The \data{UCBAdmissions} serves as an example of a $2 \times 2 \times 6$ table,
with \code{Dept} as the stratifying variable.
<>=
## UC Berkeley Student Admissions
mantelhaen.test(UCBAdmissions)
@
The results show no evidence for association between admission and gender
when adjusted for department. However, we can easily see that the assumption
of equal association across the strata (no 3-way association) is probably
violated. For $2 \times 2 \times k$ tables, this can be examined
from the odds ratios for each $2 \times 2$ table (\codefun{oddsratio}), and
tested
by using
\verb|woolf_test()| in \pkg{vcd}.
%<>=
%oddsRatio <- function(x) (x[1,1]*x[2,2])/(x[1,2]*x[2,1])
%apply(UCBAdmissions, 3, oddsRatio)
%
%woolf_test(UCBAdmissions)
%@
<>=
oddsratio(UCBAdmissions, log=FALSE)
lor <- oddsratio(UCBAdmissions) # capture log odds ratios
summary(lor)
woolf_test(UCBAdmissions)
@
We can visualize the odds ratios of Admission for
each department with fourfold displays using \codefun{fourfold}. The cell
frequencies $n_{ij}$ of each $2 \times 2$ table are shown as a quarter circle whose
radius is proportional to $\sqrt{n_{ij}}$, so that its area is proportional to the
cell frequency.
Confidence rings for the odds ratio allow a visual test of the null of no association;
the rings for adjacent quadrants overlap \emph{iff} the observed counts are consistent
with the null hypothesis. In the extended version (the default), brighter colors
are used where the odds ratio is significantly different from 1.
The following lines produce \figref{fig:fourfold1}.%
\footnote{The color values \code{col[3:4]} were modified from their default values
to show a greater contrast between significant and insignificant associations here.}
<>=
col <- c("#99CCFF", "#6699CC", "#F9AFAF", "#6666A0", "#FF0000", "#000080")
fourfold(UCB,mfrow=c(2,3), color=col)
@
%\setkeys{Gin}{width=0.8\textwidth}
\begin{figure}[htb]
\begin{center}
%<>=
%col <- c("#99CCFF", "#6699CC", "#F9AFAF", "#6666A0", "#FF0000", "#000080")
%fourfold(UCB,mfrow=c(2,3), color=col)
%@
\includegraphics[width=0.8\textwidth,trim=80 50 80 50]{fig/vcd-tut-fourfold1}
\caption{Fourfold display for the \data{UCBAdmissions} data. Where the odds ratio differs
significantly from 1.0, the confidence bands do not overlap, and the circle quadrants are
shaded more intensely.}
\label{fig:fourfold1}
\end{center}
\end{figure}
Another \pkg{vcd} function, \codefun{cotabplot}, provides a more general approach
to visualizing conditional associations in contingency tables,
similar to trellis-like plots produced by \codefun{coplot} and lattice graphics.
The \code{panel} argument supplies a function used to render each conditional
subtable. The following gives a display (not shown) similar to \figref{fig:fourfold1}.
<>=
cotabplot(UCB, panel = cotab_fourfold)
@
When we want to view the conditional
probabilities of a response variable (e.g., \code{Admit})
in relation to several factors,
an alternative visualization is a \codefun{doubledecker} plot.
This plot is a specialized version of a mosaic plot, which
highlights the levels of a response variable (plotted vertically)
in relation to the factors (shown horizontally). The following
call produces \figref{fig:doubledecker}, where we use indexing
on the first factor (\code{Admit}) to make \code{Admitted}
the highlighted level.
In this plot, the
association between \code{Admit} and \code{Gender} is shown
where the heights of the highlighted conditional probabilities
do not align. The excess of females admitted in Dept A stands out here.
<>=
doubledecker(Admit ~ Dept + Gender, data=UCBAdmissions[2:1,,])
@
\begin{figure}[htb]
\begin{center}
\includegraphics[width=0.9\textwidth]{fig/vcd-tut-doubledecker}
\caption{Doubledecker display for the \data{UCBAdmissions} data. The heights
of the highlighted bars show the conditional probabilities of \texttt{Admit},
given \texttt{Dept} and \texttt{Gender}.}
\label{fig:doubledecker}
\end{center}
\end{figure}
Finally, the there is a \codefun{plot} method for \code{oddsratio} objects.
By default, it shows the 95\% confidence interval for the log odds ratio.
\figref{fig:oddsratio} is produced by:
<>=
plot(lor, xlab="Department", ylab="Log Odds Ratio (Admit | Gender)")
@
\setkeys{Gin}{width=0.5\textwidth}
\begin{figure}[htb]
\begin{center}
<>=
plot(lor, xlab="Department", ylab="Log Odds Ratio (Admit | Gender)")
@
\caption{Log odds ratio plot for the \data{UCBAdmissions} data.}
\label{fig:oddsratio}
\end{center}
\end{figure}
\subsection[CMH tests: ordinal factors]{Cochran-Mantel-Haenszel tests for ordinal factors}\label{sec:CMH}
The standard $\chi^2$ tests for association in a two-way table
treat both table factors as nominal (unordered) categories.
When one or both factors of a two-way table are
quantitative or ordinal, more powerful tests of association
may be obtaianed by taking ordinality into account, using
row and or column scores to test for linear trends or differences
in row or column means.
More general versions of the CMH tests (Landis etal., 1978) are provided by assigning
numeric scores to the row and/or column variables.
For example, with two ordinal factors (assumed to be equally spaced), assigning
integer scores, \code{1:R} and \code{1:C} tests the linear $\times$ linear component
of association. This is statistically equivalent to the Pearson correlation between the
integer-scored table variables, with $\chi^2 = (n-1) r^2$, with only 1 $df$
rather than $(R-1)\times(C-1)$ for the test of general association.
When only one table
variable is ordinal, these general CMH tests are analogous to an ANOVA, testing
whether the row mean scores or column mean scores are equal, again consuming
fewer $df$ than the test of general association.
The \codefun{CMHtest} function in \pkg{vcdExtra} now calculates these various
CMH tests for two possibly ordered factors, optionally stratified other factor(s).
\Example
Recall the $4 \times 4$ table, \code{JobSat} introduced in \secref{sec:creating},
<>=
JobSat
@
Treating the \code{satisfaction} levels as equally spaced, but using
midpoints of the \code{income} categories as row scores gives the following results:
<>=
CMHtest(JobSat, rscores=c(7.5,20,32.5,60))
@
Note that with the relatively small cell frequencies, the test for general
give no evidence for association. However, the the \code{cor} test for linear x linear
association on 1 df is nearly significant. The \pkg{coin} contains the
functions \verb|cmh_test()| and \verb|lbl_test()|
for CMH tests of general association and linear x linear association respectively.
\subsection{Measures of Association}
There are a variety of statistical measures of \emph{strength} of association for
contingency tables--- similar in spirit to $r$ or $r^2$ for continuous variables.
With a large sample size, even a small degree of association can show a
significant $\chi^2$, as in the example below for the \data{GSS} data.
The \codefun{assocstats} function in \pkg{vcd} calculates the $\phi$
contingency coefficient, and Cramer's V for an $r \times c$ table.
The input must be in table form, a two-way $r \times c$ table.
It won't work with \data{GSS} in frequency form, but by now you should know how
to convert.
<>=
assocstats(GSStab)
@
For tables with ordinal variables, like \data{JobSat}, some people prefer the
Goodman-Kruskal $\gamma$ statistic (\citet[\S 2.4.3]{vcd:Agresti:2002})
based on a comparison of concordant
and discordant pairs of observations in the case-form equivalent of a two-way table.
<>=
GKgamma(JobSat)
@
A web article by Richard Darlington,
\url{http://www.psych.cornell.edu/Darlington/crosstab/TABLE0.HTM}
gives further description of these and other measures of association.
\subsection{Measures of Agreement}
The
\codefun{Kappa} function in the \pkg{vcd} package calculates Cohen's $\kappa$ and weighted
$\kappa$ for a square two-way table with the same row and column categories \citep{Cohen:60}.%
\footnote{
Don't confuse this with \codefun{kappa} in base \proglang{R} that computes something
entirely different (the condition number of a matrix).
}
Normal-theory $z$-tests are obtained by dividing $\kappa$ by its asymptotic standard
error (ASE). A \codefun{confint} method for \code{Kappa} objects provides confidence intervals.
<>=
(K <- Kappa(SexualFun))
confint(K)
@
A visualization of agreement, both unweighted and weighted for degree of departure
from exact agreement is provided by the \codefun{agreementplot} function.
\figref{fig:agreesex} shows the agreementplot for the \data{SexualFun} data,
produced as shown below. The Bangdiwala measures represent the proportion of the
shaded areas of the diagonal rectangles, using weights $w_1$ for exact agreement,
and $w_2$ for partial agreement one step from the main diagonal.
<>=
agree <- agreementplot(SexualFun, main="Is sex fun?")
unlist(agree)
@
%\setkeys{Gin}{width=0.5\textwidth}
\begin{figure}[htb]
\begin{center}
%<>=
%agree <- agreementplot(SexualFun, main="Is sex fun?")
%agree
%@
\includegraphics[width=0.4\textwidth,trim=50 25 50 25]{fig/vcd-tut-agreesex}
\caption{Agreement plot for the \data{SexualFun} data.}
\label{fig:agreesex}
\end{center}
\end{figure}
In other examples, the agreement plot can help to show \emph{sources}
of disagreement. For example, when the shaded boxes are above or below the diagonal
(red) line, a lack of exact agreement can be attributed in part to
different frequency of use of categories by the two raters-- lack of
\emph{marginal homogeneity}.
\subsection{Correspondence analysis}
Use the \pkg{ca} package for correspondence analysis for visually exploring relationships
between rows and columns in contingency tables. For an $r \times c$ table,
the method provides a breakdown of the Pearson $\chi^2$ for association in up to $M = \min(r-1, c-1)$
dimensions, and finds scores for the row ($x_{im}$) and column ($y_{jm}$) categories
such that the observations have the maximum possible correlations.%
\footnote{
Related methods are the non-parametric
CMH tests using assumed row/column scores (\secref{sec:CMH}),
the analogous \codefun{glm} model-based methods (\secref{sec:CMH}),
and the more general RC models which can be fit using \codefun{gnm}.
Correspondence analysis differs in that it is a primarily
descriptive/exploratory method (no significance tests), but is
directly tied to informative graphic displays of the row/column categories.
}
Here, we carry out a simple correspondence analysis of the \data{HairEye} data.
The printed results show that nearly 99\% of the association between hair color and eye color
can be accounted for in 2 dimensions, of which the first dimension accounts for 90\%.
<>=
library(ca)
ca(HairEye)
@
The resulting \code{ca} object can be plotted just by running the \codefun{plot}
method on the \code{ca} object, giving the result in
\figref{fig:ca-haireye}. \codefun{plot.ca} does not allow labels for dimensions;
these can be added with \codefun{title}.
It can be seen that most of the association is accounted for by the ordering
of both hair color and eye color along Dimension 1, a dark to light dimension.
<>=
plot(ca(HairEye), main="Hair Color and Eye Color")
title(xlab="Dim 1 (89.4%)", ylab="Dim 2 (9.5%)")
@
\setkeys{Gin}{width=0.7\textwidth}
\begin{figure}[htb]
\begin{center}
<>=
plot(ca(HairEye), main="Hair Color and Eye Color")
title(xlab="Dim 1 (89.4%)", ylab="Dim 2 (9.5%)")
@
\caption{Correspondence analysis plot for the \data{HairEye} data.}
\label{fig:ca-haireye}
\end{center}
\end{figure}
\section{Loglinear Models}\label{sec:loglin}
You can use the \codefun{loglm} function in the \pkg{MASS} package to fit log-linear
models. Equivalent models can also be fit (from a different perspective) as generalized
linear models with the \codefun{glm} function using the \code{family='poisson'} argument,
and the \pkg{gnm} package provides a wider range of generalized \emph{nonlinear} models,
particularly for testing structured associations.
The visualization methods for these models were originally developed for models fit using \codefun{loglm},
so this approach is emphasized here. Some extensions of these methods for models
fit using \codefun{glm} and \codefun{gnm} are contained in the \pkg{vcdExtra} package
and illustrated in \secref{sec:glm}.
Assume we have a 3-way contingency table based on
variables A, B, and C.
The possible different forms of \loglin\ models for a 3-way table are shown in \tabref{tab:loglin-3way}.
The \textbf{Model formula} column shows how to express each model for \codefun{loglm} in \proglang{R}.%
\footnote{
For \codefun{glm}, or \codefun{gnm}, with the data in the form of a frequency data.frame, the same model is specified
in the form \code{glm(Freq} $\sim$ \code{..., family="poisson")}, where \texttt{Freq} is the name
of the cell frequency variable and \texttt{...} specifies the \textbf{Model formula}.
}
In the \textbf{Interpretation} column, the symbol ``$\perp$'' is to be read as ``is independent of,''
and ``$\given$'' means ``conditional on,'' or ``adjusting for,'' or just ``given''.
\begin{table}[htb]
\caption{Log-linear Models for Three-Way Tables}\label{tab:loglin-3way}
\begin{center}
\begin{tabular}{llll}
\hline
\textbf{Model} & \textbf{Model formula} & \textbf{Symbol}& \textbf{Interpretation} \\
\hline\hline
Mutual independence & \verb|~A + B + C| & $[A][B][C]$ & $A \perp B \perp C$ \\
Joint independence & \verb|~A*B + C| & $[AB][C]$ & $(A \: B) \perp C$ \\
Conditional independence & \verb|~(A+B)*C| & $[AC][BC]$ & $(A \perp B) \given C$ \\
All two-way associations & \verb|~A*B + A*C + B*C| & $[AB][AC][BC]$ & homogeneous association \\
Saturated model & \verb|~A*B*C| & $[ABC]$ & 3-way association \\
\hline
\end{tabular}
\end{center}
\end{table}
For example, the formula \verb|~A + B + C| specifies the model of \emph{mutual independence} with
no associations among the three factors. In standard notation for the expected frequencies
$m_{ijk}$, this corresponds to
\begin{equation*}
\log ( m_{ijk} ) = \mu + \lambda_i^A + \lambda_j^B + \lambda_k^C \equiv \texttt{A + B + C}
\end{equation*}
The parameters $\lambda_i^A , \lambda_j^B$ and $\lambda_k^C$ pertain to the differences among the
one-way marginal frequencies for the factors A, B and C.
Similarly, the model of \emph{joint independence}, $(A \: B) \perp C$,
allows an association between A and B, but specifies that
C is independent of both of these and their combinations,
\begin{equation*}
\log ( m_{ijk} ) = \mu + \lambda_i^A + \lambda_j^B + \lambda_k^C + \lambda_{ij}^{AB} \equiv \texttt{A * B + C}
\end{equation*}
where the parameters $\lambda_{ij}^{AB}$ pertain to the overall association between A and B (collapsing over C).
In the literature or text books, you will often find these models expressed in shorthand symbolic notation,
using brackets, \texttt{[ ]} to enclose the \emph{high-order terms} in the model.
Thus, the joint independence model can be denoted \texttt{[AB][C]}, as shown in the \textbf{Symbol} column in
\tabref{tab:loglin-3way}.
Models of \emph{conditional independence} allow (and fit) two of the three possible
two-way associations. There are three such models, depending on which variable is conditioned upon.
For a given conditional independence model, e.g., \texttt{[AB][AC]}, the given variable is the one
common to all terms, so this example has the interpretation $(B \perp C) \given A$.
\subsection[Fitting with loglm()]{Fitting with \codefun{loglm}}\label{sec:loglm}
For example, we can fit the model of mutual independence among hair color, eye color and sex
in \data{HairEyeColor} as
<>=
library(MASS)
## Independence model of hair and eye color and sex.
hec.1 <- loglm(~Hair+Eye+Sex, data=HairEyeColor)
hec.1
@
Similarly, the models of conditional independence and joint independence are specified as
<>=
## Conditional independence
hec.2 <- loglm(~(Hair + Eye) * Sex, data=HairEyeColor)
hec.2
@
<>=
## Joint independence model.
hec.3 <- loglm(~Hair*Eye + Sex, data=HairEyeColor)
hec.3
@
Note that printing the model gives a brief summary of the goodness of fit.
A set of models can be compared using the \codefun{anova} function.
<>=
anova(hec.1, hec.2, hec.3)
@
%Martin Theus and Stephan Lauer have written an excellent article on Visualizing
%Loglinear Models, using mosaic plots. There is also great tutorial example by
%Kevin Quinn on analyzing loglinear models via glm.
\subsection[Fitting with glm() and gnm()]{Fitting with \codefun{glm} and \codefun{gnm}}\label{sec:glm}
The \codefun{glm} approach, and extensions of this in the \pkg{gnm} package allows a
much wider class of models for frequency data to be fit than can be handled by
\codefun{loglm}. Of particular importance are models for ordinal factors and for
square tables, where we can test more structured hypotheses about the patterns
of association than are provided in the tests of general association under
\codefun{loglm}. These are similar in spirit to the
non-parametric CMH tests described in \secref{sec:CMH}.
\Example
The data \code{Mental} in the \pkg{vcdExtra} package gives a two-way table in frequency form
classifying young people by
their mental health status and parents' socioeconomic status (SES), where
both of these variables are ordered factors.
<>=
str(Mental)
xtabs(Freq ~ mental+ses, data=Mental) # display the frequency table
@
Simple ways of handling ordinal variables involve assigning scores to the table
categories, and the simplest cases are to use integer scores, either for the row variable (``column
effects'' model), the column variable (``row effects'' model), or both (``uniform association''
model).
<>=
indep <- glm(Freq ~ mental + ses, family = poisson, data = Mental) # independence model
@
To fit more parsimonious models than general association, we can define
numeric scores for the row and column categories
<>=
# Use integer scores for rows/cols
Cscore <- as.numeric(Mental$ses)
Rscore <- as.numeric(Mental$mental)
@
Then, the row effects model, the column effects model, and the uniform association
model can be fit as follows:
<>=
# column effects model (ses)
coleff <- glm(Freq ~ mental + ses + Rscore:ses, family = poisson, data = Mental)
# row effects model (mental)
roweff <- glm(Freq ~ mental + ses + mental:Cscore, family = poisson, data = Mental)
# linear x linear association
linlin <- glm(Freq ~ mental + ses + Rscore:Cscore, family = poisson, data = Mental)
@
The \codefun{Summarize} in \pkg{vcdExtra} provides a nice, compact summary of
the fit statistics for a set of models, collected into a \class{glmlist} object.
Smaller is better for AIC and BIC.
<>=
# compare models using AIC, BIC, etc
vcdExtra::LRstats(glmlist(indep, roweff, coleff, linlin))
@
For specific model comparisons, we can also carry out tests of \emph{nested} models with
\codefun{anova} when those models are listed from smallest to largest.
Here, there are two separate paths from the most restrictive (independence) model
through the model of uniform association, to those that allow only one of
row effects or column effects.
<>=
anova(indep, linlin, coleff, test="Chisq")
anova(indep, linlin, roweff, test="Chisq")
@
The model of linear by linear association seems best on all accounts.
For comparison, one might try the CMH tests on these data:
<>=
CMHtest(xtabs(Freq~ses+mental, data=Mental))
@
\subsection{Non-linear terms}
The strength of the \pkg{gnm} package is that it handles a wide variety of models
that handle non-linear terms, where the parameters enter the model beyond a simple
linear function.
The simplest example is the Goodman RC(1) model, which allows a multiplicative
term to account for the association of the table variables.
In the notation of generalized linear models with a log link, this can be expressed as
\begin{equation*}
\log \mu_{ij} = \alpha_i + \beta_j + \gamma_{i} \delta_{j}
\end{equation*}
where the row-multiplicative effect parameters $\gamma_i$ and
corresponding column parameters $\delta_j$ are estimated from the data.%
\footnote{
This is similar in spirit to a correspondence analysis with a single
dimension, but as a statistical model.
}
Similarly, the RC(2) model adds two multiplicative terms to
the independence model,
\begin{equation*}
\log \mu_{ij} = \alpha_i + \beta_j + \gamma_{i1} \delta_{j1} + \gamma_{i2} \delta_{j2}
\end{equation*}
In the \pkg{gnm} package, these models may be fit using the \codefun{Mult}
to specify the multiplicative term, and \codefun{instances} to specify several
such terms.
\Example
For the \code{Mental} data, we fit the RC(1) and RC(2) models, and compare
these with the independence model.
<>=
RC1 <- gnm(Freq ~ mental + ses + Mult(mental,ses), data=Mental,
family=poisson, , verbose=FALSE)
RC2 <- gnm(Freq ~ mental+ses + instances(Mult(mental,ses),2), data=Mental,
family=poisson, verbose=FALSE)
anova(indep, RC1, RC2, test="Chisq")
@
\section{Mosaic plots}\label{sec:mosaic}
Mosaic plots provide an ideal method both for visualizing contingency tables and for
visualizing the fit--- or more importantly--- lack of fit of a \loglin\ model.
For a two-way table, \codefun{mosaic} fits a model of independence, $[A][B]$
or \verb|~A+B| as an \proglang{R} formula.
For $n$-way tables, \codefun{mosaic} can fit any \loglin\ model, and can also be
used to plot a model fit with \codefun{loglm}.
See \citet{vcd:Friendly:1994,vcd:Friendly:1999} for the statistical ideas behind these
uses of mosaic displays in connection with \loglin\ models.
The essential idea is to recursively sub-divide a unit square into rectangular ``tiles'' for the
cells of the table, such
that the are area of each tile is proportional to the cell frequency.
For a given \loglin\ model, the tiles can then be shaded in various ways to reflect
the residuals (lack of fit) for a given model. The pattern of residuals can then
be used to suggest a better model or understand \emph{where} a given model fits or
does not fit.
\codefun{mosaic} provides a wide range of options for the directions of splitting,
the specification of shading, labeling, spacing, legend and many other details.
It is actually implemented as a special case of a more general
class of displays for $n$-way tables called \code{strucplot}, including
sieve diagrams, association plots, double-decker plots as well as mosaic
plots. For details, see \code{help(strucplot)} and the ``See also'' links,
and also \citet{vcd:Meyer+Zeileis+Hornik:2006b}, which is available as
an \proglang{R} vignette via \code{vignette("strucplot", package="vcd")}.
\figref{fig:arthritis}, showing the association between
\code{Treatment} and \code{Improved} was produced with the following
call to \codefun{mosaic}.
<>=
mosaic(art, gp = shading_max, split_vertical = TRUE,
main="Arthritis: [Treatment] [Improved]")
@
Note that the residuals for the independence model were not large
(as shown in the legend),
yet the association between \code{Treatment} and \code{Improved}
is highly significant.
<>=
summary(art)
@
In contrast, one of the other shading schemes, from \citet{vcd:Friendly:1994}
(use: \verb|gp = shading_Friendly|),
uses fixed cutoffs of $\pm 2, \pm 4$,
to shade cells which are \emph{individually} significant
at approximately $\alpha = 0.05$ and $\alpha = 0.001$ levels, respectively.
The right panel below uses \verb|gp = shading_Friendly|.
\setkeys{Gin}{width=0.5\textwidth}
<>=
mosaic(art, gp = shading_max, split_vertical = TRUE,
main="Arthritis: gp = shading_max")
@
<>=
mosaic(art, gp = shading_Friendly, split_vertical = TRUE,
main="Arthritis: gp = shading_Friendly")
@
\subsection[Mosaics for loglinear models]{Mosaics for \loglin\ models}\label{sec:mosaic-llm}
When you have fit a \loglin\ model using \codefun{loglm},
and saved the result (as a \code{loglm} object) the simplest way to display the
results is to use the \codefun{plot} method for the \code{loglm} object.
Calling \code{mosaic(loglm.object)} has the same result.
In \secref{sec:loglm} above, we fit several different models to the
\data{HairEyeColor} data. We can produce mosaic displays of each just
by plotting them:
<>=
# mosaic plots, using plot.loglm() method
plot(hec.1, main="model: [Hair][Eye][Sex]")
plot(hec.2, main="model: [HairSex][EyeSex]")
plot(hec.3, main="model: [HairEye][Sex]")
@
\setkeys{Gin}{width=0.32\textwidth}
<>=
plot(hec.1, main="model: [Hair][Eye][Sex]")
@
<>=
plot(hec.2, main="model: [HairSex][EyeSex]")
@
<>=
plot(hec.3, main="model: [HairSex][EyeSex]")
@
Alternatively, you can supply the model formula to \codefun{mosaic}
with the \code{expected} argument. This is passed to \codefun{loglm},
which fits the model, and returns residuals used for shading in the plot.
For example, here we examine the \data{TV2} constructed in \secref{sec:complex}
above. The goal is to see how Network choice depends on (varies with)
Day and Time. To do this:
\begin{itemize}
\item We fit a model of joint independence of
\code{Network} on the combinations of \code{Day} and \code{Time},
with the model formula \verb|~Day:Time + Network|.
\item To make the display more easily read, we place \code{Day} and \code{Time}
on the vertical axis and \code{Network} on the horizontal,
\item The \code{Time} values overlap on the right vertical axis, so we use
\codefun{level} to abbreviate them. \codefun{mosaic} also supports a
more sophisticated set of labeling functions. Instead of changing the data
table, we could have used
\verb|labeling_args = list(abbreviate = c(Time = 2))| for a similar effect.
\end{itemize}
The following call to \codefun{mosaic} produces \figref{fig:TV-mosaic}:
<>=
dimnames(TV2)$Time <- c("8", "9", "10") # re-level for mosaic display
mosaic(~ Day + Network + Time, data=TV2, expected=~Day:Time + Network,
legend=FALSE, gp=shading_Friendly)
@
\setkeys{Gin}{width=0.75\textwidth}
\begin{figure}[htb]
\begin{center}
<>=
dimnames(TV2)$Time <- c("8", "9", "10") # re-level for mosaic display
mosaic(~ Day + Network + Time, data=TV2, expected=~Day:Time + Network,
legend=FALSE, gp=shading_Friendly)
@
\caption{Mosaic plot for the \data{TV} data
showing model of joint independence, \texttt{Day:Time + Network} .}
\label{fig:TV-mosaic}
\end{center}
\end{figure}
From this, it is easy to read from the display how network choice varies with day and
time. For example, CBS dominates in all time slots on Monday;
ABC and NBC dominate on Tuesday, particularly in the later time slots;
Thursday is an NBC day, while on Friday, ABC gets the greatest share.
In interpreting this mosaic and other plots, it is important to understand that
associations included in the model---here, that between day and time---are \emph{not}
shown in the shading of the cells, because they have been fitted (taken into account)
in the \loglin\ model.
For comparison, you might want to try fitting the model of homogeneous association.
This allows all pairs of factors to be associated, but asserts that each pairwise
association is the same across the levels of the remaining factor.
The resulting plot displays the contributions to a 3-way association, but is not shown here.
<>=
mosaic(~ Day + Network + Time, data=TV2,
expected=~Day:Time + Day:Network + Time:Network,
legend=FALSE, gp=shading_Friendly)
@
\subsection[Mosaics for glm() and gnm() models]{Mosaics for \codefun{glm} and \codefun{gnm} models}\label{sec:mosglm}
The \pkg{vcdExtra} package provides an additional method, \codefun{mosaic.glm}
for models fit with \codefun{glm} and \codefun{gnm}.%
\footnote{
Models fit with \codefun{gnm} are of \code{class = c("gnm", "glm", "lm")},
so all \code{*.glm} methods apply, unless overridden in the \pkg{gnm} package.
}
These are not restricted to the
Poisson family, but only apply to cases where the response variable is non-negative.
\Example
Here, we plot the independence and the linear-by-linear association model
for the Mental health data from \secref{sec:glm}.
These examples illustrate some of the options for labeling (variable names and
residuals printed in cells). Note that the \code{formula} supplied to \codefun{mosaic}
for \class{glm} objects refers to the order of factors displayed in the plot, not the model.
<>=
long.labels <- list(set_varnames = c(mental="Mental Health Status", ses="Parent SES"))
mosaic(indep, ~ses+mental, residuals_type="rstandard",
labeling_args = long.labels, labeling=labeling_residuals,
main="Mental health data: Independence")
mosaic(linlin, ~ses+mental, residuals_type="rstandard",
labeling_args = long.labels, labeling=labeling_residuals, suppress=1,
gp=shading_Friendly, main="Mental health data: Linear x Linear")
@
\setkeys{Gin}{width=0.49\textwidth}
<>=
long.labels <- list(set_varnames = c(mental="Mental Health Status", ses="Parent SES"))
mosaic(indep, ~ses+mental, residuals_type="rstandard",
labeling_args = long.labels, labeling=labeling_residuals,
main="Mental health data: Independence")
@
<>=
long.labels <- list(set_varnames = c(mental="Mental Health Status", ses="Parent SES"))
mosaic(linlin, ~ses+mental, residuals_type="rstandard",
labeling_args = long.labels, labeling=labeling_residuals, suppress=1,
gp=shading_Friendly, main="Mental health data: Linear x Linear")
@
The \pkg{gnm} package also fits a wide variety of models with nonlinear terms or terms for
structured associations of table variables. In the following, we fit the RC(1)
model
\begin{equation*}
\log ( m_{ij} ) = \mu + \lambda_i^A + \lambda_j^B + \phi \mu_i \nu_j
\end{equation*}
This is similar to the linear by linear model, except that the row effect
parameters ($\mu_i$) and column parameters ($\nu_j$) are estimated from the data
rather than given assigned equally-spaced values. The multiplicative terms
are specified by the \codefun{Mult}.
<>=
Mental$mental <- C(Mental$mental, treatment)
Mental$ses <- C(Mental$ses, treatment)
RC1model <- gnm(Freq ~ mental + ses + Mult(mental, ses),
family = poisson, data = Mental)
mosaic(RC1model, residuals_type="rstandard", labeling_args = long.labels,
labeling=labeling_residuals, suppress=1, gp=shading_Friendly,
main="Mental health data: RC(1) model")
@
Other forms of nonlinear terms are provided for the inverse of a predictor
(\codefun{Inv}) and the exponential of a predictor (\codefun{Exp}).
You should read \code{vignette("gnmOverview", package="gnm")} for further details.
\subsection{Mosaic tips and techniques}\label{sec:tips}
The \pkg{vcd} package implements an extremely general collection of graphical methods for
$n$-way frequency tables within the strucplot framework, which includes mosaic plots
(\codefun{mosaic}), as well as association plots (\codefun{assoc}), sieve diagrams
(\codefun{sieve}), as well as tabular displays (\codefun{structable}).
The graphical methods in \pkg{vcd} support a wide of options that control almost all of the details
of the plots, but it is often difficult to determine what arguments you need to supply
to achieve a given effect from the \code{help()}. As a first step, you should read the
\code{vignette("strucplot")} in \pkg{vcd} to understand the overall structure of
these plot methods. The notes below describe a few useful things that may not be obvious,
or can be done in different ways.
\subsubsection[Changing labels]{Changing the labels for variables and levels}
With data in contingency table form or as a frequency data frame, it often happens
that the variable names and/or the level values of the factors, while suitable
for analysis, are less than adequate when used in mosaic plots and other strucplot
displays.
For example, we might prefer that a variable named \code{ses} appear
as \code{"Socioeconomic Status"}, or a factor with levels \code{c("M", "F")}
be labeled using \code{c("Male", "Female")} in a plot. Or, sometimes we
start with a factor whose levels are fully spelled out
(e.g., \code{c("strongly disagree", "disagree", "neutral", "agree", "strongly agree")}),
only to find that the level labels overlap in graphic displays.
The strucplot framework in \pkg{vcd} provides an extremely large variety of
functions and options for controlling almost all details of text labels in mosaics
and other plots. See \code{help(labelings)} for an overview.
For example, in \secref{sec:ordered-factors} we showed how to rearrange the dimensions
of the \code{UCBAdmissions} table, change the names of the table variables, and
relabel the levels of one of the table variables.
The code below changes the actual table for plotting purposes, but we pointed out that
these changes can create other problems in analysis.
<>=
UCB <- aperm(UCBAdmissions, c(2, 1, 3))
names(dimnames(UCB)) <- c("Sex", "Admit?", "Department")
dimnames(UCB)[[2]] <- c("Yes", "No")
@
The same effects can be achieved \emph{without} modifying the data
using the \verb|set_varnames| and
\verb|set_labels| options in \codefun{mosaic} as follows:
<>=
vnames <- list(set_varnames = c(Admit="Admission", Gender="Sex", Dept="Department"))
lnames <- list(Admit = c("Yes", "No"),
Gender = c("Males", "Females"),
Dept = LETTERS[1:6])
mosaic(UCBAdmissions, labeling_args=vnames, set_labels=lnames)
@
In some cases, it may be sufficient to abbreviate (or clip, or rotate) level names to avoid
overlap. For example, the statements below produce another version of
\figref{fig:TV-mosaic} with days of the week abbreviated to their first three letters.
Section 4 in the \code{vignette("strucplot")} provides many other examples.
<>=
dimnames(TV2)$Time <- c("8", "9", "10") # re-level for mosaic display
mosaic(~ Day + Network + Time, data=TV2, expected=~Day:Time + Network,
legend=FALSE, gp=shading_Friendly,
labeling_args=list(abbreviate=c(Day=3)) )
@
%\subsubsection{Fitting complex models with glm() and gnm()}
\section[Continuous predictors]{Continuous predictors}\label{sec:contin}
When continuous predictors are available---and potentially important--- in explaining a
categorical outcome, models for that outcome include:
logistic regression (binary response),
the proportional odds model (ordered polytomous response),
multinomial (generalized) logistic regression.
Many of these are special cases of the generalized linear model using the
\code{"poisson"} or \code{"binomial"} family and their relatives.
\subsection{Spine and conditional density plots}\label{sec:spine}
I don't go into fitting such models here, but I would be remiss not to illustrate some
visualizations in \pkg{vcd} that are helpful here.
The first of these is the spine plot or spinogram \citep{vcd:Hummel:1996}
(produced with \codefun{spine}).
These are special cases of mosaic plots with
specific spacing and shading to show how a categorical response varies with
a continuous or categorical predictor.
They are also a generalization of stacked bar plots where not the heights but
the \emph{widths} of the bars corresponds to the relative frequencies of \code{x}. The heights
of the bars then correspond to the conditional relative frequencies of {y} in
every \code{x} group.
\Example
For the \data{Arthritis} data, we can see how \code{Improved} varies with \code{Age}
as follows. \codefun{spine} takes a formula of the form
\verb|y ~ x| with a single dependent factor and a single explanatory variable \code{x}
(a numeric variable or a factor).
The range of a numeric variable\code{x} is divided into intervals based on the
\code{breaks} argument, and stacked bars are drawn to show the distribution of
\code{y} as \code{x} varies. As shown below, the discrete table that is visualized
is returned by the function.
<>=
(spine(Improved ~ Age, data = Arthritis, breaks = 3))
(spine(Improved ~ Age, data = Arthritis, breaks = "Scott"))
@
\setkeys{Gin}{width=0.49\textwidth}
<>=
(spine(Improved ~ Age, data = Arthritis, breaks = 3))
@
<>=
(spine(Improved ~ Age, data = Arthritis, breaks = "Scott"))
@
The conditional density plot \citep{vcd:Hofmann+Theus} is a further generalization.
This visualization technique is similar to spinograms, but uses a smoothing approach
rather than discretizing the explanatory variable. As well, it uses
the original \code{x} axis and not a distorted one.
\setkeys{Gin}{width=0.6\textwidth}
\begin{figure}[htb]
\begin{center}
<>=
cdplot(Improved ~ Age, data = Arthritis)
with(Arthritis, rug(jitter(Age), col="white", quiet=TRUE))
@
\caption{Conditional density plot for the \data{Arthritis} data
showing the variation of Improved with Age.}
\label{fig:cd-plot}
\end{center}
\end{figure}
In such plots, it is useful to also see the distribution of the observations
across the horizontal axis, e.g., with a \codefun{rug} plot.
\figref{fig:cd-plot} uses \codefun{cdplot} from the \pkg{graphics} package
rather than \verb|cd_plot()| from \pkg{vcd}, and is produced with
<>=
cdplot(Improved ~ Age, data = Arthritis)
with(Arthritis, rug(jitter(Age), col="white", quiet=TRUE))
@
From \figref{fig:cd-plot} it can be easily seen that the proportion
of patients reporting Some or Marked improvement increases with Age,
but there are some peculiar bumps in the distribution.
These may be real or artifactual, but they would be hard to see
with most other visualization methods.
When we switch from non-parametric data exploration to parametric
statistical models, such effects are easily missed.
\subsection[Model-based plots]{Model-based plots: effect plots and \pkg{ggplot2} plots}\label{sec:modelplots}
The nonparametric conditional density plot uses smoothing methods
to convey the distributions of the response variable,
but displays that are simpler to interpret can often be obtained by plotting
the predicted response from a parametric model.
For complex \codefun{glm} models with interaction effects, the \pkg{effects}
package provides the most useful displays,
plotting the predicted values for a given term, averaging over other
predictors not included in that term. I don't illustrate this here, but
see \citet{effects:1,effects:2} and \code{help(package="effects")}.
Here I just briefly illustrate the capabilities of the \pkg{ggplot2} package
for model-smoothed plots of categorical responses in \codefun{glm} models.
\Example
The \data{Donner} data frame in \pkg{vcdExtra} gives details on the survival
of 90 members of the Donner party,
a group of people who attempted to migrate to California in 1846.
They were trapped by an early blizzard on the eastern side of the
Sierra Nevada mountains, and before they could be rescued,
nearly half of the party had died.
What factors affected who lived and who died?
<>=
data(Donner, package="vcdExtra")
str(Donner)
@
A potential model of interest
is the logistic regression model for $Pr(survived)$, allowing separate
fits for males and females as a function of \code{age}.
The key to this is the \verb|stat_smooth()| function, using
\code{method = "glm", method.args = list(family = binomial)}. The \verb|formula = y ~ x|
specifies a linear fit on the logit scale (\figref{fig:donner3}, left)
<>=
# separate linear fits on age for M/F
ggplot(Donner, aes(age, survived, color = sex)) +
geom_point(position = position_jitter(height = 0.02, width = 0)) +
stat_smooth(method = "glm", method.args = list(family = binomial), formula = y ~ x,
alpha = 0.2, size=2, aes(fill = sex))
@
Alternatively, we can allow a quadratic relation with \code{age}
by specifying \verb|formula = y ~ poly(x,2)| (\figref{fig:donner3}, right).
<>=
# separate quadratics
ggplot(Donner, aes(age, survived, color = sex)) +
geom_point(position = position_jitter(height = 0.02, width = 0)) +
stat_smooth(method = "glm", method.args = list(family = binomial), formula = y ~ poly(x,2),
alpha = 0.2, size=2, aes(fill = sex))
@
\setkeys{Gin}{width=0.49\textwidth}
\begin{figure}[htb]
\begin{center}
<>=
ggplot(Donner, aes(age, survived, color = sex)) +
geom_point(position = position_jitter(height = 0.02, width = 0)) +
stat_smooth(method = "glm", method.args = list(family = binomial), formula = y ~ x,
alpha = 0.2, size=2, aes(fill = sex))
@
<>=
# separate quadratics
ggplot(Donner, aes(age, survived, color = sex)) +
geom_point(position = position_jitter(height = 0.02, width = 0)) +
stat_smooth(method = "glm", method.args = list(family = binomial), formula = y ~ poly(x,2),
alpha = 0.2, size=2, aes(fill = sex))
@
\caption{Logistic regression plots for the \data{Donner} data
showing survival vs. age, by sex. Left: linear logistic model; right: quadratic model}
\label{fig:donner3}
\end{center}
\end{figure}
These plots very nicely show (a) the fitted $Pr(survived)$ for males and females;
(b) confidence bands around the smoothed model fits and (c) the individual
observations by jittered points at 0 and 1 for those who died and survived, respectively.
\bibliography{vcd,vcdExtra}
\end{document}
vcdExtra/vignettes/vcd.bib 0000644 0001762 0000144 00000063530 13164013351 015300 0 ustar ligges users %% general graphics & original methods
@Article{vcd:Cohen:1980,
author = {A. Cohen},
title = {On the Graphical Display of the Significant
Components in a Two-Way Contingency Table},
journal = {Communications in Statistics---Theory and Methods},
year = {1980},
volume = {A9},
pages = {1025--1041}
}
@InProceedings{vcd:Hartigan+Kleiner:1981,
author = {J. A. Hartigan and B. Kleiner},
title = {Mosaics for Contingency Tables},
booktitle = {Computer Science and Statistics: Proceedings of the 13th Symposium on the Interface},
pages = {268--273},
year = {1981},
editor = {W. F. Eddy},
address = {New York},
publisher = {Springer-Verlag}
}
@Article{vcd:Hartigan+Kleiner:1984,
author = {J. A. Hartigan and B. Kleiner},
title = {A Mosaic of Television Ratings},
journal = {The American Statistician},
year = {1984},
volume = {38},
pages = {32--35}
}
@TechReport{vcd:Young:1996,
author = {Forrest W. Young},
title = {{\pkg{ViSta}}: The Visual Statistics System},
institution = {UNC L.~L.~Thurstone Psychometric Laboratory Research Memorandum},
year = 1996,
number = {94--1(c)}
}
@Book{vcd:Cleveland:1993,
author = {William S. Cleveland},
title = {Visualizing Data},
publisher = {Hobart Press},
year = 1993,
address = {Summit, New Jersey}
}
@Article{vcd:Becker+Cleveland+Shyu:1996,
author = {Richard A. Becker and William S. Cleveland and Ming-Jen Shyu},
title = {The Visual Design and Control of Trellis Display},
journal = {Journal of Computational and Graphical Statistics},
year = {1996},
volume = {5},
pages = {123--155}
}
@InProceedings{vcd:Riedwyl+Schuepbach:1994,
author = {H. Riedwyl and M. Sch{\"u}pbach},
title = {Parquet Diagram to Plot Contingency Tables},
booktitle = {Softstat '93: Advances in Statistical Software},
pages = {293--299},
year = 1994,
editor = {F. Faulbaum},
address = {New York},
publisher = {Gustav Fischer}
}
%% color
@InProceedings{vcd:Ihaka:2003,
author = {Ross Ihaka},
title = {Colour for Presentation Graphics},
booktitle = {Proceedings of the 3rd International Workshop on Distributed
Statistical Computing, Vienna, Austria},
editor = {Kurt Hornik and Friedrich Leisch and Achim Zeileis},
year = {2003},
url = {http://www.ci.tuwien.ac.at/Conferences/DSC-2003/Proceedings/},
note = {{ISSN 1609-395X}},
}
@Article{vcd:Lumley:2006,
author = {Thomas Lumley},
title = {Color Coding and Color Blindness in Statistical Graphics},
journal = {ASA Statistical Computing \& Graphics Newsletter},
year = {2006},
volume = {17},
number = {2},
pages = {4--7}
}
@Book{vcd:Munsell:1905,
author = {Albert H. Munsell},
title = {A Color Notation},
publisher = {Munsell Color Company},
year = {1905},
address = {Boston, Massachusetts}
}
@Article{vcd:Harrower+Brewer:2003,
author = {Mark A. Harrower and Cynthia A. Brewer},
title = {\pkg{ColorBrewer.org}: An Online Tool for Selecting Color Schemes for Maps},
journal = {The Cartographic Journal},
year = {2003},
volume = {40},
pages = {27--37}
}
@InProceedings{vcd:Brewer:1999,
author = {Cynthia A. Brewer},
title = {Color Use Guidelines for Data Representation},
booktitle = {Proceedings of the Section on Statistical Graphics,
American Statistical Association},
address = {Alexandria, VA},
year = {1999},
pages = {55--60}
}
@Article{vcd:Cleveland+McGill:1983,
author = {William S. Cleveland and Robert McGill},
title = {A Color-caused Optical Illusion on a Statistical Graph},
journal = {The American Statistician},
year = {1983},
volume = {37},
pages = {101--105}
}
@Book{vcd:CIE:2004,
author = {{Commission Internationale de l'\'Eclairage}},
title = {Colorimetry},
edition = {3rd},
publisher = {Publication CIE 15:2004},
address = {Vienna, Austria},
year = {2004},
note = {{ISBN} 3-901-90633-9}
}
@InProceedings{vcd:Moretti+Lyons:2002,
author = {Giovanni Moretti and Paul Lyons},
title = {Tools for the Selection of Colour Palettes},
booktitle = {Proceedings of the New Zealand Symposium On Computer-Human Interaction (SIGCHI 2002)},
address = {University of Waikato, New Zealand},
month = {July},
year = {2002}
}
@Article{vcd:MacAdam:1942,
author = {D. L. MacAdam},
title = {Visual Sensitivities to Color Differences in Daylight},
journal = {Journal of the Optical Society of America},
year = {1942},
volume = {32},
number = {5},
pages = {247--274},
}
@Book{vcd:Wyszecki+Stiles:2000,
author = {G\"unter Wyszecki and W. S. Stiles},
title = {Color Science},
edition = {2nd},
publisher = {Wiley},
year = {2000},
note = {{ISBN} 0-471-39918-3}
}
@Misc{vcd:Poynton:2000,
author = {Charles Poynton},
title = {Frequently-Asked Questions About Color},
year = {2000},
howpublished = {URL \url{http://www.poynton.com/ColorFAQ.html}},
note = {Accessed 2006-09-14},
}
@Misc{vcd:Wiki+HSV:2006,
author = {Wikipedia},
title = {{HSV} Color Space --- {W}ikipedia{,} The Free Encyclopedia},
year = {2006},
howpublished = {URL \url{http://en.wikipedia.org/w/index.php?title=HSV_color_space&oldid=74735552}},
note = {Accessed 2006-09-14},
}
@Misc{vcd:Wiki+LUV:2006,
author = {Wikipedia},
title = {{Lab} Color Space --- {W}ikipedia{,} The Free Encyclopedia},
year = {2006},
howpublished = {URL \url{http://en.wikipedia.org/w/index.php?title=Lab_color_space&oldid=72611029}},
note = {Accessed 2006-09-14},
}
@Article{vcd:Smith:1978,
author = {Alvy Ray Smith},
title = {Color Gamut Transform Pairs},
journal = {Computer Graphics},
pages = {12--19},
year = {1978},
volume = {12},
number = {3},
note = {ACM SIGGRAPH 78 Conference Proceedings},
}
%% url = {http://www.alvyray.com/},
@Article{vcd:Meier+Spalter+Karelitz:2004,
author = {Barbara J. Meier and Anne Morgan Spalter and David B. Karelitz},
title = {Interactive Color Palette Tools},
journal = {{IEEE} Computer Graphics and Applications},
volume = {24},
number = {3},
year = {2004},
pages = {64--72},
}
%% url = {http://graphics.cs.brown.edu/research/color/}
@InCollection{vcd:Mollon:1995,
author = {J. Mollon},
editor = {T. Lamb and J. Bourriau},
booktitle = {Colour: Art and Science},
title = {Seeing Color},
publisher = {Cambridge Univesity Press},
year = 1995
}
%% Friendly publications
@Article{vcd:Friendly:1994,
author = {Michael Friendly},
title = {Mosaic Displays for Multi-Way Contingency Tables},
journal = {Journal of the American Statistical Association},
year = {1994},
volume = {89},
pages = {190--200}
}
@Article{vcd:Friendly:1999,
author = {Michael Friendly},
title = {Extending Mosaic Displays: Marginal, Conditional, and
Partial Views of Categorical Data},
journal = {Journal of Computational and Graphical Statistics},
year = {1999},
volume = {8},
number = {3},
pages = {373--395}
}
@Book{vcd:Friendly:2000,
author = {Michael Friendly},
title = {Visualizing Categorical Data},
publisher = {\textsf{SAS} Insitute},
year = {2000},
address = {Carey, NC},
URL = {http://www.math.yorku.ca/SCS/vcd/}
}
@Book{FriendlyMeyer:2016:DDAR,
title = {Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data},
year = {2016},
author = {Friendly, Michael and Meyer, David},
publisher = {Chapman \& Hall/CRC},
address = {Boca Raton, FL},
isbn = {978-1-4987-2583-5},
}
%% Augsburg publications
@Article{vcd:Theus+Lauer:1999,
author = {Martin Theus and Stephan R. W. Lauer},
title = {Visualizing Loglinear Models},
journal = {Journal of Computational and Graphical Statistics},
year = 1999,
volume = 8,
number = 3,
pages = {396--412}
}
@Article{vcd:Hofmann:2003,
author = {Heike Hofmann},
title = {Constructing and Reading Mosaicplots},
journal = {Computational Statistics \& Data Analysis},
year = {2003},
volume = {43},
pages = {565--580}
}
@Article{vcd:Hofmann:2001,
author = {Heike Hofmann},
title = {Generalized Odds Ratios for Visual Modelling},
journal = {Journal of Computational and Graphical Statistics},
year = {2001},
volume = {10},
pages = {1--13}
}
@Article{vcd:Theus:2003,
author = {Martin Theus},
title = {Interactive Data Visualization Using \pkg{Mondrian}},
journal = {Journal of Statistical Software},
volume = 7,
number = 11,
pages = {1--9},
year = 2003,
url = {http://www.jstatsoft.org/v07/i11/},
}
@Unpublished{vcd:Hofmann+Theus,
author = {Heike Hofmann and Martin Theus},
title = {Interactive Graphics for Visualizing Conditional
Distributions},
note = {Unpublished Manuscript},
year = {2005}
}
@Article{vcd:Hummel:1996,
author = {J. Hummel},
title = {Linked Bar Charts: Analysing Categorical Data
Graphically},
journal = {Computational Statistics},
year = 1996,
volume = 11,
pages = {23--33}
}
@Article{vcd:Unwin+Hawkins+Hofmann:1996,
author = {Antony R. Unwin and G. Hawkins and Heike Hofmann and
B. Siegl},
title = {Interactive Graphics for Data Sets with Missing
Values -- \pkg{MANET}},
journal = {Journal of Computational and Graphical Statistics},
year = 1996,
pages = {113--122},
volume = 4,
number = 6
}
@Manual{vcd:Urbanek+Wichtrey:2006,
title = {\pkg{iplots}: Interactive Graphics for \textsf{R}},
author = {Simon Urbanek and Tobias Wichtrey},
year = {2006},
note = {\textsf{R} package version 1.0-3},
url = {http://www.rosuda.org/iPlots/}
}
%% Software
@Manual{vcd:R:2006,
title = {\textsf{R}: {A} Language and Environment for Statistical Computing},
author = {{\textsf{R} Development Core Team}},
organization = {\textsf{R} Foundation for Statistical Computing},
address = {Vienna, Austria},
year = {2006},
note = {{ISBN} 3-900051-00-3},
url = {http://www.R-project.org/}
}
@Article{vcd:Murrell:2002,
author = {Paul Murrell},
title = {The \pkg{grid} Graphics Package},
journal = {\proglang{R} News},
year = 2002,
volume = 2,
number = 2,
pages = {14--19},
month = {June},
url = {http://CRAN.R-project.org/doc/Rnews/}
}
@Book{vcd:Murrell:2006,
author = {Paul Murrell},
title = {\textsf{R} Graphics},
publisher = {Chapmann \& Hall/CRC},
address = {Boca Raton, Florida},
year = {2006},
}
@Book{vcd:Venables+Ripley:2002,
author = {William N. Venables and Brian D. Ripley},
title = {Modern Applied Statistics with \textsf{S}},
edition = {4th},
publisher = {Springer-Verlag},
address = {New York},
year = {2002},
note = {{ISBN} 0-387-95457-0},
url = {http://www.stats.ox.ac.uk/pub/MASS4/}
}
@Manual{vcd:Ihaka:2006,
title = {\pkg{colorspace}: Colorspace Manipulation},
author = {Ross Ihaka},
year = {2006},
note = {\textsf{R} package version 0.95}
}
@Manual{vcd:Meyer+Zeileis+Hornik:2006,
title = {\pkg{vcd}: Visualizing Categorical Data},
author = {David Meyer and Achim Zeileis and Kurt Hornik},
year = {2006},
note = {\textsf{R} package version 1.0-6}
}
@article{vcd:Ligges+Maechler:2003,
title = {\pkg{scatterplot3d} -- An {R} Package for Visualizing
Multivariate Data},
author = {Uwe Ligges and Martin M{\"a}chler},
journal = {Journal of Statistical Software},
year = 2003,
pages = {1--20},
number = 11,
volume = 8,
url = {http://www.jstatsoft.org/v08/i11/}
}
@Manual{vcd:SAS:2005,
title = {\proglang{SAS/STAT} Version 9},
author = {\proglang{SAS} Institute Inc.},
year = {2005},
address = {Cary, NC}
}
@Manual{vcd:SPLUS:2005,
title = {\proglang{S-PLUS} 7},
author = {{Insightful Inc.}},
year = {2005},
address = {Seattle, WA}
}
%% data
@Article{vcd:Azzalini+Bowman:1990,
author = {A. Azzalini and A. W. Bowman},
title = {A Look at Some Data on the {O}ld {F}aithful Geyser},
journal = {Applied Statistics},
year = {1990},
volume = {39},
pages = {357--365},
}
@Article{vcd:Obel:1975,
author = {E.B. Obel},
title = {A Comparative Study of Patients with Cancer of the
Ovary Who Have Survived More or Less Than 10 Years},
journal = {Acta Obstetricia et Gynecologica Scandinavica},
year = 1975,
volume = 55,
pages = {429--439}
}
@InCollection{vcd:Koch+Edwards:1988,
author = {G. Koch and S. Edwards},
title = {Clinical Efficiency Trials with Categorical Data},
booktitle = {Biopharmaceutical Statistics for Drug Development},
editor = {K. E. Peace},
publisher = {Marcel Dekker},
address = {New York},
year = {1988},
pages = {403--451}
}
@TechReport{vcd:Knorr-Held:1999,
author = {Leonhard Knorr-Held},
title = {Dynamic Rating of Sports Teams},
institution = {SFB 386 ``Statistical Analysis of Discrete Structures''},
year = {1999},
type = {Discussion Paper},
number = {98},
url = {http://www.stat.uni-muenchen.de/sfb386/}
}
@Article{vcd:Snee:1974,
author = {R. D. Snee},
title = {Graphical Display of Two-Way Contingency Tables},
journal = {The American Statistician},
year = 1974,
volume = 28,
pages = {9--12}
}
@Article{vcd:Bickel+Hammel+O'Connell:1975,
author = {P. J. Bickel and E. A. Hammel and J. W. O'Connell},
title = {Sex Bias in Graduate Admissions: Data from {B}erkeley},
journal = {Science},
year = 1975,
volume = 187,
pages = {398--403}
}
@Book{vcd:Gilbert:1981,
author = {G. N. Gilbert},
title = {Modelling Society: An Introduction to Loglinear
Analysis for Social Researchers},
publisher = {Allen and Unwin},
year = 1981,
address = {London}
}
@Book{vcd:Thornes+Collard:1979,
author = {B. Thornes and J. Collard},
title = {Who Divorces?},
publisher = {Routledge \& Kegan},
year = 1979,
address = {London}
}
@Article{vcd:Dawson:1995,
author = {Robert J. MacG Dawson},
title = {The ``Unusual Episode'' Data Revisited},
journal = {Journal of Statistics Education},
year = 1995,
volume = 3,
url = {http://www.amstat.org/publications/jse/v3n3/datasets.dawson.html}
}
@Article{vcd:Haberman:1974,
author = {S. J. Haberman},
title = {Log-linear Models for Frequency Tables with Ordered
Classifications},
journal = {Biometrics},
year = 1974,
volume = 30,
pages = {689--700}
}
@Article{vcd:Wing:1962,
author = {J. K. Wing},
title = {Institutionalism in Mental Hospitals},
journal = {British Journal of Social Clinical Psychology},
year = 1962,
volume = 1,
pages = {38--51}
}
@Book{vcd:Andersen:1991,
author = {E. B. Andersen},
title = {The Statistical Analysis of Categorical Data},
publisher = {Springer-Verlag},
year = {1991},
address = {Berlin},
edition = {2nd}
}
@Article{vcd:Haberman:1973,
author = {S. J. Haberman},
title = {The Analysis of Residuals in Cross-classified Tables},
journal = {Biometrics},
year = {1973},
volume = {29},
pages = {205--220}
}
@Book{vcd:Everitt+Hothorn:2006,
author = {Brian S. Everitt and Torsten Hothorn},
title = {A Handbook of Statistical Analyses Using \textsf{R}},
publisher = {Chapman \& Hall/CRC},
address = {Boca Raton, Florida},
year = {2006}
}
@Article{vcd:Salib+Hillier:1997,
author = {Emad Salib and Valerie Hillier},
title = {A Case-Control Study of Smoking and {A}lzheimer's Disease},
journal = {International Journal of Geriatric Psychiatry},
year = {1997},
volume = {12},
pages = {295--300}
}
%% inference
@Book{vcd:Agresti:2002,
author = {Alan Agresti},
title = {Categorical Data Analysis},
publisher = {John Wiley \& Sons},
year = {2002},
address = {Hoboken, New Jersey},
edition = {2nd}
}
@Book{vcd:Mazanec+Strasser:2000,
author = {Josef A. Mazanec and Helmut Strasser},
title = {A Nonparametric Approach to Perceptions-based
Market Segmentation: Foundations},
publisher = {Springer-Verlag},
year = {2000},
address = {Berlin}
}
@Article{vcd:Strasser+Weber:1999,
author = {Helmut Strasser and Christian Weber},
title = {On the Asymptotic Theory of Permutation Statistics},
journal = {Mathematical Methods of Statistics},
volume = {8},
pages = {220--250},
year = {1999}
}
@Book{vcd:Pesarin:2001,
author = {Fortunato Pesarin},
title = {Multivariate Permutation Tests},
year = {2001},
publisher = {John Wiley \& Sons},
address = {Chichester}
}
@Article{vcd:Ernst:2004,
author = {Michael D. Ernst},
title = {Permutation Methods: A Basis for Exact Inference},
journal = {Statistical Science},
volume = {19},
year = {2004},
pages = {676--685}
}
@Article{vcd:Patefield:1981,
author = {W. M. Patefield},
title = {An Efficient Method of Generating $R \times C$ Tables
with Given Row and Column Totals},
note = {{A}lgorithm AS 159},
journal = {Applied Statistics},
volume = {30},
year = {1981},
pages = {91--97}
}
%% own
@InProceedings{vcd:Meyer+Zeileis+Hornik:2003,
author = {David Meyer and Achim Zeileis and Kurt Hornik},
title = {Visualizing Independence Using Extended Association Plots},
booktitle = {Proceedings of the 3rd International Workshop on Distributed
Statistical Computing, Vienna, Austria},
editor = {Kurt Hornik and Friedrich Leisch and Achim Zeileis},
year = {2003},
url = {http://www.ci.tuwien.ac.at/Conferences/DSC-2003/Proceedings/},
note = {{ISSN 1609-395X}},
}
@TechReport{vcd:Zeileis+Meyer+Hornik:2005,
author = {Achim Zeileis and David Meyer and Kurt Hornik},
title = {Residual-based Shadings for Visualizing (Conditional) Independence},
institution = {Department of Statistics and Mathematics,
Wirtschaftsuniversit\"at Wien,
Research Report Series},
year = {2005},
type = {Report},
number = {20},
month = {August},
url = {http://epub.wu-wien.ac.at/dyn/openURL?id=oai:epub.wu-wien.ac.at:epub-wu-01_871}
}
@Article{vcd:Zeileis+Meyer+Hornik:2007,
author = {Achim Zeileis and David Meyer and Kurt Hornik},
title = {Residual-based Shadings for Visualizing (Conditional)
Independence},
journal = {Journal of Computational and Graphical Statistics},
year = {2007},
volume = {16},
number = {3},
pages = {507--525},
doi = {10.1198/106186007X237856},
url = {http://statmath.wu-wien.ac.at/~zeileis/papers/Zeileis+Meyer+Hornik-2007.pdf}
}
@TechReport{vcd:Meyer+Zeileis+Hornik:2005a,
author = {David Meyer and Achim Zeileis and Kurt Hornik},
title = {The Strucplot Framework: Visualizing Multi-Way Contingency
Tables with \pkg{vcd}},
institution = {Department of Statistics and Mathematics,
Wirtschaftsuniversit\"at Wien,
Research Report Series},
year = {2005},
type = {Report},
number = {22},
month = {November},
url = {http://epub.wu-wien.ac.at/dyn/openURL?id=oai:epub.wu-wien.ac.at:epub-wu-01_8a1}
}
@Article{vcd:Meyer+Zeileis+Hornik:2006b,
author = {David Meyer and Achim Zeileis and Kurt Hornik},
title = {The Strucplot Framework: Visualizing Multi-way
Contingency Tables with \pkg{vcd}},
year = {2006},
journal = {Journal of Statistical Software},
volume = {17},
number = {3},
pages = {1--48},
url = {http://www.jstatsoft.org/v17/i03/}
}
@InCollection{vcd:Meyer+Zeileis+Hornik:2006a,
author = {David Meyer and Achim Zeileis and Kurt Hornik},
title = {Visualizing Contingency Tables},
editor = {Chun-Houh Chen and Wolfang H\"ardle and Antony
Unwin},
booktitle = {Handbook of Data Visualization},
series = {Springer Handbooks of Computational Statistics},
year = {2006},
publisher = {Springer-Verlag},
address = {New York},
note = {{ISBN} 3-540-33036-4, to appear}
}
@Article{vcd:Hothorn+Hornik+VanDeWiel:2006,
author = {Torsten Hothorn and Kurt Hornik and
Mark A. van de Wiel and Achim Zeileis},
title = {A {L}ego System for Conditional Inference},
journal = {The American Statistician},
year = {2006},
volume = {60},
number = {3},
pages = {257--263},
doi = {10.1198/000313006X118430}
}
@TechReport{vcd:Zeileis+Hornik:2006,
author = {Achim Zeileis and Kurt Hornik},
title = {Choosing Color Palettes for Statistical Graphics},
institution = {Department of Statistics and Mathematics,
Wirtschaftsuniversit\"at Wien,
Research Report Series},
year = {2006},
type = {Report},
number = {41},
month = {October},
url = {http://epub.wu-wien.ac.at/}
}
%% bad color examples
@Article{vcd:Gneiting+Sevcikova+Percival:2006,
author = {Tilmann Gneiting and Hana \v{S}ev\v{c}\'ikov\'a and Donald B. Percival
and Martin Schlather and Yindeng Jiang},
title = {Fast and Exact Simulation of Large Gaussian Lattice Systems in {$\mathbb{R}^2$}:
Exploring the Limits},
year = {2006},
journal = {Journal of Computational and Graphical Statistics},
volume = {15},
number = {3},
pages = {483--501},
note = {Figures~1--4}
}
@Article{vcd:Yang+Buckley+Dudoit:2002,
author = {Yee Hwa Yang and Michael J. Buckley and Sandrine Dudoit and Terence P. Speed},
title = {Comparison of Methods for Image Analysis on {cDNA} Microarray Data},
year = {2002},
journal = {Journal of Computational and Graphical Statistics},
volume = {11},
number = {1},
pages = {108--136},
note = {Figure~4a}
}
@Article{vcd:Kneib:2006,
author = {Thomas Kneib},
title = {Mixed Model-based Inference in Geoadditive Hazard Regression for
Interval-censored Survival Times},
year = {2006},
journal = {Computational Statistics \& Data Analysis},
volume = {51},
pages = {777--792},
note = {Figure~5 (left)}
}
@Article{vcd:Friendly:2002,
author = {Michael Friendly},
title = {A Brief History of the Mosaic Display},
year = {2002},
journal = {Journal of Computational and Graphical Statistics},
volume = {11},
number = {1},
pages = {89--107},
note = {Figure~11 (left, middle)}
}
@Article{vcd:Celeux+Hurn+Robert:2000,
author = {Gilles Celeux and Merrilee Hurn and Christian P. Robert},
title = {Computational and Inferential Difficulties with Mixture Posterior Distributions},
year = {2000},
journal = {Journal of the American Statistical Association},
volume = {95},
number = {451},
pages = {957--970},
note = {Figure~3}
}
%% pointers from Hadley
@article{cleveland:1987,
Author = {Cleveland, William and McGill, Robert},
Journal = {Journal of the Royal Statistical Society A},
Number = {3},
Pages = {192-229},
Title = {Graphical Perception: The Visual Decoding of Quantitative Information on Graphical Displays of Data},
Volume = {150},
Year = {1987}}
@article{cleveland:1984,
Author = {Cleveland, William S. and McGill, M. E.},
Journal = {Journal of the American Statistical Association},
Number = 387,
Pages = {531-554},
Title = {Graphical Perception: Theory, Experimentation and Application to the Development of Graphical Methods},
Volume = 79,
Year = 1984}
@article{huang:1997,
Author = {Huang, Chisheng and McDonald, John Alan and Stuetzle, Werner},
Journal = {Journal of Computational and Graphical Statistics},
Pages = {383--396},
Title = {Variable resolution bivariate plots},
Volume = {6},
Year = {1997}}
@article{carr:1987,
Author = {Carr, D. B. and Littlefield, R. J. and Nicholson, W. L. and Littlefield, J. S.},
Journal = {Journal of the American Statistical Association},
Number = {398},
Pages = {424-436},
Title = {Scatterplot Matrix Techniques for Large N},
Volume = {82},
Year = {1987}}
@book{cleveland:1994,
Author = {Cleveland, William},
Publisher = {Hobart Press},
Title = {The Elements of Graphing Data},
Year = {1994}}
@book{chambers:1983,
Author = {Chambers, John and Cleveland, William and Kleiner, Beat and Tukey, Paul},
Publisher = {Wadsworth},
Title = {Graphical methods for data analysis},
Year = {1983}}
@book{bertin:1983,
Address = {Madison, WI},
Author = {Bertin, Jacques},
Publisher = {University of Wisconsin Press},
Title = {Semiology of Graphics},
Year = {1983}}
@book{wilkinson:2006,
Author = {Wilkinson, Leland},
Publisher = {Springer-Verlag},
Series = {Statistics and Computing},
Title = {The Grammar of Graphics},
Year = {2005}}
vcdExtra/NEWS 0000644 0001762 0000144 00000025645 14202267352 012550 0 ustar ligges users Version 0.7-6 (2022-02-12)
o Fix some issues with CMHtest `types` argument #PR11 [Thx: ShuguangSun, Matt Kumar]
o Fix some Winbuilder URL nits; extensive spell checking
Version 0.7-5 (2020-12-25)
o Fix problem re use of rgl in mosaic3d examples
Version 0.7-4 (2019-09-25)
o Fix datasets() to work with packages not using LazyData #PR7 [Thx: Duncan Murdock]
o Bump package version
Version 0.7-3 (2018-06-04)
o fix version number for gnm in DESCRIPTION
o fix expand.dft and expand.table to work with tibbles [Thx: Duncan Murdoch]
o vcdExtra gets a hex sticker
Version 0.7-1 (2017-09-28)
o Fixed buglet in expand.dft when table is 1-dim [Thx: Long Qu]
o Added zero.test, a simple score test for zero inflation
o Development has moved to https://github.com/friendly/vcdExtra
Version 0.7-0 (2016-01-27)
o Added Glass data
o introduce links and references to DDAR
o added mcaplot() to plot MCA solutions in DDAR style
o added update.xtabs method
o updated vignette to refer to DDAR
Version 0.6-12 (2015-10-06)
o Added Burt data
o Fixed examples/vignette for ggplot_2.0.0
Version 0.6-11 (2015-09-14)
o bump pkg version for CRAN
Version 0.6-10 (2015-07-27)
o Added HospVisits data
Version 0.6-9 (2015-06-11)
o Added Mice data
o Removed uses of summarise() and Summarise() from demos and examples, in preparation for deprecating them.
o summarise() is now deprecated
o Now use importsFrom() for all functions from recommended packages
Version 0.6-8 (2015-04-15)
o Fixed Title: and Description: for CRAN
Version 0.6-7 (2015-04-02)
o Removed loddsratio (now in vcd) (rev 252)
o Removed print.Kappa (now in vcd) (rev 253)
o Fixed bug in CMHtest: rmeans and cmeans labels were reversed (rev 254)
o Fixed error in Fungicide.Rd, now that we require vcd_1.3-3 (rev 254)
o Added WorkerSat data - 2 x 2 x 2
o Added AirCrash data
Version 0.6-6 (2015-02-04)
o Minor doc changes
Version 0.6-5 (2014-11-07)
o Added Cormorants data (fixed to latest)
o Added LRstats, to replace Summarise. Older summarise() and Summarise() will eventually be deprecated.
o Now Suggests: AER for NMES1988 data
o collapse.table() now works with array objects
Version 0.6-3 (2014-10-27)
o Fixed bug in logLik.loglm when the data contain zero frequencies (rev 228)
o Made Summarise generic, adding a method for "glmlist" objects (rev 230)
o Added a coef() method for "glmlist" objects (thx: John Fox)
o Added a Summarise.loglmlist method (rev 232)
o Replaced all documentation uses of summarise() with Summarise() (rev 233)
o Added cutfac(), a convenience wrapper for cut() (rev 234)
o Now use rgl:: in all mosaic3d functions
Version 0.6-2 (2014-06-30)
o added Summarise, to replace summarise
o Added HairEyePlace data - 4 x 5 x 2, Caithness and Aberdeen hair/eye color (rev 223)
o Added PhdPubs data from Long (1997) - publications by PhD candidates
o Allow Summarise to work with models w/o a deviance() function
o Fixed bug in Summarise wrt degrees of freedom
Version 0.6-1 (2014-04-14)
o Added ICU data
o Added Toxaemia data - multivariate response contingency table (rev 209)
o Added Vietnam data - 2 x 5 x 4 frequency table (rev 210)
o Added logLik.loglm to allow use of AIC() and BIC() for loglm models (rev 212)
o Fixed loddsratio.Rd to work with revised vcd::CoalMiners data (rev 212)
o Added blogits for bivariate binary response data
o Added Vote1980 data (rev 214)
Version 0.6-0 (2014-03-07)
o Removed Authors: in DESCRIPTION, bumped version
Version 0.5-12 (2013-12-16)
o Added ShakeWords data set- word frequency counts from Shakespeare (rev 188)
o Added Geissler data-- all family sizes for Saxony sex composition data (rev 190)
o Added logseries functions for the logarithmic series distribution (rev 191)
o Added Depends data -- dependencies of r packages (rev 192)
o Fixed buglet in seq_loglm() not respecting arrays
o Added seq_mosaic() (rev 194)
o Added CyclingDeaths data (rev 196)
o mosaic3d() gets an interpolate= option to control shading levels (rev 197)
o Fixed bug in seq_mosaic, thx to David Meyer (rev 200)
o Fixed bug in seq_loglm() when marginals != 1:nf
Version 0.5-11 (2013-07-01)
o Added mosaic.glmlist to plot mosaics (or other strucplots) for some or all models in a glmlist (rev 169)
o Added loglim-utilities.R, containing a suite of functions to provide a more conceptual way to specify
loglinear models by type ('joint', 'conditional', 'mutual', 'markov', 'saturated') (rev 171)
o Added mosaic.loglmlist, similar to mosaic.glmlist for models fit using MASS::loglm (rev 173)
o Both mosaic.glmlist and mosaic.glmlist get an explicit panel=argument; both get some more sensible default
default arguments (rev 175)
o Added seq_loglm to fit sequential loglm models to marginal subtables, giving a loglmlist result (rev 176)
o Added Accident data (rev 178); fleshed out Accident examples (rev 180)
o Fixed use of ::: for R 3.0.1 (rev 179)
o Fixed various problems related to use of MASS:loglm (rev 181-183)
o Added Titanicp to datasets (rev 185)
Version 0.5-8 (2013-03-06)
o Revised vcd-tutorial showing some examples of plyr; added a section on RC models
o Added Donner data with example of ggplot2 plot for a binomial glm()
o Added vcd-tutorial section using ggplot2 for Donner data
o Enhanced datasets() to provide a maxTitle argument (rev 153)
o Added doubledecker plots to Dyke.Rd (rev 156)
o Added Draft1970table and Draft1970 data sets (rev 158)
o Added example of doubledecker plots to vcd-tutorial vignette (rev 164)
Version 0.5-7 (2013-03-01)
o Completed CMHtest methods, adding overall tests across strata in a general way
o CMHtest now gets an S3 generic with a formula interface
o print Kappa gets digits= and CI= arguments
Version 0.5-6 (2012-11-30)
o Added Hosmer Lemeshow and HLtest methods, including plotting via vcd::rootogram()
o Added CMHtest for general Cochran-Mantel-Haenszel tests
o Revised vcd-tutorial vignette, adding a section on CMH tests; removed dependence on Z.cls
Version 0.5-3 (2012-03-07)
o Added Mammograms data (4x4, ordered factors, agreement)
o Extended mosaic.glm examples
o Added Alligator data (4x2x2x5, in frequency form)
o Added DaytonSurvey data (5-way, 2x2x2x2x2 in frequency form)
o Extended vcd-tutorial vignette with a section on collapsing over factors
o Removed aperm.* now that aperm.table is in base R
Version 0.5-2 (2010-11-28)
o Added loddsratio and related methods for log odds ratios, generalizing vcd::oddsratio from
2 x 2 (x strata) tables to R x C (x strata) tables
o Added as.matrix.loddsratio, as.array.loddsratio methods
o Added some simple plot examples to example(loddsratio), anticipating a plot method
o Added data(Fungicide), a 2 x 2 x 2 x 2 table
o Renamed summarize() and related methods to summarise() to avoid conflict with plyr.
o Addition to vcd-tutorial vignette on use of aperm() with table objects
o Updated demo(yamaguchi-xie) to correct row/col nomenclature and add plot of BIC
o Added aperm() S3 generic to handle table objects
o Moved tv.dat to inst/doc/extdata to avoid warnings in R 2.12+
Version 0.5-1 (2010-09-17)
o Added Yamaguchi87 data (5x5x3 three-way mobility table in frequency form)
o Added demo(yamaguchi-xie) illustrating fitting and visualization of the models of homogeneous and
log multiplicative layer effects fit in Xie (1992, Table 1)
o Added BIC to summarize() and friends
o Added Hauser79 data (two-way mobility table), plus some examples from Powers and Xie (2008)
o Added Crossings() to construct interactions for Goodman 1972 crossings model.
o Added datasets() to list datasets in packages
o Extended description and examples of Kway()
o Added meanResiduals() and extended mosaic.glm() (Heather Turner)
o summarize() gets a sortby argument for glmlist and loglmlist objects
Version 0.5-0 (2010-04-28)
o Fleshed out mosaic3d, allowing display of observed or expected, internally calculated or externally
supplied residuals, specifying the initial 3D shape, etc. This completes the 'top-level' work on
mosaic3d(), borrowing code from vcd::strucplot.
o Added initial handling for zero cells in the table to mosaic3d().
o Added center3d() for finding the mean coordinates of shape3d objects.
o Added demo(mosaic-hec) comparing 2D and 3D mosaics for HairEyeColor data
o Gave mosaic3d a label_edge argument, allowing labels for dimensions at minima or maxima
o Made mosaic3d object oriented, giving it a loglm method
o Added Kway(), fitting all 0-way, 1-way, 2-way, ... k-way models in a glm
Version 0.4-3 (2010-03-25)
o Added demo(mosaic3d-demo), a proof-of-concept for doing 3D mosaic displays
o Added mosaic3d(), an initial basic version.
o Factored out split3d() and gave it S3 methods
Version 0.4-2 (2010-03-09)
o Revised vignette("vcd-tutorial"): added some hints for mosaic(), corrected stuff regarding prior
limitations of mosaic.glm()
o Added demo(Wong3-1): three-way table, with models of conditional association
o Added Suggest: effects for effects plots of glm(), multinom() and polr() models
o Added demo(housing): visualize models fit in example(housing, package="MASS") using mosaic() and
effect plots.
o Updated demo(Wong2-3): added model comparison plots, glmlist processing
o Added Suggest: VGAM
o Extended package description in vcdExtra-package.Rd
o Added glmlist() to facilitate processing, extraction, plotting, etc. of a collection of glm() models
o Added loglmlist(), for collections of loglm() objects
o Added summarize methods for glm, glmlist, loglm and loglmlist objects
Version 0.4-1 (2010-02-21)
o Added example(Caesar), illustrating structural zeros
o Re-named Heckman variables to e1971, ..., e1968 (errors from loglm); began example(Heckman)
o Added example(Detergent), example(Dyke)
o Fixed bug with mosaic.glm when data in global environment (Heather Turner)
o Added sieve.glm and assoc.glm methods (MF)
o Added modFit.glm and modFit.loglm
o Added demo(Wong2-3)
Version 0.4-0 (2010-02-23)
o Added new datasets: data/{Abortion, Bartlett, Caesar, Cancer, Detergent, Dyke, Gilby, Heart, Heckman, Hoyt, Mobility}
from mosdata.sas via md2r.sas converter.
o Fixed small documentation warnings
o Switched inst/CHANGES to NEWS
Version 0.3-6 (2009-04-21)
o Added Depends: gnm
o Added demo/{mental-glm, ucb-glm, vision-quasi}
o Added demo/{occStatus,yaish-unidiff}
o Initial release to CRAN
Version 0.3-5 (2009-3-6)
o mosaic.glm now uses object$data if available
Version 0.3-4 (2009-2-11)
o Fixed bugs in mosaic.glm, mosaic.gnm in models with terms like Diag(dest, origin)
that get included in x$xlevels
Version 0.3-3 (2009-2-10)
o Fixed bugs in mosaic.gnm
o Fixed print.GKgamma
o Added example of GKgamma to vcd-tutorial
Version 0.3-2 (2009-2-8)
o Added more examples to mosaic.glm.Rd
Version 0.3-0 (2009-2-6)
o Fixed bugs in mosaic.glm and mosaic.gnm
Version 0.2 (2009-2-1)
o Added vcd-tutorial vignette
Version 0.1 (2009-1-26)
o Initial version on R-Forge.
vcdExtra/R/ 0000755 0001762 0000144 00000000000 14202234157 012233 5 ustar ligges users vcdExtra/R/Crossings.R 0000644 0001762 0000144 00000002302 13164013351 014322 0 ustar ligges users # crossings model (Goodman, 1972)
# Ref:
#Goodman, L. (1972). Some multiplicative models for the analysis of cross-classified data.
#In: Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability,
#Berkeley, CA: University of California Press, pp. 649-696.
crossings <- function(i, j, n) {
npar <- n - 1
result <- list()
for(c in 1:npar) {
overi <- c >= i
overj <- c >= j
result[[c]] <- (overi & !overj) + (overj & !overi)
}
result <- matrix(unlist(result), length(i), npar)
colnames(result) <- paste('C', 1:npar, sep='')
result
}
Crossings <- function(...) {
dots <- list(...)
if (length(dots) != 2) stop("Crossings() is defined for only two factors")
if (length(dots[[1]]) != length(dots[[2]]))
stop("arguments to Crossings() must all have same length")
dots <- lapply(dots, as.factor)
n <- nlevels(dots[[1]])
if (nlevels(dots[[2]]) != n)
stop("arguments to Crossings() must all have same number of levels")
result <- crossings(as.numeric(dots[[1]]), as.numeric(dots[[2]]), n)
rownames(result) <- do.call("paste", c(dots, sep = ""))
result
}
vcdExtra/R/logLik.loglm.R 0000644 0001762 0000144 00000001270 13164013351 014705 0 ustar ligges users # logLik method for loglm objects, to allow use of AIC() and BIC()
# with MASS::loglm, giving comparable results to the use of these
# functions with glm(..., family=poisson) models.
# allow for non-integer frequencies
# allow for zero frequencies, with a zero= argument
logLik.loglm <- function(object, ..., zero=1E-10) {
fr <- if(!is.null(object$frequencies)) unclass(object$frequencies) else {
unclass(update(object, keep.frequencies = TRUE)$frequencies)
}
df <- prod(dim(fr)) - object$df
if (any(fr==0)) {
fr <- as.vector(fr)
fr[fr==0] <- zero
}
structure(sum((log(fr) - 1) * fr - lgamma(fr + 1)) - object$deviance/2,
df = df, class = "logLik")
}
vcdExtra/R/mosaic.glmlist.R 0000644 0001762 0000144 00000011376 13164013351 015310 0 ustar ligges users #' Mosaic Displays for a glmlist Object
#' @param x a glmlist object
#' @param selection the index or name of one glm in \code{x}
#' @param panel panel function
#' @param type a character string indicating whether the \code{"observed"} or the \code{"expected"} values of the table should be visualized
#' @param legend show a legend in the mosaic displays?
#' @param main either a logical, or a vector of character strings used for plotting the main title. If main is a logical and TRUE, the name of the selected glm object is used
#' @param ask should the function display a menu of models, when one is not specified in \code{selection}?
#' @param graphics use a graphic menu when \code{ask=TRUE}?
#' @param rows,cols when \code{ask=FALSE}, the number of rows and columns in which to plot the mosaics
#' @param newpage start a new page? (only applies to \code{ask=FALSE})
#' @param ... other arguments passed to \code{\link{mosaic.glm}}
#' @export
mosaic.glmlist <- function(x, selection,
panel=mosaic,
type=c("observed", "expected"),
legend=ask | !missing(selection),
main=NULL,
ask=TRUE, graphics=TRUE, rows, cols, newpage=TRUE,
...) {
# calls <- sapply(x, mod.call) # get model calls as strings
models <- names(x)
if (!is.null(main)) {
if (is.logical(main) && main)
main <- models
}
else main <- rep(main, length(x))
type=match.arg(type)
if (!missing(selection)){
if (is.character(selection)) selection <- gsub(" ", "", selection)
return(panel(x[[selection]], type=type, main=main[selection], legend=legend, ...))
}
# perhaps make these model labels more explicit for the menu
if (ask & interactive()){
repeat {
selection <- menu(models, graphics=graphics, title="Select Model to Plot")
if (selection == 0) break
else panel(x[[selection]], type=type, main=main[selection], legend=legend, ...)
}
}
else {
nmodels <- length(x)
mfrow <- mfrow(nmodels)
if (missing(rows) || missing(cols)){
rows <- mfrow[1]
cols <- mfrow[2]
}
if (newpage) grid.newpage()
lay <- grid.layout(nrow=rows, ncol = cols)
pushViewport(viewport(layout = lay, y = 0, just = "bottom"))
for (i in 1:rows) {
for (j in 1:cols){
if ((sel <-(i-1)*cols + j) > nmodels) break
pushViewport(viewport(layout.pos.row=i, layout.pos.col=j))
panel(x[[sel]], type=type, main=main[sel], newpage=FALSE, legend=legend, ...)
popViewport()
}
}
}
}
mosaic.loglmlist <- function(x, selection,
panel=mosaic,
type=c("observed", "expected"),
legend=ask | !missing(selection),
main=NULL,
ask=TRUE, graphics=TRUE, rows, cols, newpage=TRUE,
...) {
models <- names(x)
strings <- as.vector(sapply(x, function(x) x$model.string))
if (!is.null(main)) {
if (is.logical(main) && main)
main <- ifelse(as.vector(sapply(strings, is.null)), models, strings)
}
else main <- rep(main, length(x))
type=match.arg(type)
if (!missing(selection)){
if (is.character(selection)) selection <- gsub(" ", "", selection)
return(panel(x[[selection]], type=type, main=main[selection], legend=legend, ...))
}
# perhaps make these model labels more explicit for the menu
if (ask & interactive()){
repeat {
selection <- menu(models, graphics=graphics, title="Select Model to Plot")
if (selection == 0) break
else panel(x[[selection]], type=type, main=main[selection], legend=legend, ...)
}
}
else {
nmodels <- length(x)
mfrow <- mfrow(nmodels)
if (missing(rows) || missing(cols)){
rows <- mfrow[1]
cols <- mfrow[2]
}
if (newpage) grid.newpage()
lay <- grid.layout(nrow=rows, ncol = cols)
pushViewport(viewport(layout = lay, y = 0, just = "bottom"))
for (i in 1:rows) {
for (j in 1:cols){
if ((sel <-(i-1)*cols + j) > nmodels) break
pushViewport(viewport(layout.pos.row=i, layout.pos.col=j))
panel(x[[sel]], type=type, main=main[sel], newpage=FALSE, legend=legend, ...)
popViewport()
}
}
}
}
# from effects::utilities.R
mfrow <- function(n, max.plots=0){
# number of rows and columns for array of n plots
if (max.plots != 0 & n > max.plots)
stop(paste("number of plots =",n," exceeds maximum =", max.plots))
rows <- round(sqrt(n))
cols <- ceiling(n/rows)
c(rows, cols)
}
# from plot.lm: get model call as a string
# TODO: should use abbreviate()
mod.call <- function(x) {
cal <- x$call
if (!is.na(m.f <- match("formula", names(cal)))) {
cal <- cal[c(1, m.f)]
names(cal)[2L] <- ""
}
cc <- deparse(cal, 80)
nc <- nchar(cc[1L], "c")
abbr <- length(cc) > 1 || nc > 75
cap <- if (abbr)
paste(substr(cc[1L], 1L, min(75L, nc)), "...")
else cc[1L]
cap
}
vcdExtra/R/Summarise.R 0000644 0001762 0000144 00000007323 13164013351 014325 0 ustar ligges users # fixed buglet when deviance() returns a null
# fixed bug: residual df calculated incorrectly
# but this now depends on objects having a df.residual component
# TRUE for lm, glm, polr, negbin objects
# made generic, adding a glmlist method
Summarise <- function(object, ...) {
UseMethod("Summarise")
}
Summarise.glmlist <- function(object, ..., saturated = NULL, sortby=NULL)
{
ns <- sapply(object, function(x) length(x$residuals))
if (any(ns != ns[1L]))
stop("models were not all fitted to the same size of dataset")
nmodels <- length(object)
if (nmodels == 1)
return(Summarise.default(object[[1L]], saturated=saturated))
rval <- lapply(object, Summarise.default, saturated=saturated)
rval <- do.call(rbind, rval)
if (!is.null(sortby)) {
rval <- rval[order(rval[,sortby], decreasing=TRUE),]
}
rval
}
# could just do Summarise.loglmlist <- Summarise.glmlist
Summarise.loglmlist <- function(object, ..., saturated = NULL, sortby=NULL)
{
ns <- sapply(object, function(x) length(x$residuals))
if (any(ns != ns[1L]))
stop("models were not all fitted to the same size of dataset")
nmodels <- length(object)
if (nmodels == 1)
return(Summarise.default(object[[1L]], saturated=saturated))
rval <- lapply(object, Summarise.default, saturated=saturated)
rval <- do.call(rbind, rval)
if (!is.null(sortby)) {
rval <- rval[order(rval[,sortby], decreasing=TRUE),]
}
rval
}
Summarise.default <- function(object, ..., saturated = NULL, sortby=NULL)
{
## interface methods for logLik() and nobs()
## - use S4 methods if loaded
## - use residuals() if nobs() is not available
logLik0 <- if("stats4" %in% loadedNamespaces()) stats4::logLik else logLik
nobs0 <- function(x, ...) {
nobs1 <- if("stats4" %in% loadedNamespaces()) stats4::nobs else nobs
nobs2 <- function(x, ...) NROW(residuals(x, ...))
rval <- try(nobs1(x, ...), silent = TRUE)
if(inherits(rval, "try-error") | is.null(rval)) rval <- nobs2(x, ...)
return(rval)
}
dof <- function(x) {
if (inherits(x, "loglm")) {
rval <- x$df
} else {
rval <- try(x$df.residual, silent=TRUE)
}
if (inherits(rval, "try-error") || is.null(rval)) stop(paste("Can't determine residual df for a", class(x), "object"))
rval
}
## collect all objects
objects <- list(object, ...)
nmodels <- length(objects)
## check sample sizes
ns <- sapply(objects, nobs0)
if(any(ns != ns[1L])) stop("models were not all fitted to the same size of dataset")
## extract log-likelihood and df (number of parameters)
ll <- lapply(objects, logLik0)
par <- as.numeric(sapply(ll, function(x) attr(x, "df")))
df <- as.numeric(sapply(objects, function(x) dof(x)))
ll <- sapply(ll, as.numeric)
## compute saturated reference value (use 0 if deviance is not available)
if(is.null(saturated)) {
dev <- try(sapply(objects, deviance), silent = TRUE)
if(inherits(dev, "try-error") || any(sapply(dev, is.null))) {
saturated <- 0
} else {
saturated <- ll + dev/2
}
}
## setup ANOVA-style matrix
rval <- matrix(rep(NA, 5 * nmodels), ncol = 5)
colnames(rval) <- c("AIC", "BIC", "LR Chisq", "Df", "Pr(>Chisq)")
rownames(rval) <- as.character(sapply(match.call(), deparse)[-1L])[1:nmodels]
rval[,1] <- -2 * ll + 2 * par
rval[,2] <- -2 * ll + log(ns) * par
rval[,3] <- -2 * (ll - saturated)
rval[,4] <- df
rval[,5] <- pchisq(rval[,3], df, lower.tail = FALSE)
if (!is.null(sortby)) {
rval <- rval[order(rval[,sortby], decreasing=TRUE),]
}
## return
structure(as.data.frame(rval), heading = "Likelihood summary table:", class = c("anova", "data.frame"))
}
vcdExtra/R/summarise-old.R 0000644 0001762 0000144 00000005712 13164013351 015141 0 ustar ligges users
# summarise a glm object or glmlist
summarise <- function(...) {
.Deprecated("LRstats")
LRstats(...)
}
# summarise <- function(object, ...) {
# UseMethod("summarise")
# }
#
# stat.summarise <- function(deviance, df, onames, n) {
# p <- pchisq(deviance, df, lower.tail=FALSE)
# aic <- deviance - 2*df
# if (missing(n)) {
# result <- data.frame(aic, deviance, df, p)
# names(result) <- c("AIC", "LR Chisq", "Df", "Pr(>Chisq)")
# }
# else {
# bic <- deviance - log(n)*df
# result <- data.frame(aic, bic, deviance, df, p)
# names(result) <- c("AIC", "BIC", "LR Chisq", "Df", "Pr(>Chisq)")
# }
#
# rownames(result) <- onames
# attr(result, "heading") <- "Model Summary:"
# class(result) <- c("anova", "data.frame")
# result
# }
#
#
# summarise.glm <-function(object, ..., test=NULL){
# dotargs <- list(...)
# is.glm <- unlist(lapply(dotargs, function(x) inherits(x, "glm")))
# dotargs <- dotargs[is.glm]
# if (length(dotargs))
# return(summarise.glmlist(c(list(object), dotargs), test = test))
#
# oname <- as.character(sys.call())[2]
# result <- stat.summarise(object$deviance, object$df.residual, oname, sum(fitted(object)))
# result
# }
#
# summarise.glmlist <-function(object, ..., test=NULL, sortby=NULL){
# nmodels <- length(object)
# if (nmodels == 1)
# return(summarise.glm(object[[1]], test = test))
# if (is.null(names(object))) {
# oname <- as.character(sys.call())[-1]
# oname <- oname[1:length(object)]
# }
# else oname <- names(object)
#
# resdf <- as.numeric(lapply(object, function(x) x$df.residual))
# resdev <- as.numeric(lapply(object, function(x) x$deviance))
# n <- as.numeric(lapply(object, function(x) sum(fitted(x))))
# result <- stat.summarise(resdev, resdf, oname, n)
# if (!is.null(sortby)) {
# result <- result[order(result[,sortby], decreasing=TRUE),]
# }
# result
# }
#
#
# summarise.loglm <-function(object, ...){
# dotargs <- list(...)
# is.loglm <- unlist(lapply(dotargs, function(x) inherits(x, "loglm")))
# dotargs <- dotargs[is.loglm]
# if (length(dotargs))
# return(summarise.loglmlist(c(list(object), dotargs)))
#
# oname <- as.character(sys.call())[2]
# result <- stat.summarise(object$deviance, object$df, oname, sum(fitted(object)))
# result
# }
#
# summarise.loglmlist <-function(object, ..., sortby=NULL){
# nmodels <- length(object)
# if (nmodels == 1)
# return(summarise.loglm(object[[1]]))
# if (is.null(names(object))) {
# oname <- as.character(sys.call())[-1]
# oname <- oname[1:length(object)]
# }
# else oname <- names(object)
#
# resdf <- as.numeric(lapply(object, function(x) x$df))
# resdev <- as.numeric(lapply(object, function(x) x$deviance))
# n <- as.numeric(lapply(object, function(x) sum(fitted(x))))
# result <- stat.summarise(resdev, resdf, oname, n)
# if (!is.null(sortby)) {
# result <- result[order(result[,sortby], decreasing=TRUE),]
# }
# result
# }
#
vcdExtra/R/zero.test.R 0000644 0001762 0000144 00000002610 13164013351 014307 0 ustar ligges users # Score test for zero inflation in Poisson data
#https://stats.stackexchange.com/questions/118322/how-to-test-for-zero-inflation-in-a-dataset
# References:
# Broek, Jan van den. 1995. ?A Score Test for Zero Inflation in a Poisson Distribution.? Biometrics 51 (2): 738?43. doi:10.2307/2532959.
# Yang, Zhao, James W. Hardin, and Cheryl L. Addy. 2010. ?Score Tests for Zero-Inflation in Overdispersed Count Data.? Communications in Statistics - Theory and Methods 39 (11): 2008?30. doi:10.1080/03610920902948228
# Van den Broek, J. (1995). A Score Test for Zero Inflation in a Poisson Distribution. Biometrics, 51(2), 738-743. doi:10.2307/2532959
zero.test <- function(x) {
if(is.table(x)) { # expand to vector of values
if(length(dim(x)) > 1) stop ("x must be a 1-way table")
x <- rep(as.numeric(names(x)), unname(c(x)))
}
lambda <- mean(x)
p0_tilde <- exp(-lambda)
n0 <- sum(1*(!(x >0)))
n <- length(x)
numerator <- (n0 - n*p0_tilde)^2
denominator <- n*p0_tilde*(1-p0_tilde) - n*lambda*(p0_tilde^2)
stat <- numerator/denominator
pvalue <- pchisq(stat,df=1, ncp=0, lower.tail=FALSE)
result <- list(statistic=stat, df=1, prob=pvalue)
cat(paste("Score test for zero inflation\n\n",
"\tChi-square =", round(stat,5), "\n",
"\tdf = 1\n",
"\tpvalue:", format.pval(pvalue), "\n"))
invisible(result)
}
vcdExtra/R/cutfac.R 0000644 0001762 0000144 00000000564 13164013351 013625 0 ustar ligges users # Cut a variable to a factor
cutfac <- function(x, breaks = NULL, q=10) {
if(is.null(breaks)) breaks <- unique(quantile(x, 0:q/q))
x <- cut(x, breaks, include.lowest = TRUE, right = FALSE)
levels(x) <- paste(breaks[-length(breaks)], ifelse(diff(breaks) > 1,
c(paste("-", breaks[-c(1, length(breaks))] - 1, sep = ""), "+"), ""), sep = "")
return(x)
}
vcdExtra/R/expand.dft.R 0000644 0001762 0000144 00000002162 13427613416 014421 0 ustar ligges users # Author: Marc Schwarz
# Ref: http://tolstoy.newcastle.edu.au/R/e6/help/09/01/1873.html
expand.dft <- function(x, var.names = NULL, freq = "Freq", ...)
{
# allow: a table object, or a data frame in frequency form
if(inherits(x, "table"))
x <- as.data.frame.table(x, responseName = freq)
freq.col <- which(colnames(x) == freq)
if (length(freq.col) == 0)
stop(paste(sQuote("freq"), "not found in column names"))
DF <- sapply(1:nrow(x),
function(i) x[rep(i, each = x[i, freq.col, drop = TRUE]), ],
simplify = FALSE)
DF <- do.call("rbind", DF)[, -freq.col, drop=FALSE]
for (i in 1:ncol(DF))
{
DF[[i]] <- type.convert(as.character(DF[[i]]), ...)
}
rownames(DF) <- NULL
if (!is.null(var.names))
{
if (length(var.names) < dim(DF)[2])
{
stop(paste("Too few", sQuote("var.names"), "given."))
} else if (length(var.names) > dim(DF)[2]) {
stop(paste("Too many", sQuote("var.names"), "given."))
} else {
names(DF) <- var.names
}
}
DF
}
# make this a synonym
expand.table <- expand.dft
vcdExtra/R/HLtest.R 0000644 0001762 0000144 00000004012 13164013351 013553 0 ustar ligges users # Functions for Hosmer Lemeshow test
# original function downloaded from
# http://sas-and-r.blogspot.com/2010/09/example-87-hosmer-and-lemeshow-goodness.html
#
# see also: MKmisc::gof.test for more general versions
HLtest <- HosmerLemeshow <- function(model, g=10) {
if (!inherits(model, "glm")) stop("requires a binomial family glm")
if (!family(model)$family == 'binomial') stop("requires a binomial family glm")
y <- model$y
yhat <- model$fitted.values
cutyhat = cut(yhat,
breaks = quantile(yhat, probs=seq(0, 1, 1/g)),
include.lowest=TRUE)
obs = xtabs(cbind(1 - y, y) ~ cutyhat)
exp = xtabs(cbind(1 - yhat, yhat) ~ cutyhat)
chi = (obs - exp)/sqrt(exp)
# browser()
table <- data.frame(cut=dimnames(obs)$cutyhat,
total= as.numeric(apply(obs, 1, sum)),
obs=as.numeric(as.character(obs[,1])),
exp=as.numeric(as.character(exp[,1])),
chi=as.numeric(as.character(chi[,1]))
)
rownames(table) <- 1:g
chisq = sum(chi^2)
p = 1 - pchisq(chisq, g - 2)
result <- list(table=table, chisq=chisq, df=g-2, p.value=p, groups=g, call=model$call)
class(result) <- "HLtest"
return(result)
}
print.HLtest <- function(x, ...) {
heading <- "Hosmer and Lemeshow Goodness-of-Fit Test"
df <- data.frame("ChiSquare"=x$chisq, df=x$df, "P_value"= x$p.value)
cat(heading,"\n\n")
cat("Call:\n")
print(x$call)
print(df, row.names=FALSE)
invisible(x)
}
# Q: how to print **s next to larg chisq components?
summary.HLtest <- function(object, ...) {
heading <- "Partition for Hosmer and Lemeshow Goodness-of-Fit Test"
cat(heading,"\n\n")
print(object$table)
print(object)
}
## Q: how to display any large chi residuals on the bars??
rootogram.HLtest <- function(x, ...) {
rootogram(as.numeric(x$table$obs), as.numeric(x$table$exp),
xlab="Fitted value group", names=1:x$groups, ...)
}
plot.HLtest <- function(x, ...) {
rootogram.HLtest(x, ...)
}
vcdExtra/R/loglin-utilities.R 0000644 0001762 0000144 00000013750 13164013351 015656 0 ustar ligges users #' Loglinear Model Utilities
#' These functions generate lists of terms to specify a loglinear model
#' in a form compatible with loglin and provide for conversion to an
#' equivalent loglm specification. They allow for a more conceptual
#' way to specify such models.
#' models of joint independence, of some factors wrt one or more other factors
#' @param nf number of factors for which to generate model
#' @param table a contingency table used for factor names, typically the output from \code{\link[base]{table}}
#' @param factors names of factors used in the model when \code{table} is not specified
#' @param with indices of the factors against which others are considered jointly independent
#' @export
joint <- function(nf, table=NULL, factors=1:nf, with=nf) {
if (!is.null(table)) factors <- names(dimnames(table))
if (nf == 1) return (list(term1=factors[1]))
if (nf == 2) return (list(term1=factors[1], term2=factors[2]))
others <- setdiff(1:nf, with)
result <- list(term1=factors[others], term2=factors[with])
result
}
#' models of conditional independence of some factors wrt one or more other factors
#' @param nf number of factors for which to generate model
#' @param table a contingency table used for factor names, typically the output from \code{\link[base]{table}}
#' @param factors names of factors used in the model when \code{table} is not specified
#' @param with indices of the factors against which others are considered conditionally independent
#' @export
conditional <- function(nf, table=NULL, factors=1:nf, with=nf) {
if (!is.null(table)) factors <- names(dimnames(table))
if (nf == 1) return (list(term1=factors[1]))
if (nf == 2) return (list(term1=factors[1], term2=factors[2]))
main <- setdiff(1:nf, with)
others <- matrix(factors[with], length(with), length(main))
result <- rbind(factors[main], others)
result <- as.list(as.data.frame(result, stringsAsFactors=FALSE))
names(result) <- paste('term', 1:length(result), sep='')
result
}
#' models of mutual independence of all factors
#' @param nf number of factors for which to generate model
#' @param table a contingency table used for factor names, typically the output from \code{\link[base]{table}}
#' @param factors names of factors used in the model when \code{table} is not specified
#' @export
mutual <- function(nf, table=NULL, factors=1:nf) {
if (!is.null(table)) factors <- names(dimnames(table))
result <- sapply(factors[1:nf], list)
names(result) <- paste('term', 1:length(result), sep='')
result
}
#' saturated model: highest-order interaction
#' @param nf number of factors for which to generate model
#' @param table a contingency table used for factor names, typically the output from \code{\link[base]{table}}
#' @param factors names of factors used in the model when \code{table} is not specified
#' @export
saturated <- function(nf, table=NULL, factors=1:nf) {
if (!is.null(table)) factors <- names(dimnames(table))
list(term1=factors[1:nf])
}
# models of conditional independence, given one pair of variables
## Not needed: handled by condit, with length(with)>1
#condit2 <- function(nf, factors=1:nf, with=1:2) {
# if (nf == 1) return (list(term1=factors[1]))
# if (nf == 2) return (list(term1=factors[1], term2=factors[2]))
# others <- setdiff(1:nf, with)
# result <- rbind(factors[with], cbind(factors[others], factors[others]))
# result <- as.list(as.data.frame(result, stringsAsFactors=FALSE))
# names(result) <- paste('term', 1:length(result), sep='')
# result
#}
#' markov models of a given order
#' @param nf number of factors for which to generate model
#' @param table a contingency table used for factor names, typically the output from \code{\link[base]{table}}
#' @param factors names of factors used in the model when \code{table} is not specified
#' @param order order of the markov chain
#' @export
markov <- function(nf, factors=1:nf, order=1) {
if (nf == 1) return (list(term1=factors[1]))
if (nf == 2) return (list(term1=factors[1], term2=factors[2]))
if (length(factors) < order+2) {
warning(paste('Not enough factors for order', order, 'Markov chain; using order=1'))
order <-1
result <- rbind(factors[1:(nf-1)], factors[2:nf])
}
else {
if (nf <= order+1) result <- factors[1:nf]
else {
result <- NULL
for (i in 1:(order+1))
result <- rbind(result, factors[i:(nf-order+i-1)])
}
}
result <- as.list(as.data.frame(result, stringsAsFactors=FALSE))
names(result) <- paste('term', 1:length(result), sep='')
result
}
#' convert a loglin model to a model formula for loglm
#' @param x a list of terms in a loglinear model, such as returned by \code{joint}, \code{conditional}, \dots
#' @param env environment in which to evaluate the formula
#' @source Code from Henrique Dallazuanna, , R-help 7-4-2013
loglin2formula <- function(x, env = parent.frame()) {
terms <- lapply(x, paste, collapse = ":")
formula(sprintf(" ~ %s", do.call(paste, c(terms, sep = "+"))), env=env)
}
#' convert a loglin model to a string, using bracket notation for the high-order terms
#' @param x a list of terms in a loglinear model, such as returned by \code{joint}, \code{conditional}, \dots
#' @param brackets characters to use to surround model terms. Either a single character string containing two characters
#' or a character vector of length two.
#' @param sep characters used to separate factor names within a term
#' @param collapse characters used to separate terms
#' @param abbrev
loglin2string <- function(x, brackets = c('[', ']'), sep=',', collapse=' ', abbrev) {
if (length(brackets)==1 && (nchar(brackets)>1)) brackets <- unlist(strsplit(brackets, ""))
terms <- lapply(x, paste, collapse=sep)
terms <- paste(brackets[1], terms, brackets[2], sep='')
paste(terms, collapse= ' ')
}
vcdExtra/R/update.xtabs.R 0000644 0001762 0000144 00000001264 13164013351 014760 0 ustar ligges users update.xtabs <-
function (object, formula., ..., evaluate = TRUE)
{
if (is.null(call<-attr(object, "call")))
stop("need an object with call component")
extras <- match.call(expand.dots = FALSE)$...
if (!missing(formula.))
call$formula <- update.formula(call$formula, formula.)
if (length(extras)) {
existing <- !is.na(match(names(extras), names(call)))
for (a in names(extras)[existing]) call[[a]] <- extras[[a]]
if (any(!existing)) {
call <- c(as.list(call), extras[!existing])
call <- as.call(call)
}
}
if (evaluate)
eval(call, parent.frame())
else call
}
vcdExtra/R/logseries.R 0000644 0001762 0000144 00000011472 13164013351 014354 0 ustar ligges users ## Original from gmlss.dist
## I think this is working correctly 01/03/10
#LG <- function (mu.link = "logit")
#{
# mstats <- checklink("mu.link", "LG", substitute(mu.link),c("logit", "probit", "cloglog", "cauchit", "log", "own"))
# structure(
# list(family = c("LG", "Logarithmic"),
# parameters = list(mu = TRUE), # the mean
# nopar = 1,
# type = "Discrete",
# mu.link = as.character(substitute(mu.link)),
# mu.linkfun = mstats$linkfun,
# mu.linkinv = mstats$linkinv,
# mu.dr = mstats$mu.eta,
# dldm = function(y,mu) (y/mu)+1/((1-mu)*log(1-mu)),
# d2ldm2 = function(y,mu)
# {
# dldm <- (y/mu)+1/((1-mu)*log(1-mu))
# d2ldm2 <- -dldm^2
# d2ldm2
# },
# G.dev.incr = function(y,mu,...) -2*dLG(x = y, mu = mu, log = TRUE),
# rqres = expression(rqres(pfun="pLG", type="Discrete", ymin=1, y=y, mu=mu)),
# mu.initial =expression({mu <- 0.9 } ),
# mu.valid = function(mu) all(mu > 0 & mu < 1),
# y.valid = function(y) all(y > 0)
# ),
# class = c("gamlss.family","family"))
#}
#-----------------------------------------------------------------------------------------
dlogseries<-function(x, prob = 0.5, log = FALSE)
{
if (any(prob <= 0) | any(prob >= 1) ) stop(paste("prob must be greater than 0 and less than 1", "\n", ""))
if (any(x <= 0) ) stop(paste("x must be >0", "\n", ""))
logfy <- x*log(prob)-log(x)-log(-log(1-prob))
if(log == FALSE) fy <- exp(logfy) else fy <- logfy
fy
}
#----------------------------------------------------------------------------------------
plogseries <- function(q, prob = 0.5, lower.tail = TRUE, log.p = FALSE)
{
if (any(prob <= 0) | any(prob >= 1) ) stop(paste("prob must be greater than 0 and less than 1", "\n", ""))
if (any(q <= 0) ) stop(paste("q must be >0", "\n", ""))
ly <- length(q)
FFF <- rep(0,ly)
nmu <- rep(prob, length = ly)
j <- seq(along=q)
for (i in j)
{
y.y <- q[i]
mm <- nmu[i]
allval <- seq(1,y.y)
pdfall <- dlogseries(allval, prob = mm, log = FALSE)
FFF[i] <- sum(pdfall)
}
cdf <- FFF
cdf <- if(lower.tail==TRUE) cdf else 1-cdf
cdf <- if(log.p==FALSE) cdf else log(cdf)
cdf
}
#----------------------------------------------------------------------------------------
qlogseries <- function(p, prob=0.5, lower.tail = TRUE, log.p = FALSE,
max.value = 10000)
{
if (any(prob <= 0) | any(prob >= 1) ) stop(paste("prob must be greater than 0 and less than 1", "\n", ""))
if (any(p < 0) | any(p > 1.0001)) stop(paste("p must be between 0 and 1", "\n", ""))
if (log.p==TRUE) p <- exp(p) else p <- p
if (lower.tail==TRUE) p <- p else p <- 1-p
ly <- length(p)
QQQ <- rep(0,ly)
nmu <- rep(prob, length = ly)
for (i in seq(along=p))
{
cumpro <- 0
if (p[i]+0.000000001 >= 1) QQQ[i] <- Inf
else
{
for (j in seq(from = 1, to = max.value))
{
cumpro <- plogseries(j, prob = nmu[i], log.p = FALSE)
QQQ[i] <- j
if (p[i] <= cumpro ) break
}
}
}
QQQ
}
#----------------------------------------------------------------------------------------
rlogseries <- function(n, prob = 0.5)
{
if (any(prob <= 0) | any(prob >= 1) ) stop(paste("prob must be greater than 0 and less than 1", "\n", ""))
if (any(n <= 0)) stop(paste("n must be a positive integer", "\n", ""))
n <- ceiling(n)
p <- runif(n)
r <- qlogseries(p, prob=prob)
r
}
#----------------------------------------------------------------------------------------
vcdExtra/R/print.Kappa.R 0000644 0001762 0000144 00000001032 13164013351 014536 0 ustar ligges users # Print method for Kappa: Add a column showing z values
## DONE: now set digits
## DONE: now include CI
print.Kappa <-
function (x, digits=max(getOption("digits") - 3, 3), CI=FALSE, level=0.95, ...)
{
tab <- rbind(x$Unweighted, x$Weighted)
z <- tab[,1] / tab[,2]
tab <- cbind(tab, z)
if (CI) {
q <- qnorm((1 + level)/2)
lower <- tab[,1] - q * tab[,2]
upper <- tab[,1] + q * tab[,2]
tab <- cbind(tab, lower, upper)
}
rownames(tab) <- names(x)[1:2]
print(tab, digits=digits, ...)
invisible(x)
}
vcdExtra/R/Kway.R 0000644 0001762 0000144 00000001711 13164013351 013266 0 ustar ligges users # Generate and fit all 1-way, 2-way, ... k-way terms in a glm
Kway <- function(formula, family=poisson, data, ..., order=nt, prefix="kway") {
if (is.character(family))
family <- get(family, mode = "function", envir = parent.frame())
if (is.function(family))
family <- family()
if (is.null(family$family)) {
print(family)
stop("'family' not recognized")
}
if (missing(data))
data <- environment(formula)
models <- list()
mod <- glm(formula, family=family, data, ...)
mod$call$formula <- formula
terms <- terms(formula)
tl <- attr(terms, "term.labels")
nt <- length(tl)
models[[1]] <- mod
for(i in 2:order) {
models[[i]] <- update(mod, substitute(.~.^p, list(p = i)))
} # null model
mod0 <- update(mod, .~1)
models <- c(list(mod0), models)
names(models) <- paste(prefix, 0:order, sep = ".")
class(models) <- "glmlist"
models
}
vcdExtra/R/modFit.R 0000644 0001762 0000144 00000001647 13164013351 013605 0 ustar ligges users ##
## One-line summary of model fit for a glm/loglm object
##
`modFit` <-
function(x, ...) UseMethod("modFit")
modFit.glm <- function(x, stats="chisq", digits=2, ...) {
if (!inherits(x,"glm")) stop("modFit requires a glm object")
result <- NULL
if ("chisq" %in% stats)
result <- paste("G^2(",x$df.residual,")=",
formatC(x$deviance,digits=digits,format="f"),sep="")
if ("aic" %in% stats)
result <- paste(result, " AIC=", formatC(x$aic,digits=digits,format="f"),sep="")
result
}
modFit.loglm <- function(x, stats="chisq", digits=2, ...) {
if (!inherits(x,"loglm")) stop("modFit requires a loglm object")
result <- NULL
if ("chisq" %in% stats)
result <- paste("G^2(",x$df,")=",
formatC(x$deviance,digits=digits,format="f"),sep="")
if ("aic" %in% stats) {
aic<-x$deviance-x$df*2
result <- paste(result, " AIC=", formatC(aic,digits=digits,format="f"),sep="")
}
result
}
vcdExtra/R/glmlist.R 0000644 0001762 0000144 00000004625 13164013351 014035 0 ustar ligges users # glmlist - make a glmlist object containing a list of fitted glm objects with their names
# borrowing code from Hmisc::llist
glmlist <- function(...) {
args <- list(...);
lname <- names(args)
name <- vname <- as.character(sys.call())[-1]
for (i in 1:length(args)) {
vname[i] <- if (length(lname) && lname[i] != "")
lname[i]
else name[i]
}
names(args) <- vname[1:length(args)]
is.glm <- unlist(lapply(args, function(x) inherits(x, "glm")))
if (!all(is.glm)) {
warning("Objects ", paste(vname[!is.glm], collapse=', '),
" removed because they are not glm objects")
args <- args[is.glm]
}
class(args) <- "glmlist"
return(args);
}
# loglmlist - do the same for loglm objects
loglmlist <- function(...) {
args <- list(...);
lname <- names(args)
name <- vname <- as.character(sys.call())[-1]
for (i in 1:length(args)) {
vname[i] <- if (length(lname) && lname[i] != "")
lname[i]
else name[i]
}
names(args) <- vname[1:length(args)]
is.loglm <- unlist(lapply(args, function(x) inherits(x, "loglm")))
if (!all(is.loglm)) {
warning("Objects ", paste(vname[!is.loglm], collapse=', '),
" removed because they are not loglm objects")
args <- args[is.loglm]
}
class(args) <- "loglmlist"
return(args);
}
# generic version: named list
nlist <- function(...) {
args <- list(...);
lname <- names(args)
name <- vname <- as.character(sys.call())[-1]
for (i in 1:length(args)) {
vname[i] <- if (length(lname) && lname[i] != "")
lname[i]
else name[i]
}
names(args) <- vname[1:length(args)]
return(args);
}
# coeficient method for a glmlist (from John Fox, r-help, 10-28-2014)
coef.glmlist <- function(object, result=c("list", "matrix", "data.frame"),
...){
result <- match.arg(result)
coefs <- lapply(object, coef)
if (result == "list") return(coefs)
coef.names <- unique(unlist(lapply(coefs, names)))
n.mods <- length(object)
coef.matrix <- matrix(NA, length(coef.names), n.mods)
rownames(coef.matrix) <- coef.names
colnames(coef.matrix) <- names(object)
for (i in 1:n.mods){
coef <- coef(object[[i]])
coef.matrix[names(coef), i] <- coef
}
if (result == "matrix") return(coef.matrix)
as.data.frame(coef.matrix)
}
vcdExtra/R/mcaplot.R 0000644 0001762 0000144 00000010163 13164013351 014013 0 ustar ligges users #' ---
#' title: "Custom plot function for mjca"
#' date: "28 Jan 2016"
#' ---
#'
#'
#' @param obj An \code{"mjca"} object
#' @param map Character string specifying the map type. Allowed options include
#' \code{"symmetric"} (default),
#' \code{"rowprincipal"}, \code{"colprincipal"}, \code{"symbiplot"},
#' \code{"rowgab"}, \code{"colgab"}, \code{"rowgreen"}, \code{"colgreen"}
#' @param dim Dimensions to plot, a vector of length 2.
#' @param col Vector of colors, one for each factor in the MCA
#' @param pch Vector of point symbols for the category levels, one for each factor
#' @param cex Character size for points and level labels
#' @param pos Position of level labels relative to the category points; either a single number
#' or a vector of length equal to the number of category points.
#' @param lines A logical or an integer vector indicating which factors are to be
#' joined with lines using \code{\link{multilines}}
#' @param lwd Line width(s) for the lines
#' @param legend Logical; draw a legend for the factor names?
#' @param legend.pos Position of the legend in the plot
#' @param xlab,ylab Labels for horizontal and vertical axes. The default, \code{"_auto_" }
#' means that the function auto-generates a label of the form \code{Dimension X (xx.x %)}
#' @param rev.axes A logical vector of length 2, where TRUE reverses the direction of the
#' corresponding axis.
#' @param ... Arguments passed down to \code{plot}
#' @return Returns the coordinates of the category points invisibly
#'
#' @author Michael Friendly
#' @seealso \code{\link{plot.mjca}}
#' @examples
#' data(Titanic)
#' titanic.mca <- mjca(Titanic)
#' mcaplot(titanic.mca, legend=TRUE, legend.pos="topleft")
#'
#' data(HairEyeColor)
#' haireye.mca <- mjca(HairEyeColor)
#' mcaplot(haireye.mca, legend=TRUE, cex.lab=1.3)
mcaplot <- function(obj, map="symmetric", dim=1:2,
col=c("blue", "red", "brown", "black", "green3", "purple"),
pch=15:20, cex=1.2, pos=3,
lines=TRUE, lwd=2,
legend=FALSE, legend.pos="topright",
xlab = "_auto_",
ylab = "_auto_",
rev.axes = c(FALSE, FALSE),
...) {
if(!requireNamespace("ca", quietly=TRUE)) stop("The ca package is required")
if(!inherits(obj, "mjca")) stop("Only defined for mjca objects")
coords <- cacoord(obj, type=map, rows=FALSE)
coords <- data.frame(coords, obj$factors) # extract factor names & levels
nlev <- obj$levels.n
nfac <- length(nlev)
pch <- rep_len(pch, nfac)
col <- rep_len(col, nfac)
lwd <- rep_len(lwd, nfac)
rev.axes <- rep(rev.axes, length.out=2)
if(any(dim > ncol(coords)))
stop("dim must be valid dimensions of the coordinates")
labs <- pctlab(obj)
if (xlab == "_auto_") xlab <- labs[dim[1]]
if (ylab == "_auto_") ylab <- labs[dim[2]]
if(isTRUE(rev.axes[1])) coords[, dim[1]] <- -coords[, dim[1]]
if(isTRUE(rev.axes[2])) coords[, dim[2]] <- -coords[, dim[2]]
plot(coords[, dim], type='n', asp=1, xlab=xlab, ylab=ylab, ...)
points(coords[,dim], pch=rep(pch, nlev), col=rep(col, nlev), cex=cex)
text(coords[,dim], labels=coords$level, col=rep(col, nlev), pos=pos, cex=cex, xpd=TRUE)
if (is.logical(lines)) lines <- if(lines) 1:nfac else NULL
if(length(lines))
multilines(coords[, dim], group=coords$factor, which=lines, col=col, lwd=lwd)
abline(h = 0, v = 0, lty = "longdash", col="gray")
if (legend) {
factors <- coords$factor
factors <- factors[!duplicated(factors)]
legend(legend.pos, legend=factors,
title="Factor", title.col="black",
col=col, text.col=col, pch=pch,
bg=rgb(.95, .95, .95, .3), cex=cex)
}
invisible(coords)
}
pctlab <- function(obj, prefix="Dimension ", decimals=1) {
values <- obj$sv^2
if (obj$lambda == "JCA"){
pct <- rep_len(NA, length(values))
} else {
if (obj$lambda == "adjusted") {
values <- obj$inertia.e
pct <- round(100 * values, decimals)
} else {
pct <- round(100 * values / sum(values), decimals)
}
}
pctval <- ifelse(is.na(pct), NULL, paste0(" (", pct, "%)"))
paste0(prefix, 1:length(values), pctval)
}
vcdExtra/R/LRstats.R 0000644 0001762 0000144 00000007275 13164013351 013762 0 ustar ligges users # fixed buglet when deviance() returns a null
# fixed bug: residual df calculated incorrectly
# but this now depends on objects having a df.residual component
# TRUE for lm, glm, polr, negbin objects
# made generic, adding a glmlist method
LRstats <- function(object, ...) {
UseMethod("LRstats")
}
LRstats.glmlist <- function(object, ..., saturated = NULL, sortby=NULL)
{
ns <- sapply(object, function(x) length(x$residuals))
if (any(ns != ns[1L]))
stop("models were not all fitted to the same size of dataset")
nmodels <- length(object)
if (nmodels == 1)
return(LRstats.default(object[[1L]], saturated=saturated))
rval <- lapply(object, LRstats.default, saturated=saturated)
rval <- do.call(rbind, rval)
if (!is.null(sortby)) {
rval <- rval[order(rval[,sortby], decreasing=TRUE),]
}
rval
}
# could just do LRstats.loglmlist <- LRstats.glmlist
LRstats.loglmlist <- function(object, ..., saturated = NULL, sortby=NULL)
{
ns <- sapply(object, function(x) length(x$residuals))
if (any(ns != ns[1L]))
stop("models were not all fitted to the same size of dataset")
nmodels <- length(object)
if (nmodels == 1)
return(LRstats.default(object[[1L]], saturated=saturated))
rval <- lapply(object, LRstats.default, saturated=saturated)
rval <- do.call(rbind, rval)
if (!is.null(sortby)) {
rval <- rval[order(rval[,sortby], decreasing=TRUE),]
}
rval
}
LRstats.default <- function(object, ..., saturated = NULL, sortby=NULL)
{
## interface methods for logLik() and nobs()
## - use S4 methods if loaded
## - use residuals() if nobs() is not available
logLik0 <- if("stats4" %in% loadedNamespaces()) stats4::logLik else logLik
nobs0 <- function(x, ...) {
nobs1 <- if("stats4" %in% loadedNamespaces()) stats4::nobs else nobs
nobs2 <- function(x, ...) NROW(residuals(x, ...))
rval <- try(nobs1(x, ...), silent = TRUE)
if(inherits(rval, "try-error") | is.null(rval)) rval <- nobs2(x, ...)
return(rval)
}
dof <- function(x) {
if (inherits(x, "loglm")) {
rval <- x$df
} else {
rval <- try(x$df.residual, silent=TRUE)
}
if (inherits(rval, "try-error") || is.null(rval)) stop(paste("Can't determine residual df for a", class(x), "object"))
rval
}
## collect all objects
objects <- list(object, ...)
nmodels <- length(objects)
## check sample sizes
ns <- sapply(objects, nobs0)
if(any(ns != ns[1L])) stop("models were not all fitted to the same size of dataset")
## extract log-likelihood and df (number of parameters)
ll <- lapply(objects, logLik0)
par <- as.numeric(sapply(ll, function(x) attr(x, "df")))
df <- as.numeric(sapply(objects, function(x) dof(x)))
ll <- sapply(ll, as.numeric)
## compute saturated reference value (use 0 if deviance is not available)
if(is.null(saturated)) {
dev <- try(sapply(objects, deviance), silent = TRUE)
if(inherits(dev, "try-error") || any(sapply(dev, is.null))) {
saturated <- 0
} else {
saturated <- ll + dev/2
}
}
## setup ANOVA-style matrix
rval <- matrix(rep(NA, 5 * nmodels), ncol = 5)
colnames(rval) <- c("AIC", "BIC", "LR Chisq", "Df", "Pr(>Chisq)")
rownames(rval) <- as.character(sapply(match.call(), deparse)[-1L])[1:nmodels]
rval[,1] <- -2 * ll + 2 * par
rval[,2] <- -2 * ll + log(ns) * par
rval[,3] <- -2 * (ll - saturated)
rval[,4] <- df
rval[,5] <- pchisq(rval[,3], df, lower.tail = FALSE)
if (!is.null(sortby)) {
rval <- rval[order(rval[,sortby], decreasing=TRUE),]
}
## return
structure(as.data.frame(rval), heading = "Likelihood summary table:", class = c("anova", "data.frame"))
}
vcdExtra/R/mosaic3d.R 0000644 0001762 0000144 00000016342 13164013351 014063 0 ustar ligges users #####################################
## Produce a 3D mosaic plot using rgl
#####################################
# TODO: provide formula interface
# TODO: handle zero margins (causes display to be erased in shapelist3d)
# DONE: handle zero cells
# DONE: generalize the calculation of residuals
# DONE: allow display of type=c("observed", "expected")
# DONE: if ndim>3, provide for labels at max or min
# DONE: make object oriented and provide a loglm method
# mosaic3d: provide observed array of counts and either residuals, expected frequencies,
# or a loglin set of margins to fit
mosaic3d <- function(x, ...) {
UseMethod("mosaic3d")
}
mosaic3d.loglm <-
function (x, type = c("observed", "expected"),
residuals_type = c("pearson", "deviance"),
# gp = shading_hcl, gp_args = list(),
...)
{
residuals_type <- match.arg(tolower(residuals_type), c("pearson", "deviance"))
if (is.null(x$fitted))
x <- update(x, fitted = TRUE)
expected <- fitted(x)
residuals <- residuals(x, type = "pearson")
observed <- residuals * sqrt(expected) + expected
if (residuals_type == "deviance")
residuals <- residuals(x, type = "deviance")
# gp <- if (inherits(gp, "grapcon_generator"))
# do.call("gp", c(list(observed, residuals, expected, x$df),
# as.list(gp_args)))
# else gp
mosaic3d.default(observed, residuals = residuals, expected = expected,
type = type, residuals_type = residuals_type,
# gp = gp,
...)
}
mosaic3d.default <- function(x, expected=NULL, residuals=NULL,
type = c("observed", "expected"), residuals_type = NULL,
shape=rgl::cube3d(alpha=alpha), alpha=0.5,
spacing=0.1, split_dir=1:3,
shading=shading_basic, interpolate=c(2,4), zero_size=.05,
label_edge,
labeling_args=list(), newpage=TRUE, box=FALSE, ...) {
if (!requireNamespace("rgl")) stop("rgl is required")
type <- match.arg(type)
if (is.null(residuals)) {
residuals_type <- if (is.null(residuals_type))
"pearson"
else match.arg(tolower(residuals_type), c("pearson",
"deviance", "ft"))
}
## convert structable object
if (is.structable(x)) {
x <- as.table(x)
}
## table characteristics
levels <- dim(x)
ndim <- length(levels)
dn <- dimnames(x)
if (is.null(dn))
dn <- dimnames(x) <- lapply(levels, seq)
vnames <- names(dimnames(x))
if (is.null(vnames))
vnames <- names(dn) <- names(dimnames(x)) <- LETTERS[1:ndim]
## replace NAs by 0
if (any(nas <- is.na(x))) x[nas] <- 0
## model fitting:
## calculate expected if needed
if ((is.null(expected) && is.null(residuals)) ||
!is.numeric(expected)) {
if (inherits(expected, "formula")) {
fm <- loglm(expected, x, fitted = TRUE)
expected <- fitted(fm)
df <- fm$df
} else {
if (is.null(expected))
expected <- as.list(1:ndim)
fm <- loglin(x, expected, fit = TRUE, print = FALSE)
expected <- fm$fit
df <- fm$df
}
}
## compute residuals
if (is.null(residuals))
residuals <- switch(residuals_type,
pearson = (x - expected) / sqrt(ifelse(expected > 0, expected, 1)),
deviance = {
tmp <- 2 * (x * log(ifelse(x == 0, 1, x / ifelse(expected > 0, expected, 1))) - (x - expected))
tmp <- sqrt(pmax(tmp, 0))
ifelse(x > expected, tmp, -tmp)
},
ft = sqrt(x) + sqrt(x + 1) - sqrt(4 * expected + 1)
)
## replace NAs by 0
if (any(nas <- is.na(residuals))) residuals[nas] <- 0
# switch observed and expected if required
observed <- if (type == "observed") x else expected
expected <- if (type == "observed") expected else x
# replicate arguments to number of dimensions
spacing <- rep(spacing, length=ndim)
split_dir <- rep(split_dir, length=ndim)
if(missing(label_edge))
label_edge <- rep( c('-', '+'), each=3, length=ndim)
zeros <- observed <= .Machine$double.eps
shapelist <- shape
# sanity check
if (!inherits(shapelist, "shape3d")) stop("shape must be a shape3d object")
if (newpage) rgl::open3d()
for (k in 1:ndim) {
marg <- margin.table(observed, k:1)
if (k==1) {
shapelist <- split3d(shapelist, marg, split_dir[k], space=spacing[k])
label3d(shapelist, split_dir[k], dn[[k]], vnames[k], edge=label_edge[k], ...)
}
else {
marg <- matrix(marg, nrow=levels[k])
shapelist <- split3d(shapelist, marg, split_dir[k], space=spacing[k])
names(shapelist) <- apply(as.matrix(expand.grid(dn[1:k])), 1, paste, collapse=":")
L <- length(shapelist)
label_cells <- if (label_edge[k]=='-') 1:levels[k]
else (L-levels[k]+1):L
label3d(shapelist[label_cells], split_dir[k], dn[[k]], vnames[k], edge=label_edge[k], ...)
}
}
# assign colors
# TODO: allow alpha to control transparency of side walls
col <- shading(residuals, interpolate=interpolate)
# display, but exclude the zero cells
rgl::shapelist3d(shapelist[!as.vector(zeros)], col=col[!as.vector(zeros)], ...)
# plot markers for zero cells
if (any(zeros)) {
ctrs <- t(sapply(shapelist, center3d))
rgl::spheres3d(ctrs[as.vector(zeros),], radius=zero_size)
}
# invisible(structable(observed))
invisible(shapelist)
}
# basic shading_Friendly, adapting the simple code used in mosaicplot()
shading_basic <- function(residuals, interpolate=TRUE) {
if (is.logical(interpolate))
interpolate <- c(2, 4)
else if (any(interpolate <= 0) || length(interpolate) > 5)
stop("invalid 'interpolate' specification")
shade <- sort(interpolate)
breaks <- c(-Inf, -rev(shade), 0, shade, Inf)
colors <- c(hsv(0, s = seq.int(1, to = 0, length.out = length(shade) +
1)), hsv(4/6, s = seq.int(0, to = 1, length.out = length(shade) +
1)))
colors[as.numeric(cut(residuals, breaks))]
}
# provide labels for 3D objects below/above their extent along a given dimension
# FIXME: kludge for interline gap between level labels and variable name
# TODO: how to pass & extract labeling_args, e.g., labeling_args=list(at='min', fontsize=10)
label3d <- function(objlist, dim, text, varname, offset=.05, adj, edge="-", gap=.1, labeling_args, ...) {
if(missing(adj)) {
if (dim < 3) adj <- ifelse(edge == '-', c(0.5, 1), c(0.5, 0))
else adj <- ifelse(edge == '-', c(1, 0.5), c(0, 0.5))
}
ranges <- lapply(objlist, range3d)
loc <- t(sapply(ranges, colMeans)) # positions of labels on dimension dim
min <- t(sapply(ranges, function(x) x[1,])) # other dimensions at min values
max <- t(sapply(ranges, function(x) x[2,])) # other dimensions at max values
xyz <- if (edge == '-') (min - offset) else (max + offset)
xyz[,dim] <- loc[,dim]
if(!missing(varname)) {
loclab <- colMeans(loc) # NB: doesn't take space into acct
xyzlab <- if (edge == '-')
min[1,] - offset - gap
else max[1,] + offset + gap
xyzlab[dim] <- loclab[dim]
xyz <- rbind(xyz, xyzlab)
text <- c(text, varname)
}
result <- c(labels = rgl::texts3d(xyz, texts=text, adj=adj, ...))
invisible(result)
}
vcdExtra/R/vcdExtra-deprecated.R 0000644 0001762 0000144 00000000147 13164013351 016233 0 ustar ligges users #summarise <- function (...) {
# .Deprecated("summarise", package="vcdExtra")
# LRstats(...)
#}
#
vcdExtra/R/seq_loglm.R 0000644 0001762 0000144 00000005713 13164013351 014343 0 ustar ligges users #' Sequential loglinear models for an n-way table
#' This function takes an n-way contingency table and fits a series of sequential
#' models to the 1-, 2-, ... n-way marginal tables, corresponding to a variety of
#' types of loglinear models.
#' @param x a contingency table in array form, with optional category labels specified in the dimnames(x) attribute,
#' or else a data.frame in frequency form, with the frequency variable names "Freq".
#' @param type type of sequential model to fit
#' @param marginals which marginals to fit?
#' @param vorder order of variables
#' @param k indices of conditioning variable(s) for "joint", "conditional" or order for "markov"
#' @param prefix
#' @param fitted keep fitted values?
seq_loglm <- function(
x,
type = c("joint", "conditional", "mutual", "markov", "saturated"),
marginals = 1:nf, # which marginals to fit?
vorder = 1:nf, # order of variables in the sequential models
k = NULL, # conditioning variable(s) for "joint", "conditional" or order for "markov"
prefix = 'model',
fitted = TRUE, # keep fitted values?
...
)
{
if (inherits(x, "data.frame") && "Freq" %in% colnames(x)) {
x <- xtabs(Freq ~ ., data=x)
}
if (!inherits(x, c("table", "array"))) stop("not an xtabs, table, array or data.frame with a 'Freq' variable")
nf <- length(dim(x))
x <- aperm(x, vorder)
factors <- names(dimnames(x))
indices <- 1:nf
type = match.arg(type)
# models <- as.list(rep(NULL, length(marginals)))
models <- list()
for (i in marginals) {
mtab <- margin.table(x, 1:i)
if (i==1) {
# KLUDGE: use loglin, but try to make it look like a loglm object
mod <- loglin(mtab, margin=NULL, print=FALSE)
mod$model.string = paste("=", factors[1])
mod$margin <- list(factors[1])
# mod$margin <- names(dimnames(mtab))
# names(mod$margin) <- factors[1]
if (fitted) {
fit <- mtab
fit[] <- (sum(mtab) / length(mtab))
mod$fitted <- fit
}
mod$nobs <- length(mtab)
mod$frequencies <- mtab
mod$deviance <- mod$lrt
class(mod) <- c("loglin", "loglm")
}
else {
expected <- switch(type,
'conditional' = conditional(i, mtab, with=if(is.null(k)) i else k),
'joint' = joint(i, mtab, with=if(is.null(k)) i else k),
'mutual' = mutual(i, mtab),
'markov' = markov(i, mtab, order=if(is.null(k)) 1 else k),
'saturated' = saturated(i, mtab)
)
form <- loglin2formula(expected)
# mod <- loglm(formula=form, data=mtab, fitted=TRUE)
mod <- eval(bquote(MASS::loglm(.(form), data=mtab, fitted=fitted)))
mod$model.string <- loglin2string(expected, brackets=if (i= S-PLUS 6.0)
if(is.null(version$language) && inherits(x, "crosstabs")) { oldClass(x)<-NULL; attr(x, "marginals")<-NULL}
## TODO: add tests for matrix or table
n <- nrow(x)
m <- ncol(x)
pi.c<-pi.d<-matrix(0, nrow=n, ncol=m)
row.x<-row(x)
col.x<-col(x)
for(i in 1:(n)){
for(j in 1:(m)){
pi.c[i, j]<-sum(x[row.xi & col.x>j])
pi.d[i, j]<-sum(x[row.xj]) + sum(x[row.x>i & col.x 1,
maxTitle=NULL)
{
# make sure requested packages are available and loaded
for (i in seq_along(package)) {
if (!isNamespaceLoaded(package[i]))
if (requireNamespace(package[i], quietly=TRUE))
cat(paste("Loading package:", package[i], "\n"))
else stop(paste("Package", package[i], "is not available"))
}
dsitems <- data(package=package)$results
wanted <- c('Package', 'Item','Title')
ds <- as.data.frame(dsitems[,wanted], stringsAsFactors=FALSE)
getData <- function(x, pkg) {
# fix items with " (...)" in names, e.g., "BJsales.lead (BJsales)" in datasets
objname <- gsub(" .*", "", x)
e <- loadNamespace(pkg)
if (!exists(x, envir = e)) {
dataname <- sub("^.*\\(", "", x)
dataname <- sub("\\)$", "", dataname)
e <- new.env()
data(list = dataname, package = pkg, envir = e)
}
get(objname, envir = e)
}
getDim <- function(i) {
data <- getData(ds$Item[i], ds$Package[i])
if (is.null(dim(data))) length(data) else paste(dim(data), collapse='x')
}
getClass <- function(i) {
data <- getData(ds$Item[i], ds$Package[i])
cl <- class(data)
if (length(cl)>1 && !allClass) cl[length(cl)] else cl
}
ds$dim <- unlist(lapply(seq_len(nrow(ds)), getDim ))
ds$class <- unlist(lapply(seq_len(nrow(ds)), getClass ))
if (!is.null(maxTitle)) ds$Title <- substr(ds$Title, 1, maxTitle)
if (incPackage)
ds[c('Package', 'Item','class','dim','Title')]
else
ds[c('Item','class','dim','Title')]
}
vcdExtra/R/CMHtest.R 0000644 0001762 0000144 00000022775 14202234157 013702 0 ustar ligges users # Cochran-Mantel-Haenszel tests for ordinal factors in contingency tables
# The code below follows Stokes, Davis & Koch, (2000).
# "Categorical Data Analysis using the SAS System", 2nd Ed.,
# pp 74--75, 92--101, 124--129.
# Ref: Landis, R. J., Heyman, E. R., and Koch, G. G. (1978),
# Average Partial Association in Three-way Contingency Tables:
# A Review and Discussion of Alternative Tests,
# International Statistical Review, 46, 237-254.
# See: https://onlinecourses.science.psu.edu/stat504/book/export/html/90
# http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_freq_a0000000648.htm
# DONE: this should be the main function, handling 2-way & higher-way tables
# With strata, use apply() or recursion over strata
# DONE: With strata, calculate overall CMH tests controlling for strata
# FIXED: rmeans and cmeans tests were labeled incorrectly
CMHtest <- function(x, ...)
UseMethod("CMHtest")
CMHtest.formula <-
function(formula, data = NULL, subset = NULL, na.action = NULL, ...)
{
m <- match.call(expand.dots = FALSE)
edata <- eval(m$data, parent.frame())
fstr <- strsplit(paste(deparse(formula), collapse = ""), "~")
vars <- strsplit(strsplit(gsub(" ", "", fstr[[1]][2]), "\\|")[[1]], "\\+")
varnames <- vars[[1]]
condnames <- if (length(vars) > 1) vars[[2]] else NULL
dep <- gsub(" ", "", fstr[[1]][1])
if (!dep %in% c("","Freq")) {
if (all(varnames == ".")) {
varnames <- if (is.data.frame(data))
colnames(data)
else
names(dimnames(as.table(data)))
varnames <- varnames[-which(varnames %in% dep)]
}
varnames <- c(varnames, dep)
}
if (inherits(edata, "ftable") || inherits(edata, "table") || length(dim(edata)) > 2) {
condind <- NULL
dat <- as.table(data)
if(all(varnames != ".")) {
ind <- match(varnames, names(dimnames(dat)))
if (any(is.na(ind)))
stop(paste("Can't find", paste(varnames[is.na(ind)], collapse=" / "), "in", deparse(substitute(data))))
if (!is.null(condnames)) {
condind <- match(condnames, names(dimnames(dat)))
if (any(is.na(condind)))
stop(paste("Can't find", paste(condnames[is.na(condind)], collapse=" / "), "in", deparse(substitute(data))))
ind <- c(condind, ind)
}
dat <- margin.table(dat, ind)
}
CMHtest.default(dat,
strata = if (is.null(condind)) NULL else match(condnames, names(dimnames(dat))), ...)
} else {
m <- m[c(1, match(c("formula", "data", "subset", "na.action"), names(m), 0))]
m[[1]] <- as.name("xtabs")
m$formula <-
formula(paste(if("Freq" %in% colnames(data)) "Freq",
"~",
paste(c(varnames, condnames), collapse = "+")))
tab <- eval(m, parent.frame())
CMHtest.default(tab, ...)
}
}
CMHtest.default <- function(x, strata = NULL, rscores=1:R, cscores=1:C,
types=c("cor", "rmeans", "cmeans", "general"),
overall=FALSE, details=overall, ...)
{
snames <- function(x, strata) {
sn <- dimnames(x)[strata]
dn <- names(sn)
apply(expand.grid(sn), 1, function(x) paste(dn, x, sep=":", collapse = "|"))
}
## check dimensions
L <- length(d <- dim(x))
if(any(d < 2L)) stop("All table dimensions must be 2 or greater")
if(L > 2L & is.null(strata)) strata <- 3L:L
if(is.character(strata)) strata <- which(names(dimnames(x)) == strata)
if(L - length(strata) != 2L) stop("All but 2 dimensions must be specified as strata.")
## rearrange table to put primary dimensions first
x <- aperm(x, c(setdiff(1:L, strata), strata))
d <- dim(x)
R <- d[1]
C <- d[2]
# handle strata
if (!is.null(strata)) {
sn <- snames(x, strata)
res <- c(apply(x, strata, CMHtest2, rscores=rscores, cscores=cscores,
types=types,details=details, ...))
# DONE: fix names if there are 2+ strata
names(res) <- sn
for (i in seq_along(res)) res[[i]]$stratum <- sn[i]
# DONE: Calculate generalized CMH, controlling for strata
if (overall) {
if (!details) warning("Overall CMH tests not calculated because details=FALSE")
else {
resall <- CMHtest3(res, types=types)
res$ALL <- resall
}
}
return(res)
}
else CMHtest2(x, rscores=rscores, cscores=cscores,
types=types,details=details, ...)
}
# handle two-way case, for a given stratum
# DONE: now allow rscores/cscores == 'midrank' for midrank scores
# DONE: allow rscores/cscores=NULL for unordered factors, where ordinal
# scores don't make sense
# DONE: modified to return all A matrices as a list
# DONE: cmh() moved outside
CMHtest2 <- function(x, stratum=NULL, rscores=1:R, cscores=1:C,
types=c("cor", "rmeans", "cmeans", "general"),
details=FALSE, ...) {
# left kronecker product
lkronecker <- function(x, y, make.dimnames=TRUE, ...)
kronecker(y, x, make.dimnames=make.dimnames, ...)
# midrank scores (modified ridits) based on row/column totals
midrank <- function (n) {
cs <- cumsum(n)
(2*cs - n +1) / (2*(cs[length(cs)]+1))
}
L <- length(d <- dim(x))
R <- d[1]
C <- d[2]
if (is.character(rscores) && rscores=="midrank") rscores <- midrank(rowSums(x))
if (is.character(cscores) && cscores=="midrank") cscores <- midrank(colSums(x))
nt <- sum(x)
pr <- rowSums(x) / nt
pc <- colSums(x) / nt
m <- as.vector(nt * outer(pr,pc)) # expected values under independence
n <- as.vector(x) # cell frequencies
V1 <- (diag(pr) - pr %*% t(pr))
V2 <- (diag(pc) - pc %*% t(pc))
V <- (nt^2/(nt-1)) * lkronecker(V1, V2, make.dimnames=TRUE)
if (length(types)==1 && types=="ALL") types <- c("general", "rmeans", "cmeans", "cor" )
types <- match.arg(types, several.ok=TRUE)
# handle is.null(rscores) etc here
if (is.null(rscores)) types <- setdiff(types, c("cmeans", "cor"))
if (is.null(cscores)) types <- setdiff(types, c("rmeans", "cor"))
table <- NULL
Amats <- list()
for (type in types) {
if("cor" == type) {
A <- lkronecker( t(rscores), t(cscores) )
df <- 1
table <- rbind(table, cmh(n, m, A, V, df))
Amats$cor <- A
} else if("rmeans" == type) {
A <- lkronecker( cbind(diag(R-1), rep(0, R-1)), t(cscores))
df <- R-1
table <- rbind(table, cmh(n, m, A, V, df))
Amats$rmeans <- A
} else if("cmeans" == type) {
A <- lkronecker( t(rscores), cbind(diag(C-1), rep(0, C-1)))
df <- C-1
table <- rbind(table, cmh(n, m, A, V, df))
Amats$cmeans <- A
} else if ("general" == type) {
A <- lkronecker( cbind(diag(R-1), rep(0, R-1)), cbind(diag(C-1), rep(0, C-1)))
df <- (R-1)*(C-1)
table <- rbind(table, cmh(n, m, A, V, df))
Amats$general <- A
}
}
colnames(table) <- c("Chisq", "Df", "Prob")
rownames(table) <- types
xnames <- names(dimnames(x))
result <- list(table=table, names=xnames, rscores=rscores, cscores=cscores, stratum=stratum )
if (details) result <- c(result, list(A=Amats, V=V, n=n, m=m))
class(result) <- "CMHtest"
result
}
# do overall test, from a computed CMHtest list
CMHtest3 <- function(object,
types=c("cor", "rmeans", "cmeans", "general"))
{
nstrat <- length(object) # number of strata
# extract components, each a list of nstrat terms
n.list <- lapply(object, function(s) s$n)
m.list <- lapply(object, function(s) s$m)
V.list <- lapply(object, function(s) s$V)
A.list <- lapply(object, function(s) s$A)
nt <- sapply(lapply(object, function(s) s$n), sum)
Df <- object[[1]]$table[,"Df"]
if (length(types)==1 && types=="ALL") types <- c("general", "rmeans", "cmeans", "cor" )
types <- match.arg(types, several.ok=TRUE)
table <- list()
for (type in types) {
AVA <- 0
Anm <- 0
for (k in 1:nstrat) {
A <- A.list[[k]][[type]]
V <- V.list[[k]]
n <- n.list[[k]]
m <- m.list[[k]]
AVA <- AVA + A %*% V %*% t(A)
Anm <- Anm + A %*% (n-m)
}
Q <- t(Anm) %*% solve(AVA) %*% Anm
df <- Df[type]
pvalue <- pchisq(Q, df, lower.tail=FALSE)
table <- rbind(table, c(Q, df, pvalue))
}
rownames(table) <- types
colnames(table) <- c("Chisq", "Df", "Prob")
xnames <- object[[1]]$names
result=list(table=table, names=xnames, stratum="ALL")
class(result) <- "CMHtest"
result
}
# basic CMH calculation
cmh <- function(n, m,A, V, df) {
AVA <- A %*% V %*% t(A)
Q <- t(n-m) %*% t(A) %*% solve(AVA) %*% A %*% (n-m)
pvalue <- pchisq(Q, df, lower.tail=FALSE)
c(Q, df, pvalue)
}
# DONE: incorporate stratum name in the heading
# TODO: handle the printing of pvalues better
print.CMHtest <- function(x, digits = max(getOption("digits") - 2, 3), ...) {
heading <- "Cochran-Mantel-Haenszel Statistics"
if (!is.null(x$names)) heading <- paste(heading, "for", paste(x$names, collapse=" by "))
if (!is.null(x$stratum)) heading <- paste(heading,
ifelse(x$stratum=="ALL", "\n\tOverall tests, controlling for all strata", paste("\n\tin stratum", x$stratum)))
# TODO: determine score types (integer, midrank) for heading
df <- x$table
types <- rownames(df)
labels <- list(cor="Nonzero correlation", rmeans="Row mean scores differ",
cmeans="Col mean scores differ", general="General association")
labels <- unlist(labels[types]) # select the labels for the types
df <- data.frame("AltHypothesis"=as.character(labels), df, stringsAsFactors=FALSE)
cat(heading,"\n\n")
print(df, digits=digits, ...)
cat("\n")
invisible(x)
}
vcdExtra/MD5 0000644 0001762 0000144 00000020766 14202500463 012351 0 ustar ligges users b68992f7a0767dfff558e0f8d03224b7 *DESCRIPTION
54bd83d63a6c74ec0543e1704b88a5fc *NAMESPACE
88e2d0edd3301f76b71ce29bf7ba29c4 *NEWS
0b9242b2515cf6dad28203c5835b02ce *R/CMHtest.R
7bf2895634e1b0faca9385bd81ccee37 *R/Crossings.R
1963eb3fb347cf99287b4794b4fd57dc *R/GKgamma.R
a7a0bf2f6825917c8e31f99c7b25d265 *R/HLtest.R
10217e3a0582ec4cddba2bfc130a8fad *R/Kway.R
c8079ae2c1058550fa5b5fda9624dc8b *R/LRstats.R
8e444f0aaaea055b108a478f8ddf2def *R/Summarise.R
650530a623dec352a740a6626baddac7 *R/blogits.R
d3838c45cc40201160f85f0eb732d3c5 *R/collapse.table.R
b0483049482397dff2bb9ca2ef123725 *R/cutfac.R
f35d0b821039af9613f556357d4f2c82 *R/datasets.R
ba689f17617d1f6f199d908bafa97fb3 *R/expand.dft.R
c1cf46896f5087dccb67fe9e27c7d200 *R/glmlist.R
ea1051fba28687e9b147a8912baf9802 *R/logLik.loglm.R
564524d08cf38c5eea4926a1c6e05fb3 *R/loglin-utilities.R
67c413ec19c991bf083e0459eb3ff71f *R/logseries.R
e66f1348beffe4281c1eb9b80036e586 *R/mcaplot.R
1384d1ce36ba17ce25b00603306a258c *R/modFit.R
014cf5c8db1d36327db64bc35c5a7ed3 *R/mosaic.glm.R
5ce7705b0b6911d86ed0ee6547fc7218 *R/mosaic.glmlist.R
de9b6b1b1b831283a118c2e5e9e72932 *R/mosaic3d.R
1d5324d4c8b0e2cb572aa9e61ddb7148 *R/print.Kappa.R
1925c7203e978e7b778c25a5b2afa0c1 *R/seq_loglm.R
1987e35844f89995f5f071e3c219e848 *R/seq_mosaic.R
ebdbee3b568920673218c4610b858562 *R/split3d.R
559c21fac93caf6fe54e3e48a6265c17 *R/summarise-old.R
e782855ba6eaecbc19b51e1c4b8d69ba *R/update.xtabs.R
c2c481edc3058a26cd2eb178510decaa *R/vcdExtra-deprecated.R
5fe4e1529aa78269afa34eac8595e84c *R/zero.test.R
f29ad45c0e9c9e00774148aab1bf1ed2 *README.md
7b20157c9f3505de8d3fb466bf003ff0 *build/partial.rdb
b220ed4909dac12a49fa8e6ff1e31723 *build/vignette.rds
9e9f5a59531115d08e1a4e3904d52568 *data/Abortion.rda
d28631e2c49f525a100e152a9eb9a510 *data/Accident.RData
ade55a126530afe71b7a1f0c6f2559d9 *data/AirCrash.RData
da162a7822bae829cfe70fefc4fb3940 *data/Alligator.RData
f78b211dc59fd05ee408f9e36a7ab465 *data/Bartlett.rda
095237eb09ee6c0390963dba18bbe369 *data/Burt.RData
be694e73113d787376f490ab7b9c2111 *data/Caesar.rda
5f2710dec70694a826d5441deb8e2834 *data/Cancer.rda
fcd475580d46bb85c9e153bc3fba286b *data/Cormorants.RData
b83df313ba8d797dc8c88f89fc11633f *data/CyclingDeaths.RData
4db9c986e680e4124e1050179db30145 *data/DaytonSurvey.RData
05d9373ce3ae3f4c418b3e14c10c028c *data/Depends.RData
64b2877c9c9632324d08bc4d03f72c17 *data/Detergent.rda
f34cd4cb33c50308542125065ff15456 *data/Donner.RData
f9d26be5c016b54ecd109e4489a0c6ac *data/Draft1970.RData
390b34d55b25f7d2fcb1dff1bebf440b *data/Draft1970table.RData
b73fd0b7e4a37762e9a78d5a2b5ee7f7 *data/Dyke.rda
8e1819cbdaf4df49ec14ff0400845333 *data/Fungicide.rda
63ed6844022ca2aaa4d5cd0b8594ad78 *data/GSS.rda
d6b04ac4b77a3eaaeee872b7919868c9 *data/Geissler.RData
29a5b76d19e693832c6388550e42f844 *data/Gilby.rda
c243c6a0b3bc5f6b18db3af0a18e776f *data/Glass.RData
96f75a9a79605ae17c5d68800569e1a7 *data/HairEyePlace.RData
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\author{Michael Friendly\\York University, Toronto}
\title{Working with categorical data with \proglang{R} and the \pkg{vcd} and \pkg{vcdExtra} packages}
\date{\footnotesize{Using \Rpackage{vcdExtra} version \Sexpr{packageDescription("vcdExtra")[["Version"]]}
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<>=
set.seed(1071)
#library(vcd)
library(vcdExtra)
library(ggplot2)
#data(Titanic)
data(HairEyeColor)
data(PreSex)
data(Arthritis)
art <- xtabs(~Treatment + Improved, data = Arthritis)
if(!file.exists("fig")) dir.create("fig")
@
%% end of declarations %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{document}
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%% an abstract and keywords
\begin{abstract}
This tutorial describes the creation and manipulation of frequency and contingency tables
from categorical variables, along with tests of independence, measures
of association, and methods for graphically displaying results.
The framework is provided by the \proglang{R} package \pkg{vcd},
but other packages are used to help with various tasks.
The \pkg{vcdExtra} package extends the graphical and statistical methods provided by
\pkg{vcd}.
This package is now the main support package for the book
\emph{Discrete Data Analysis with R: Visualizing and Modeling Techniques for Categorical
and Count Data} \citep{FriendlyMeyer:2016:DDAR}. The web page for the book,
\href{http://ddar.datavis.ca}{ddar.datavis.ca}, gives further details.
\end{abstract}
%\keywords{contingency tables, mosaic plots, sieve plots,
%categorical data, independence, conditional independence, generalized linear models,
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%\Plainkeywords{contingency tables, mosaic plots,
% sieve plots, categorical data, independence,
% conditional independence, generalized linear models, R}
{\small
% \sloppy
% \begin{multicols}{2}
\tableofcontents
% \end{multicols}
}
\section[Introduction]{Introduction}\label{sec:intro}
%% Note: If there is markup in \(sub)section, then it has to be escape as above.
This tutorial, part of the \pkg{vcdExtra} package,
describes how to work with categorical data in the context of
fitting statistical models in \proglang{R} and visualizing
the results using the \pkg{vcd} and \pkg{vcdExtra} packages.
It focuses first on methods and tools for creating
and manipulating \proglang{R} data objects which represent
frequency and contingency tables involving
categorical variables.
Further sections describe some simple methods for calculating
tests of independence and measures of association among
categorical variables, and also
methods for graphically displaying results.
There is much more to the analysis of categorical data than is described here,
where the emphasis is on cross-tabulated tables of frequencies (``contingency tables''),
statistical tests, associated \loglin\ models, and visualization of \emph{how}
variables are related.
A more general treatment of graphical methods for categorical data is contained
in the book, \emph{Discrete Data Analysis with R: Visualizing and Modeling Techniques for Categorical
and Count Data} \citep{FriendlyMeyer:2016:DDAR}.
An earlier book using SAS is
\emph{Visualizing Categorical Data} \citep{vcd:Friendly:2000}, for which
\pkg{vcd} is a partial \proglang{R} companion, covering topics not otherwise
available in \proglang{R}.
On the other hand, the implementation of graphical
methods in \pkg{vcd} is more general in many respects than what I provided in
\proglang{SAS}. Statistical models for categorical data in \proglang{R} have
been extended considerably with the \pkg{gnm} package for generalized \emph{nonlinear}
models. The \pkg{vcdExtra} package extends \pkg{vcd} methods to models fit using
\codefun{glm} and \codefun{gnm}.
A more complete theoretical description of these statistical
methods is provided in Agresti's \citeyearpar{vcd:Agresti:2002,Agresti:2013}
\emph{Categorical Data Analysis}. For this, see
the \proglang{Splus/R} companion by Laura Thompson,
\url{http://www.stat.ufl.edu/~aa/cda/Thompson_manual.pdf}
and Agresti's support web page,
\url{http://www.stat.ufl.edu/~aa/cda/cda.html}.
\section[Creating frequency tables]{Creating and manipulating frequency tables}\label{sec:creating}
\proglang{R} provides many methods for creating frequency and contingency tables. Several are
described below. In the examples below, we use some real examples and some anonymous
ones, where the variables \code{A}, \code{B}, and \code{C} represent
categorical variables, and \code{X} represents an arbitrary \proglang{R} data object.
The first thing you need to know is that categorical data can be represented in
three different forms in \proglang{R}, and it is sometimes necessary to convert
from one form to another, for carrying out statistical tests, fitting models
or visualizing the results. Once a data object exists in \proglang{R},
you can examine its complete structure with
the \codefun{str} function, or view the names of its components with the
\codefun{names} function.
\begin{description}
\item[case form] a data frame containing individual observations, with one or
more factors, used as the classifying variables. In case form, there may also
be numeric covariates.
The total number of observations
is \code{nrow(X)}, and the number of variables is \code{ncol(X)}.
\Example
The \data{Arthritis} data is available in case form in the \pkg{vcd} package.
There are two explanatory factors: \code{Treatment} and \code{Sex}. \code{Age}
is a numeric covariate, and \code{Improved} is the response--- an ordered factor,
with levels
\code{\Sexpr{paste(levels(Arthritis$Improved),collapse=' < ')}}.
Excluding \code{Age}, we would have
a $2 \times 2 \times 3$ contingency table for \code{Treatment}, \code{Sex} and \code{Improved}.
%\code{"None" < "Some" < "Marked"}.
<>=
names(Arthritis) # show the variables
str(Arthritis) # show the structure
head(Arthritis,5) # first 5 observations, same as Arthritis[1:5,]
@
\item[frequency form] a data frame containing one or more factors, and a frequency
variable, often called \code{Freq} or \code{count}. The total number of observations
is
\verb|sum(X$Freq)|,
\code{sum(X[,"Freq"])} or some equivalent form.
The number of cells in the table is \code{nrow(X)}.
\Example
For small frequency tables, it is often convenient to enter them in frequency form
using \codefun{expand.grid} for the factors and \codefun{c} to list the counts in a vector.
The example below, from \cite{vcd:Agresti:2002} gives results for the 1991 General Social Survey,
with respondents classified by sex and party identification.
<>=
# Agresti (2002), table 3.11, p. 106
GSS <- data.frame(
expand.grid(sex=c("female", "male"),
party=c("dem", "indep", "rep")),
count=c(279,165,73,47,225,191))
GSS
names(GSS)
str(GSS)
sum(GSS$count)
@
\item[table form] a matrix, array or table object, whose elements are the frequencies
in an $n$-way table. The variable names (factors) and their levels are given by
\code{dimnames(X)}. The total number of observations
is \code{sum(X)}. The number of dimensions of the table is \code{length(dimnames(X))},
and the table sizes are given by \code{sapply(dimnames(X), length)}.
\Example
The \data{HairEyeColor} is stored in table form in \pkg{vcd}.
<>=
str(HairEyeColor) # show the structure
sum(HairEyeColor) # number of cases
sapply(dimnames(HairEyeColor), length) # table dimension sizes
@
\Example
Enter frequencies in a matrix, and assign \code{dimnames},
giving the variable names and category labels. Note that, by default,
\codefun{matrix} uses the elements supplied by \emph{columns} in the
result, unless you specify \code{byrow=TRUE}.
<>=
## A 4 x 4 table Agresti (2002, Table 2.8, p. 57) Job Satisfaction
JobSat <- matrix(c(1,2,1,0, 3,3,6,1, 10,10,14,9, 6,7,12,11), 4, 4)
dimnames(JobSat) = list(income=c("< 15k", "15-25k", "25-40k", "> 40k"),
satisfaction=c("VeryD", "LittleD", "ModerateS", "VeryS"))
JobSat
@
\data{JobSat} is a matrix, not an object of \code{class("table")}, and some functions
are happier with tables than matrices.
You can coerce it to a table with \codefun{as.table},
<>=
JobSat <- as.table(JobSat)
str(JobSat)
@
\end{description}
\subsection[Ordered factors]{Ordered factors and reordered tables}\label{sec:ordered-factors}
In table form, the values of the table factors are ordered by their position in the table.
Thus in the \data{JobSat} data, both \code{income} and \code{satisfaction} represent ordered
factors, and the \emph{positions} of the values in the rows and columns reflects their
ordered nature.
Yet, for analysis, there are time when you need \emph{numeric} values for the levels
of ordered factors in a table, e.g., to treat a factor as a quantitative variable.
In such cases, you can simply re-assign the \code{dimnames} attribute of the table
variables. For example, here, we assign numeric values to \code{income} as the middle of their
ranges, and treat \code{satisfaction} as equally spaced with integer scores.
<>=
dimnames(JobSat)$income<-c(7.5,20,32.5,60)
dimnames(JobSat)$satisfaction<-1:4
@
For the \data{HairEyeColor} data, hair color and eye color are ordered arbitrarily.
For visualizing the data using mosaic plots and other methods described below, it
turns out to be more useful to assure that both hair color and eye color are
ordered from dark to light.
Hair colors are actually ordered this way already, and it is easiest to re-order
eye colors by indexing. Again \codefun{str} is your friend.
<>=
HairEyeColor <- HairEyeColor[, c(1,3,4,2), ]
str(HairEyeColor)
@
This is also the order for both hair color and eye color shown in
the result of a correspondence analysis (\figref{fig:ca-haireye}) below.
With data in case form or frequency form, when you have ordered factors
represented with character values, you must ensure that they are treated
as ordered in \proglang{R}.%
\footnote{In \proglang{SAS}, many procedures offer the option
\code{order = data | internal | formatted} to allow character values
to be ordered according to (a) their order in the data set, (b)
sorted internal value, or (c) sorted formatted representation
provided by a \proglang{SAS} format.
}
Imagine that the \data{Arthritis} data was read from a text file.
By default the \code{Improved} will be ordered alphabetically:
\code{Marked},
\code{None},
\code{Some}--- not what we want. In this case, the function
\codefun{ordered} (and others) can be useful.
<>=
Arthritis <- read.csv("arthritis.txt",header=TRUE)
Arthritis$Improved <- ordered(Arthritis$Improved, levels=c("None", "Some", "Marked"))
@
With this order of \code{Improved}, the response in this data,
a mosaic display of \code{Treatment} and \code{Improved} (\figref{fig:arthritis})shows a clearly
interpretable pattern.
<>=
mosaic(art, gp = shading_max, split_vertical = TRUE, main="Arthritis: [Treatment] [Improved]")
@
%\setkeys{Gin}{width=0.7\textwidth}
\begin{figure}[htb]
\begin{center}
%<>=
%mosaic(art, gp = shading_max, split_vertical = TRUE, main="Arthritis: [Treatment] [Improved]")
%@
\includegraphics[width=0.7\textwidth]{fig/vcd-tut-Arthritis}
\caption{Mosaic plot for the \data{Arthritis} data, showing the marginal model of independence
for Treatment and Improved. Age, a covariate, and Sex are ignored here.}
\label{fig:arthritis}
\end{center}
\end{figure}
Finally, there are situations where, particularly for display purposes, you
want to re-order the \emph{dimensions} of an $n$-way table, or change the
labels for the variables or levels.
This is easy when the data are in table form: \codefun{aperm} permutes
the dimensions, and assigning to \code{names} and \code{dimnames}
changes variable names and level labels respectively.
We will use the following version of \data{UCBAdmissions} in
\secref{sec:mantel} below.%
\footnote{
Changing \code{Admit} to \code{Admit?} might be useful for display purposes, but is
dangerous--- because it is then
difficult to use that variable name in a model formula.
See \secref{sec:tips} for options \code{labeling\_args} and \code{set\_labels}
to change variable and level names
for displays in the \code{strucplot} framework.
}
<>=
UCB <- aperm(UCBAdmissions, c(2, 1, 3))
dimnames(UCB)[[2]] <- c("Yes", "No")
names(dimnames(UCB)) <- c("Sex", "Admit?", "Department")
ftable(UCB)
@
%There is one subtle ``gotcha'' here: \codefun{aperm} returns an object of class \class{"array"},
%whereas \data{UCBAdmissions} is of class \class{"table"}, so methods defined for \code{table}
%objects will not work on the permuted array.
%The solution is to reassign the \code{class} of the result of \codefun{aperm}.
%
%<>=
%class(UCBAdmissions)
%class(UCB)
%str(as.data.frame(UCBAdmissions)) # OK
%str(as.data.frame(UCB)) # wrong
%
%class(UCB) <- "table"
%str(as.data.frame(UCB)) # now OK
%@
%
\subsection[structable()]{\codefun{structable}}\label{sec:structable}
For 3-way and larger tables
the \codefun{structable} function in \pkg{vcd} provides a convenient and flexible tabular display.
The variables assigned to the rows and columns of a two-way display can be specified
by a model formula.
<>=
structable(HairEyeColor) # show the table: default
structable(Hair+Sex ~ Eye, HairEyeColor) # specify col ~ row variables
@
It also returns an object of class \code{"structable"} which may be plotted with
\codefun{mosaic} (not shown here).
<>=
HSE < - structable(Hair+Sex ~ Eye, HairEyeColor) # save structable object
mosaic(HSE) # plot it
@
\subsection[table() and friends]{\codefun{table} and friends}\label{sec:table}
You can generate frequency tables from factor variables using the \codefun{table} function, tables of
proportions using the \codefun{prop.table} function, and marginal frequencies using
\codefun{margin.table}.
<>=
n=500
A <- factor(sample(c("a1","a2"), n, rep=TRUE))
B <- factor(sample(c("b1","b2"), n, rep=TRUE))
C <- factor(sample(c("c1","c2"), n, rep=TRUE))
mydata <- data.frame(A,B,C)
@
<>=
# 2-Way Frequency Table
attach(mydata)
mytable <- table(A,B) # A will be rows, B will be columns
mytable # print table
margin.table(mytable, 1) # A frequencies (summed over B)
margin.table(mytable, 2) # B frequencies (summed over A)
prop.table(mytable) # cell percentages
prop.table(mytable, 1) # row percentages
prop.table(mytable, 2) # column percentages
@
\codefun{table} can also generate multidimensional tables based on 3 or more
categorical variables. In this case, use the \codefun{ftable} or \codefun{structable}
function to print the
results more attractively.
<>=
# 3-Way Frequency Table
mytable <- table(A, B, C)
ftable(mytable)
@
\codefun{table} ignores missing values by default.
To include \code{NA} as a category in counts, include the
table option \code{exclude=NULL} if the variable is a vector. If the variable is a
factor you have to create a new factor using \code{newfactor <- factor(oldfactor,
exclude=NULL)}.
\subsection[xtabs()]{\codefun{xtabs}}\label{sec:xtabs}
The \codefun{xtabs} function allows you to create cross-tabulations of data using formula style input.
This typically works with case-form data supplied in a data frame or a matrix.
The result is a contingency table in array format, whose dimensions are determined by
the terms on the right side of the formula.
<>=
# 3-Way Frequency Table
mytable <- xtabs(~A+B+C, data=mydata)
ftable(mytable) # print table
summary(mytable) # chi-square test of indepedence
@
If a variable is included on the left side of the formula, it is assumed to be a
vector of frequencies (useful if the data have already been tabulated in frequency form).
<>=
(GSStab <- xtabs(count ~ sex + party, data=GSS))
summary(GSStab)
@
\subsection[Collapsing over factors]{Collapsing over table factors: \codefun{aggregate}, \codefun{margin.table} and \codefun{apply}}
It sometimes happens that we have a data set with more variables or factors than
we want to analyse, or else, having done some initial analyses, we decide that
certain factors are not important, and so should be excluded from graphic displays
by collapsing (summing) over them. For example, mosaic plots and fourfold displays
are often simpler to construct from versions of the data collapsed over
the factors which are not shown in the plots.
The appropriate tools to use again depend on
the form in which the data are represented--- a case-form data frame, a
frequency-form data frame (\codefun{aggregate}), or a table-form array or
table object (\codefun{margin.table} or \codefun{apply}).
When the data are in frequency form, and we want to produce another
frequency data frame, \codefun{aggregate} is a handy tool, using
the argument \code{FUN=sum} to sum the frequency variable over the
factors \emph{not} mentioned in the formula.
\Example
The data frame \data{DaytonSurvey} in the \pkg{vcdExtra} package represents a
$2^5$ table giving the frequencies of reported use (``ever used?'') of
alcohol, cigarettes and marijuana in a sample of high school seniors,
also classified by sex and race.
<>=
str(DaytonSurvey)
head(DaytonSurvey)
@
To focus on the associations among the
substances, we want to collapse over sex and race. The right-hand side of the formula
used in the call to \codefun{aggregate} gives the factors to be retained in the
new frequency data frame, \code{Dayton.ACM.df}.
<>=
# data in frequency form
# collapse over sex and race
Dayton.ACM.df <- aggregate(Freq ~ cigarette+alcohol+marijuana,
data=DaytonSurvey, FUN=sum)
Dayton.ACM.df
@
When the data are in table form, and we want to produce another
table, \codefun{apply} with \code{FUN=sum} can be used in a similar way
to sum the table over dimensions not mentioned in the \code{MARGIN}
argument. \codefun{margin.table} is just a wrapper for \codefun{apply}
using the \codefun{sum} function.
\Example
To illustrate, we first convert the \data{DaytonSurvey} to a 5-way
table using \codefun{xtabs}, giving \code{Dayton.tab}.
<>==
# in table form
Dayton.tab <- xtabs(Freq~cigarette+alcohol+marijuana+sex+race, data=DaytonSurvey)
structable(cigarette+alcohol+marijuana ~ sex+race, data=Dayton.tab)
@
Then, use \codefun{apply} on \code{Dayton.tab} to give the
3-way table \code{Dayton.ACM.tab} summed over sex and race.
The elements in this new table are the column sums for
\code{Dayton.tab} shown by \codefun{structable} just above.
<>==
# collapse over sex and race
Dayton.ACM.tab <- apply(Dayton.tab, MARGIN=1:3, FUN=sum)
Dayton.ACM.tab <- margin.table(Dayton.tab, 1:3) # same result
structable(cigarette+alcohol ~ marijuana, data=Dayton.ACM.tab)
@
Many of these operations can be performed using the \verb|**ply()| functions
in the \pkg{plyr} package.
For example, with the data in a frequency form data frame, use \codefun{ddply}
to collapse over unmentioned factors, and \codefun{plyr::summarise}%
\footnote{
Ugh. This \pkg{plyr} function clashes with a function of the same name in \pkg{vcdExtra}.
In this document I will use the explicit double-colon notation to keep them
separate.
}
as the function to be applied to each piece.
<>==
Dayton.ACM.df <- ddply(DaytonSurvey, .(cigarette, alcohol, marijuana),
plyr::summarise, Freq=sum(Freq))
@
\subsection[Collapsing levels]{Collapsing table levels: \codefun{collapse.table}}
A related problem arises when we have a table or array and for some purpose
we want to reduce the number of levels of some factors by summing subsets
of the frequencies. For example, we may have initially coded Age in 10-year
intervals, and decide that, either for analysis or display purposes, we
want to reduce Age to 20-year intervals. The \codefun{collapse.table} function
in \pkg{vcdExtra} was designed for this purpose.
\Example
Create a 3-way table, and collapse Age from 10-year to 20-year intervals.
First, we generate a $2 \times 6 \times 3$ table of random counts from a
Poisson distribution with mean of 100.
<>=
# create some sample data in frequency form
sex <- c("Male", "Female")
age <- c("10-19", "20-29", "30-39", "40-49", "50-59", "60-69")
education <- c("low", 'med', 'high')
data <- expand.grid(sex=sex, age=age, education=education)
counts <- rpois(36, 100) # random Possion cell frequencies
data <- cbind(data, counts)
# make it into a 3-way table
t1 <- xtabs(counts ~ sex + age + education, data=data)
structable(t1)
@
Now collapse \code{age} to 20-year intervals, and \code{education}
to 2 levels. In the arguments, levels of \code{age} and \code{education}
given the same label are summed in the resulting smaller table.
<>=
# collapse age to 3 levels, education to 2 levels
t2 <- collapse.table(t1,
age=c("10-29", "10-29", "30-49", "30-49", "50-69", "50-69"),
education=c(">=
as.data.frame(GSStab)
@
\Example Convert the \code{Arthritis} data in case form to a 3-way table of
\code{Treatment} $\times$ \code{Sex} $\times$ \code{Improved}.
Note the use of \codefun{with} to avoid having to use \code{Arthritis\$Treatment} etc. within the call to \codefun{table}.%
\footnote{
\codefun{table} does not allow a \code{data} argument to provide
an environment in which the table variables are to be found. In the
examples in \secref{sec:table} I used \code{attach(mydata)} for this purpose,
but \codefun{attach} leaves the variables in the global environment,
while \codefun{with} just evaluates the \codefun{table} expression in a
temporary environment of the data.
}
<>=
Art.tab <-with(Arthritis, table(Treatment, Sex, Improved))
str(Art.tab)
ftable(Art.tab)
@
There may also be times that you will need an equivalent case form \code{data.frame}
with factors representing the table variables
rather than the frequency table.
For example, the \codefun{mca} function in package \pkg{MASS}
only operates on data in this format.
Marc Schwartz provided code for \codefun{expand.dft} on the Rhelp
mailing list for converting a table back into a case form \code{data.frame}.
This function is included in \pkg{vcdExtra}.
\Example Convert the \data{Arthritis} data in table form (\code{Art.tab}) back to a \code{data.frame}
in case form, with factors
\code{Treatment}, \code{Sex} and \code{Improved}.
<>=
Art.df <- expand.dft(Art.tab)
str(Art.df)
@
\subsection{A complex example}\label{sec:complex}
If you've followed so far, you're ready for a more complicated example.
The data file, \code{tv.dat} represents a 4-way table of size
$5 \times 11 \times 5 \times 3$ where the table variables (unnamed in the file)
are read as \code{V1} -- \code{V4}, and the cell frequency is read
as \code{V5}. The file, stored in the \code{doc/extdata} directory
of \pkg{vcdExtra}, can be read as follows:
<>=
tv.data<-read.table(system.file("extdata","tv.dat", package="vcdExtra"))
head(tv.data,5)
@
For a local file, just use \codefun{read.table} in this form:
<>=
tv.data<-read.table("C:/R/data/tv.dat")
@
The data \code{tv.dat} came from the initial implementation of
mosaic displays in \proglang{R} by Jay Emerson.
In turn, they came from the initial development of mosaic displays
\citep{vcd:Hartigan+Kleiner:1984}
that illustrated the method with data on a large sample of TV viewers
whose behavior had been recorded for the Neilson ratings.
This data set contains sample television audience data from Neilsen
Media Research for the week starting November 6, 1995.
\begin{flushleft}
The table variables are:\\
~~~\code{V1}-- values 1:5 correspond to the days Monday--Friday;\\
~~~\code{V2}-- values 1:11 correspond to the quarter hour times 8:00PM through 10:30PM;\\
~~~\code{V3}-- values 1:5 correspond to ABC, CBS, NBC, Fox, and non-network choices;\\
~~~\code{V4}-- values 1:3 correspond to transition states: turn the television Off, Switch channels,
or Persist in viewing the current channel.
\end{flushleft}
We are interested just the cell frequencies, and rely on the facts that the
(a) the table is complete--- there are no missing cells,
so \code{nrow(tv.data)}=\Sexpr{nrow(tv.data)};
(b) the observations are ordered so that \code{V1} varies most rapidly and
\code{V4} most slowly. From this, we can just extract the frequency column
and reshape it into an array.
<>=
TV <- array(tv.data[,5], dim=c(5,11,5,3))
dimnames(TV) <- list(c("Monday","Tuesday","Wednesday","Thursday","Friday"),
c("8:00","8:15","8:30","8:45","9:00","9:15","9:30",
"9:45","10:00","10:15","10:30"),
c("ABC","CBS","NBC","Fox","Other"), c("Off","Switch","Persist"))
names(dimnames(TV))<-c("Day", "Time", "Network", "State")
@
More generally (even if there are missing cells), we can
use \codefun{xtabs} (or \codefun{plyr::daply})
to do the cross-tabulation, using \code{V5} as the
frequency variable. Here's how to do this same operation with \codefun{xtabs}:
<>=
TV <- xtabs(V5 ~ ., data=tv.data)
dimnames(TV) <- list(Day=c("Monday","Tuesday","Wednesday","Thursday","Friday"),
Time=c("8:00","8:15","8:30","8:45","9:00","9:15","9:30",
"9:45","10:00","10:15","10:30"),
Network=c("ABC","CBS","NBC","Fox","Other"),
State=c("Off","Switch","Persist"))
@
But this 4-way table is too large and awkward to work with. Among the networks,
Fox and Other occur infrequently.
We can also cut it down to a 3-way table by considering only viewers who persist
with the current station.%
\footnote{This relies on the fact that that indexing
an array drops dimensions of length 1 by default,
using the argument \code{drop=TRUE};
the result is coerced to the lowest possible dimension.
}
<>=
TV <- TV[,,1:3,] # keep only ABC, CBS, NBC
TV <- TV[,,,3] # keep only Persist -- now a 3 way table
structable(TV)
@
Finally, for some purposes, we might want to collapse the 11 times into a smaller number.
Here, we use \codefun{as.data.frame.table} to convert the table back to a data frame,
\codefun{levels} to re-assign the values of \code{Time},
and finally, \codefun{xtabs} to give a new, collapsed frequency table.
<>=
TV.df <- as.data.frame.table(TV)
levels(TV.df$Time) <- c(rep("8:00-8:59",4),rep("9:00-9:59",4), rep("10:00-10:44",3))
TV2 <- xtabs(Freq ~ Day + Time + Network, TV.df)
structable(Day ~ Time+Network,TV2)
@
Whew! See \figref{fig:TV-mosaic} for a mosaic plot of the \code{TV2} data.
\section{Tests of Independence}
\subsection{CrossTable}
OK, now we're ready to do some analyses. For tabular displays,
the \codefun{CrossTable} function in the \pkg{gmodels} package produces cross-tabulations
modeled after \code{PROC FREQ} in \proglang{SAS} or \code{CROSSTABS} in \proglang{SPSS}.
It has a wealth of options for the quantities that can be shown in each cell.
<>=
# 2-Way Cross Tabulation
library(gmodels)
CrossTable(GSStab,prop.t=FALSE,prop.r=FALSE,prop.c=FALSE)
@
There are options to report percentages (row, column, cell), specify decimal
places, produce Chi-square, Fisher, and McNemar tests of independence, report
expected and residual values (pearson, standardized, adjusted standardized),
include missing values as valid, annotate with row and column titles, and format
as \proglang{SAS} or \proglang{SPSS} style output! See \code{help(CrossTable)} for details.
\subsection{Chi-square test}
For 2-way tables you can use \codefun{chisq.test} to test independence of the row
and column variable. By default, the $p$-value is calculated from the asymptotic
chi-squared distribution of the test statistic. Optionally, the $p$-value can be
derived via Monte Carlo simulation.
<>=
(HairEye <- margin.table(HairEyeColor, c(1, 2)))
chisq.test(HairEye)
@
\subsection{Fisher Exact Test}\label{sec:Fisher}
\code{fisher.test(X)} provides an exact test of independence. \code{X} must be a two-way
contingency table in table form. Another form,
\code{fisher.test(X, Y)} takes two
categorical vectors of the same length.
For tables larger than $2 \times 2$ the method can be computationally intensive (or can fail) if
the frequencies are not small.
<>=
fisher.test(GSStab)
@
But this does not work because \data{HairEye} data has $n$=592 total frequency.
An exact test is unnecessary in this case.
<>=
fisher.test(HairEye)
@
%# <>=
%# #cat(try(fisher.test(HairEye)))
%# @
\begin{Soutput}
Error in fisher.test(HairEye) : FEXACT error 6.
LDKEY is too small for this problem.
Try increasing the size of the workspace.
\end{Soutput}
\subsection[Mantel-Haenszel test]{Mantel-Haenszel test and conditional association}\label{sec:mantel}
Use the \code{mantelhaen.test(X)} function to perform a Cochran-Mantel-Haenszel
$\chi^2$ chi
test of the null hypothesis that two nominal variables are
\emph{conditionally independent}, $A \perp B \given C$, in each stratum, assuming that there is no three-way
interaction. \code{X} is a 3 dimensional contingency table, where the last dimension
refers to the strata.
The \data{UCBAdmissions} serves as an example of a $2 \times 2 \times 6$ table,
with \code{Dept} as the stratifying variable.
<>=
## UC Berkeley Student Admissions
mantelhaen.test(UCBAdmissions)
@
The results show no evidence for association between admission and gender
when adjusted for department. However, we can easily see that the assumption
of equal association across the strata (no 3-way association) is probably
violated. For $2 \times 2 \times k$ tables, this can be examined
from the odds ratios for each $2 \times 2$ table (\codefun{oddsratio}), and
tested
by using
\verb|woolf_test()| in \pkg{vcd}.
%<>=
%oddsRatio <- function(x) (x[1,1]*x[2,2])/(x[1,2]*x[2,1])
%apply(UCBAdmissions, 3, oddsRatio)
%
%woolf_test(UCBAdmissions)
%@
<>=
oddsratio(UCBAdmissions, log=FALSE)
lor <- oddsratio(UCBAdmissions) # capture log odds ratios
summary(lor)
woolf_test(UCBAdmissions)
@
We can visualize the odds ratios of Admission for
each department with fourfold displays using \codefun{fourfold}. The cell
frequencies $n_{ij}$ of each $2 \times 2$ table are shown as a quarter circle whose
radius is proportional to $\sqrt{n_{ij}}$, so that its area is proportional to the
cell frequency.
Confidence rings for the odds ratio allow a visual test of the null of no association;
the rings for adjacent quadrants overlap \emph{iff} the observed counts are consistent
with the null hypothesis. In the extended version (the default), brighter colors
are used where the odds ratio is significantly different from 1.
The following lines produce \figref{fig:fourfold1}.%
\footnote{The color values \code{col[3:4]} were modified from their default values
to show a greater contrast between significant and insignificant associations here.}
<>=
col <- c("#99CCFF", "#6699CC", "#F9AFAF", "#6666A0", "#FF0000", "#000080")
fourfold(UCB,mfrow=c(2,3), color=col)
@
%\setkeys{Gin}{width=0.8\textwidth}
\begin{figure}[htb]
\begin{center}
%<>=
%col <- c("#99CCFF", "#6699CC", "#F9AFAF", "#6666A0", "#FF0000", "#000080")
%fourfold(UCB,mfrow=c(2,3), color=col)
%@
\includegraphics[width=0.8\textwidth,trim=80 50 80 50]{fig/vcd-tut-fourfold1}
\caption{Fourfold display for the \data{UCBAdmissions} data. Where the odds ratio differs
significantly from 1.0, the confidence bands do not overlap, and the circle quadrants are
shaded more intensely.}
\label{fig:fourfold1}
\end{center}
\end{figure}
Another \pkg{vcd} function, \codefun{cotabplot}, provides a more general approach
to visualizing conditional associations in contingency tables,
similar to trellis-like plots produced by \codefun{coplot} and lattice graphics.
The \code{panel} argument supplies a function used to render each conditional
subtable. The following gives a display (not shown) similar to \figref{fig:fourfold1}.
<>=
cotabplot(UCB, panel = cotab_fourfold)
@
When we want to view the conditional
probabilities of a response variable (e.g., \code{Admit})
in relation to several factors,
an alternative visualization is a \codefun{doubledecker} plot.
This plot is a specialized version of a mosaic plot, which
highlights the levels of a response variable (plotted vertically)
in relation to the factors (shown horizontally). The following
call produces \figref{fig:doubledecker}, where we use indexing
on the first factor (\code{Admit}) to make \code{Admitted}
the highlighted level.
In this plot, the
association between \code{Admit} and \code{Gender} is shown
where the heights of the highlighted conditional probabilities
do not align. The excess of females admitted in Dept A stands out here.
<>=
doubledecker(Admit ~ Dept + Gender, data=UCBAdmissions[2:1,,])
@
\begin{figure}[htb]
\begin{center}
\includegraphics[width=0.9\textwidth]{fig/vcd-tut-doubledecker}
\caption{Doubledecker display for the \data{UCBAdmissions} data. The heights
of the highlighted bars show the conditional probabilities of \texttt{Admit},
given \texttt{Dept} and \texttt{Gender}.}
\label{fig:doubledecker}
\end{center}
\end{figure}
Finally, the there is a \codefun{plot} method for \code{oddsratio} objects.
By default, it shows the 95\% confidence interval for the log odds ratio.
\figref{fig:oddsratio} is produced by:
<>=
plot(lor, xlab="Department", ylab="Log Odds Ratio (Admit | Gender)")
@
\setkeys{Gin}{width=0.5\textwidth}
\begin{figure}[htb]
\begin{center}
<>=
plot(lor, xlab="Department", ylab="Log Odds Ratio (Admit | Gender)")
@
\caption{Log odds ratio plot for the \data{UCBAdmissions} data.}
\label{fig:oddsratio}
\end{center}
\end{figure}
\subsection[CMH tests: ordinal factors]{Cochran-Mantel-Haenszel tests for ordinal factors}\label{sec:CMH}
The standard $\chi^2$ tests for association in a two-way table
treat both table factors as nominal (unordered) categories.
When one or both factors of a two-way table are
quantitative or ordinal, more powerful tests of association
may be obtaianed by taking ordinality into account, using
row and or column scores to test for linear trends or differences
in row or column means.
More general versions of the CMH tests (Landis etal., 1978) are provided by assigning
numeric scores to the row and/or column variables.
For example, with two ordinal factors (assumed to be equally spaced), assigning
integer scores, \code{1:R} and \code{1:C} tests the linear $\times$ linear component
of association. This is statistically equivalent to the Pearson correlation between the
integer-scored table variables, with $\chi^2 = (n-1) r^2$, with only 1 $df$
rather than $(R-1)\times(C-1)$ for the test of general association.
When only one table
variable is ordinal, these general CMH tests are analogous to an ANOVA, testing
whether the row mean scores or column mean scores are equal, again consuming
fewer $df$ than the test of general association.
The \codefun{CMHtest} function in \pkg{vcdExtra} now calculates these various
CMH tests for two possibly ordered factors, optionally stratified other factor(s).
\Example
Recall the $4 \times 4$ table, \code{JobSat} introduced in \secref{sec:creating},
<>=
JobSat
@
Treating the \code{satisfaction} levels as equally spaced, but using
midpoints of the \code{income} categories as row scores gives the following results:
<>=
CMHtest(JobSat, rscores=c(7.5,20,32.5,60))
@
Note that with the relatively small cell frequencies, the test for general
give no evidence for association. However, the the \code{cor} test for linear x linear
association on 1 df is nearly significant. The \pkg{coin} contains the
functions \verb|cmh_test()| and \verb|lbl_test()|
for CMH tests of general association and linear x linear association respectively.
\subsection{Measures of Association}
There are a variety of statistical measures of \emph{strength} of association for
contingency tables--- similar in spirit to $r$ or $r^2$ for continuous variables.
With a large sample size, even a small degree of association can show a
significant $\chi^2$, as in the example below for the \data{GSS} data.
The \codefun{assocstats} function in \pkg{vcd} calculates the $\phi$
contingency coefficient, and Cramer's V for an $r \times c$ table.
The input must be in table form, a two-way $r \times c$ table.
It won't work with \data{GSS} in frequency form, but by now you should know how
to convert.
<>=
assocstats(GSStab)
@
For tables with ordinal variables, like \data{JobSat}, some people prefer the
Goodman-Kruskal $\gamma$ statistic (\citet[\S 2.4.3]{vcd:Agresti:2002})
based on a comparison of concordant
and discordant pairs of observations in the case-form equivalent of a two-way table.
<>=
GKgamma(JobSat)
@
A web article by Richard Darlington,
\url{http://www.psych.cornell.edu/Darlington/crosstab/TABLE0.HTM}
gives further description of these and other measures of association.
\subsection{Measures of Agreement}
The
\codefun{Kappa} function in the \pkg{vcd} package calculates Cohen's $\kappa$ and weighted
$\kappa$ for a square two-way table with the same row and column categories \citep{Cohen:60}.%
\footnote{
Don't confuse this with \codefun{kappa} in base \proglang{R} that computes something
entirely different (the condition number of a matrix).
}
Normal-theory $z$-tests are obtained by dividing $\kappa$ by its asymptotic standard
error (ASE). A \codefun{confint} method for \code{Kappa} objects provides confidence intervals.
<>=
(K <- Kappa(SexualFun))
confint(K)
@
A visualization of agreement, both unweighted and weighted for degree of departure
from exact agreement is provided by the \codefun{agreementplot} function.
\figref{fig:agreesex} shows the agreementplot for the \data{SexualFun} data,
produced as shown below. The Bangdiwala measures represent the proportion of the
shaded areas of the diagonal rectangles, using weights $w_1$ for exact agreement,
and $w_2$ for partial agreement one step from the main diagonal.
<>=
agree <- agreementplot(SexualFun, main="Is sex fun?")
unlist(agree)
@
%\setkeys{Gin}{width=0.5\textwidth}
\begin{figure}[htb]
\begin{center}
%<>=
%agree <- agreementplot(SexualFun, main="Is sex fun?")
%agree
%@
\includegraphics[width=0.4\textwidth,trim=50 25 50 25]{fig/vcd-tut-agreesex}
\caption{Agreement plot for the \data{SexualFun} data.}
\label{fig:agreesex}
\end{center}
\end{figure}
In other examples, the agreement plot can help to show \emph{sources}
of disagreement. For example, when the shaded boxes are above or below the diagonal
(red) line, a lack of exact agreement can be attributed in part to
different frequency of use of categories by the two raters-- lack of
\emph{marginal homogeneity}.
\subsection{Correspondence analysis}
Use the \pkg{ca} package for correspondence analysis for visually exploring relationships
between rows and columns in contingency tables. For an $r \times c$ table,
the method provides a breakdown of the Pearson $\chi^2$ for association in up to $M = \min(r-1, c-1)$
dimensions, and finds scores for the row ($x_{im}$) and column ($y_{jm}$) categories
such that the observations have the maximum possible correlations.%
\footnote{
Related methods are the non-parametric
CMH tests using assumed row/column scores (\secref{sec:CMH}),
the analogous \codefun{glm} model-based methods (\secref{sec:CMH}),
and the more general RC models which can be fit using \codefun{gnm}.
Correspondence analysis differs in that it is a primarily
descriptive/exploratory method (no significance tests), but is
directly tied to informative graphic displays of the row/column categories.
}
Here, we carry out a simple correspondence analysis of the \data{HairEye} data.
The printed results show that nearly 99\% of the association between hair color and eye color
can be accounted for in 2 dimensions, of which the first dimension accounts for 90\%.
<>=
library(ca)
ca(HairEye)
@
The resulting \code{ca} object can be plotted just by running the \codefun{plot}
method on the \code{ca} object, giving the result in
\figref{fig:ca-haireye}. \codefun{plot.ca} does not allow labels for dimensions;
these can be added with \codefun{title}.
It can be seen that most of the association is accounted for by the ordering
of both hair color and eye color along Dimension 1, a dark to light dimension.
<>=
plot(ca(HairEye), main="Hair Color and Eye Color")
title(xlab="Dim 1 (89.4%)", ylab="Dim 2 (9.5%)")
@
\setkeys{Gin}{width=0.7\textwidth}
\begin{figure}[htb]
\begin{center}
<>=
plot(ca(HairEye), main="Hair Color and Eye Color")
title(xlab="Dim 1 (89.4%)", ylab="Dim 2 (9.5%)")
@
\caption{Correspondence analysis plot for the \data{HairEye} data.}
\label{fig:ca-haireye}
\end{center}
\end{figure}
\section{Loglinear Models}\label{sec:loglin}
You can use the \codefun{loglm} function in the \pkg{MASS} package to fit log-linear
models. Equivalent models can also be fit (from a different perspective) as generalized
linear models with the \codefun{glm} function using the \code{family='poisson'} argument,
and the \pkg{gnm} package provides a wider range of generalized \emph{nonlinear} models,
particularly for testing structured associations.
The visualization methods for these models were originally developed for models fit using \codefun{loglm},
so this approach is emphasized here. Some extensions of these methods for models
fit using \codefun{glm} and \codefun{gnm} are contained in the \pkg{vcdExtra} package
and illustrated in \secref{sec:glm}.
Assume we have a 3-way contingency table based on
variables A, B, and C.
The possible different forms of \loglin\ models for a 3-way table are shown in \tabref{tab:loglin-3way}.
The \textbf{Model formula} column shows how to express each model for \codefun{loglm} in \proglang{R}.%
\footnote{
For \codefun{glm}, or \codefun{gnm}, with the data in the form of a frequency data.frame, the same model is specified
in the form \code{glm(Freq} $\sim$ \code{..., family="poisson")}, where \texttt{Freq} is the name
of the cell frequency variable and \texttt{...} specifies the \textbf{Model formula}.
}
In the \textbf{Interpretation} column, the symbol ``$\perp$'' is to be read as ``is independent of,''
and ``$\given$'' means ``conditional on,'' or ``adjusting for,'' or just ``given''.
\begin{table}[htb]
\caption{Log-linear Models for Three-Way Tables}\label{tab:loglin-3way}
\begin{center}
\begin{tabular}{llll}
\hline
\textbf{Model} & \textbf{Model formula} & \textbf{Symbol}& \textbf{Interpretation} \\
\hline\hline
Mutual independence & \verb|~A + B + C| & $[A][B][C]$ & $A \perp B \perp C$ \\
Joint independence & \verb|~A*B + C| & $[AB][C]$ & $(A \: B) \perp C$ \\
Conditional independence & \verb|~(A+B)*C| & $[AC][BC]$ & $(A \perp B) \given C$ \\
All two-way associations & \verb|~A*B + A*C + B*C| & $[AB][AC][BC]$ & homogeneous association \\
Saturated model & \verb|~A*B*C| & $[ABC]$ & 3-way association \\
\hline
\end{tabular}
\end{center}
\end{table}
For example, the formula \verb|~A + B + C| specifies the model of \emph{mutual independence} with
no associations among the three factors. In standard notation for the expected frequencies
$m_{ijk}$, this corresponds to
\begin{equation*}
\log ( m_{ijk} ) = \mu + \lambda_i^A + \lambda_j^B + \lambda_k^C \equiv \texttt{A + B + C}
\end{equation*}
The parameters $\lambda_i^A , \lambda_j^B$ and $\lambda_k^C$ pertain to the differences among the
one-way marginal frequencies for the factors A, B and C.
Similarly, the model of \emph{joint independence}, $(A \: B) \perp C$,
allows an association between A and B, but specifies that
C is independent of both of these and their combinations,
\begin{equation*}
\log ( m_{ijk} ) = \mu + \lambda_i^A + \lambda_j^B + \lambda_k^C + \lambda_{ij}^{AB} \equiv \texttt{A * B + C}
\end{equation*}
where the parameters $\lambda_{ij}^{AB}$ pertain to the overall association between A and B (collapsing over C).
In the literature or text books, you will often find these models expressed in shorthand symbolic notation,
using brackets, \texttt{[ ]} to enclose the \emph{high-order terms} in the model.
Thus, the joint independence model can be denoted \texttt{[AB][C]}, as shown in the \textbf{Symbol} column in
\tabref{tab:loglin-3way}.
Models of \emph{conditional independence} allow (and fit) two of the three possible
two-way associations. There are three such models, depending on which variable is conditioned upon.
For a given conditional independence model, e.g., \texttt{[AB][AC]}, the given variable is the one
common to all terms, so this example has the interpretation $(B \perp C) \given A$.
\subsection[Fitting with loglm()]{Fitting with \codefun{loglm}}\label{sec:loglm}
For example, we can fit the model of mutual independence among hair color, eye color and sex
in \data{HairEyeColor} as
<>=
library(MASS)
## Independence model of hair and eye color and sex.
hec.1 <- loglm(~Hair+Eye+Sex, data=HairEyeColor)
hec.1
@
Similarly, the models of conditional independence and joint independence are specified as
<>=
## Conditional independence
hec.2 <- loglm(~(Hair + Eye) * Sex, data=HairEyeColor)
hec.2
@
<>=
## Joint independence model.
hec.3 <- loglm(~Hair*Eye + Sex, data=HairEyeColor)
hec.3
@
Note that printing the model gives a brief summary of the goodness of fit.
A set of models can be compared using the \codefun{anova} function.
<>=
anova(hec.1, hec.2, hec.3)
@
%Martin Theus and Stephan Lauer have written an excellent article on Visualizing
%Loglinear Models, using mosaic plots. There is also great tutorial example by
%Kevin Quinn on analyzing loglinear models via glm.
\subsection[Fitting with glm() and gnm()]{Fitting with \codefun{glm} and \codefun{gnm}}\label{sec:glm}
The \codefun{glm} approach, and extensions of this in the \pkg{gnm} package allows a
much wider class of models for frequency data to be fit than can be handled by
\codefun{loglm}. Of particular importance are models for ordinal factors and for
square tables, where we can test more structured hypotheses about the patterns
of association than are provided in the tests of general association under
\codefun{loglm}. These are similar in spirit to the
non-parametric CMH tests described in \secref{sec:CMH}.
\Example
The data \code{Mental} in the \pkg{vcdExtra} package gives a two-way table in frequency form
classifying young people by
their mental health status and parents' socioeconomic status (SES), where
both of these variables are ordered factors.
<>=
str(Mental)
xtabs(Freq ~ mental+ses, data=Mental) # display the frequency table
@
Simple ways of handling ordinal variables involve assigning scores to the table
categories, and the simplest cases are to use integer scores, either for the row variable (``column
effects'' model), the column variable (``row effects'' model), or both (``uniform association''
model).
<>=
indep <- glm(Freq ~ mental + ses, family = poisson, data = Mental) # independence model
@
To fit more parsimonious models than general association, we can define
numeric scores for the row and column categories
<>=
# Use integer scores for rows/cols
Cscore <- as.numeric(Mental$ses)
Rscore <- as.numeric(Mental$mental)
@
Then, the row effects model, the column effects model, and the uniform association
model can be fit as follows:
<>=
# column effects model (ses)
coleff <- glm(Freq ~ mental + ses + Rscore:ses, family = poisson, data = Mental)
# row effects model (mental)
roweff <- glm(Freq ~ mental + ses + mental:Cscore, family = poisson, data = Mental)
# linear x linear association
linlin <- glm(Freq ~ mental + ses + Rscore:Cscore, family = poisson, data = Mental)
@
The \codefun{Summarize} in \pkg{vcdExtra} provides a nice, compact summary of
the fit statistics for a set of models, collected into a \class{glmlist} object.
Smaller is better for AIC and BIC.
<>=
# compare models using AIC, BIC, etc
vcdExtra::LRstats(glmlist(indep, roweff, coleff, linlin))
@
For specific model comparisons, we can also carry out tests of \emph{nested} models with
\codefun{anova} when those models are listed from smallest to largest.
Here, there are two separate paths from the most restrictive (independence) model
through the model of uniform association, to those that allow only one of
row effects or column effects.
<>=
anova(indep, linlin, coleff, test="Chisq")
anova(indep, linlin, roweff, test="Chisq")
@
The model of linear by linear association seems best on all accounts.
For comparison, one might try the CMH tests on these data:
<>=
CMHtest(xtabs(Freq~ses+mental, data=Mental))
@
\subsection{Non-linear terms}
The strength of the \pkg{gnm} package is that it handles a wide variety of models
that handle non-linear terms, where the parameters enter the model beyond a simple
linear function.
The simplest example is the Goodman RC(1) model, which allows a multiplicative
term to account for the association of the table variables.
In the notation of generalized linear models with a log link, this can be expressed as
\begin{equation*}
\log \mu_{ij} = \alpha_i + \beta_j + \gamma_{i} \delta_{j}
\end{equation*}
where the row-multiplicative effect parameters $\gamma_i$ and
corresponding column parameters $\delta_j$ are estimated from the data.%
\footnote{
This is similar in spirit to a correspondence analysis with a single
dimension, but as a statistical model.
}
Similarly, the RC(2) model adds two multiplicative terms to
the independence model,
\begin{equation*}
\log \mu_{ij} = \alpha_i + \beta_j + \gamma_{i1} \delta_{j1} + \gamma_{i2} \delta_{j2}
\end{equation*}
In the \pkg{gnm} package, these models may be fit using the \codefun{Mult}
to specify the multiplicative term, and \codefun{instances} to specify several
such terms.
\Example
For the \code{Mental} data, we fit the RC(1) and RC(2) models, and compare
these with the independence model.
<>=
RC1 <- gnm(Freq ~ mental + ses + Mult(mental,ses), data=Mental,
family=poisson, , verbose=FALSE)
RC2 <- gnm(Freq ~ mental+ses + instances(Mult(mental,ses),2), data=Mental,
family=poisson, verbose=FALSE)
anova(indep, RC1, RC2, test="Chisq")
@
\section{Mosaic plots}\label{sec:mosaic}
Mosaic plots provide an ideal method both for visualizing contingency tables and for
visualizing the fit--- or more importantly--- lack of fit of a \loglin\ model.
For a two-way table, \codefun{mosaic} fits a model of independence, $[A][B]$
or \verb|~A+B| as an \proglang{R} formula.
For $n$-way tables, \codefun{mosaic} can fit any \loglin\ model, and can also be
used to plot a model fit with \codefun{loglm}.
See \citet{vcd:Friendly:1994,vcd:Friendly:1999} for the statistical ideas behind these
uses of mosaic displays in connection with \loglin\ models.
The essential idea is to recursively sub-divide a unit square into rectangular ``tiles'' for the
cells of the table, such
that the are area of each tile is proportional to the cell frequency.
For a given \loglin\ model, the tiles can then be shaded in various ways to reflect
the residuals (lack of fit) for a given model. The pattern of residuals can then
be used to suggest a better model or understand \emph{where} a given model fits or
does not fit.
\codefun{mosaic} provides a wide range of options for the directions of splitting,
the specification of shading, labeling, spacing, legend and many other details.
It is actually implemented as a special case of a more general
class of displays for $n$-way tables called \code{strucplot}, including
sieve diagrams, association plots, double-decker plots as well as mosaic
plots. For details, see \code{help(strucplot)} and the ``See also'' links,
and also \citet{vcd:Meyer+Zeileis+Hornik:2006b}, which is available as
an \proglang{R} vignette via \code{vignette("strucplot", package="vcd")}.
\figref{fig:arthritis}, showing the association between
\code{Treatment} and \code{Improved} was produced with the following
call to \codefun{mosaic}.
<>=
mosaic(art, gp = shading_max, split_vertical = TRUE,
main="Arthritis: [Treatment] [Improved]")
@
Note that the residuals for the independence model were not large
(as shown in the legend),
yet the association between \code{Treatment} and \code{Improved}
is highly significant.
<>=
summary(art)
@
In contrast, one of the other shading schemes, from \citet{vcd:Friendly:1994}
(use: \verb|gp = shading_Friendly|),
uses fixed cutoffs of $\pm 2, \pm 4$,
to shade cells which are \emph{individually} significant
at approximately $\alpha = 0.05$ and $\alpha = 0.001$ levels, respectively.
The right panel below uses \verb|gp = shading_Friendly|.
\setkeys{Gin}{width=0.5\textwidth}
<>=
mosaic(art, gp = shading_max, split_vertical = TRUE,
main="Arthritis: gp = shading_max")
@
<>=
mosaic(art, gp = shading_Friendly, split_vertical = TRUE,
main="Arthritis: gp = shading_Friendly")
@
\subsection[Mosaics for loglinear models]{Mosaics for \loglin\ models}\label{sec:mosaic-llm}
When you have fit a \loglin\ model using \codefun{loglm},
and saved the result (as a \code{loglm} object) the simplest way to display the
results is to use the \codefun{plot} method for the \code{loglm} object.
Calling \code{mosaic(loglm.object)} has the same result.
In \secref{sec:loglm} above, we fit several different models to the
\data{HairEyeColor} data. We can produce mosaic displays of each just
by plotting them:
<>=
# mosaic plots, using plot.loglm() method
plot(hec.1, main="model: [Hair][Eye][Sex]")
plot(hec.2, main="model: [HairSex][EyeSex]")
plot(hec.3, main="model: [HairEye][Sex]")
@
\setkeys{Gin}{width=0.32\textwidth}
<>=
plot(hec.1, main="model: [Hair][Eye][Sex]")
@
<>=
plot(hec.2, main="model: [HairSex][EyeSex]")
@
<>=
plot(hec.3, main="model: [HairSex][EyeSex]")
@
Alternatively, you can supply the model formula to \codefun{mosaic}
with the \code{expected} argument. This is passed to \codefun{loglm},
which fits the model, and returns residuals used for shading in the plot.
For example, here we examine the \data{TV2} constructed in \secref{sec:complex}
above. The goal is to see how Network choice depends on (varies with)
Day and Time. To do this:
\begin{itemize}
\item We fit a model of joint independence of
\code{Network} on the combinations of \code{Day} and \code{Time},
with the model formula \verb|~Day:Time + Network|.
\item To make the display more easily read, we place \code{Day} and \code{Time}
on the vertical axis and \code{Network} on the horizontal,
\item The \code{Time} values overlap on the right vertical axis, so we use
\codefun{level} to abbreviate them. \codefun{mosaic} also supports a
more sophisticated set of labeling functions. Instead of changing the data
table, we could have used
\verb|labeling_args = list(abbreviate = c(Time = 2))| for a similar effect.
\end{itemize}
The following call to \codefun{mosaic} produces \figref{fig:TV-mosaic}:
<>=
dimnames(TV2)$Time <- c("8", "9", "10") # re-level for mosaic display
mosaic(~ Day + Network + Time, data=TV2, expected=~Day:Time + Network,
legend=FALSE, gp=shading_Friendly)
@
\setkeys{Gin}{width=0.75\textwidth}
\begin{figure}[htb]
\begin{center}
<>=
dimnames(TV2)$Time <- c("8", "9", "10") # re-level for mosaic display
mosaic(~ Day + Network + Time, data=TV2, expected=~Day:Time + Network,
legend=FALSE, gp=shading_Friendly)
@
\caption{Mosaic plot for the \data{TV} data
showing model of joint independence, \texttt{Day:Time + Network} .}
\label{fig:TV-mosaic}
\end{center}
\end{figure}
From this, it is easy to read from the display how network choice varies with day and
time. For example, CBS dominates in all time slots on Monday;
ABC and NBC dominate on Tuesday, particularly in the later time slots;
Thursday is an NBC day, while on Friday, ABC gets the greatest share.
In interpreting this mosaic and other plots, it is important to understand that
associations included in the model---here, that between day and time---are \emph{not}
shown in the shading of the cells, because they have been fitted (taken into account)
in the \loglin\ model.
For comparison, you might want to try fitting the model of homogeneous association.
This allows all pairs of factors to be associated, but asserts that each pairwise
association is the same across the levels of the remaining factor.
The resulting plot displays the contributions to a 3-way association, but is not shown here.
<>=
mosaic(~ Day + Network + Time, data=TV2,
expected=~Day:Time + Day:Network + Time:Network,
legend=FALSE, gp=shading_Friendly)
@
\subsection[Mosaics for glm() and gnm() models]{Mosaics for \codefun{glm} and \codefun{gnm} models}\label{sec:mosglm}
The \pkg{vcdExtra} package provides an additional method, \codefun{mosaic.glm}
for models fit with \codefun{glm} and \codefun{gnm}.%
\footnote{
Models fit with \codefun{gnm} are of \code{class = c("gnm", "glm", "lm")},
so all \code{*.glm} methods apply, unless overridden in the \pkg{gnm} package.
}
These are not restricted to the
Poisson family, but only apply to cases where the response variable is non-negative.
\Example
Here, we plot the independence and the linear-by-linear association model
for the Mental health data from \secref{sec:glm}.
These examples illustrate some of the options for labeling (variable names and
residuals printed in cells). Note that the \code{formula} supplied to \codefun{mosaic}
for \class{glm} objects refers to the order of factors displayed in the plot, not the model.
<>=
long.labels <- list(set_varnames = c(mental="Mental Health Status", ses="Parent SES"))
mosaic(indep, ~ses+mental, residuals_type="rstandard",
labeling_args = long.labels, labeling=labeling_residuals,
main="Mental health data: Independence")
mosaic(linlin, ~ses+mental, residuals_type="rstandard",
labeling_args = long.labels, labeling=labeling_residuals, suppress=1,
gp=shading_Friendly, main="Mental health data: Linear x Linear")
@
\setkeys{Gin}{width=0.49\textwidth}
<>=
long.labels <- list(set_varnames = c(mental="Mental Health Status", ses="Parent SES"))
mosaic(indep, ~ses+mental, residuals_type="rstandard",
labeling_args = long.labels, labeling=labeling_residuals,
main="Mental health data: Independence")
@
<>=
long.labels <- list(set_varnames = c(mental="Mental Health Status", ses="Parent SES"))
mosaic(linlin, ~ses+mental, residuals_type="rstandard",
labeling_args = long.labels, labeling=labeling_residuals, suppress=1,
gp=shading_Friendly, main="Mental health data: Linear x Linear")
@
The \pkg{gnm} package also fits a wide variety of models with nonlinear terms or terms for
structured associations of table variables. In the following, we fit the RC(1)
model
\begin{equation*}
\log ( m_{ij} ) = \mu + \lambda_i^A + \lambda_j^B + \phi \mu_i \nu_j
\end{equation*}
This is similar to the linear by linear model, except that the row effect
parameters ($\mu_i$) and column parameters ($\nu_j$) are estimated from the data
rather than given assigned equally-spaced values. The multiplicative terms
are specified by the \codefun{Mult}.
<>=
Mental$mental <- C(Mental$mental, treatment)
Mental$ses <- C(Mental$ses, treatment)
RC1model <- gnm(Freq ~ mental + ses + Mult(mental, ses),
family = poisson, data = Mental)
mosaic(RC1model, residuals_type="rstandard", labeling_args = long.labels,
labeling=labeling_residuals, suppress=1, gp=shading_Friendly,
main="Mental health data: RC(1) model")
@
Other forms of nonlinear terms are provided for the inverse of a predictor
(\codefun{Inv}) and the exponential of a predictor (\codefun{Exp}).
You should read \code{vignette("gnmOverview", package="gnm")} for further details.
\subsection{Mosaic tips and techniques}\label{sec:tips}
The \pkg{vcd} package implements an extremely general collection of graphical methods for
$n$-way frequency tables within the strucplot framework, which includes mosaic plots
(\codefun{mosaic}), as well as association plots (\codefun{assoc}), sieve diagrams
(\codefun{sieve}), as well as tabular displays (\codefun{structable}).
The graphical methods in \pkg{vcd} support a wide of options that control almost all of the details
of the plots, but it is often difficult to determine what arguments you need to supply
to achieve a given effect from the \code{help()}. As a first step, you should read the
\code{vignette("strucplot")} in \pkg{vcd} to understand the overall structure of
these plot methods. The notes below describe a few useful things that may not be obvious,
or can be done in different ways.
\subsubsection[Changing labels]{Changing the labels for variables and levels}
With data in contingency table form or as a frequency data frame, it often happens
that the variable names and/or the level values of the factors, while suitable
for analysis, are less than adequate when used in mosaic plots and other strucplot
displays.
For example, we might prefer that a variable named \code{ses} appear
as \code{"Socioeconomic Status"}, or a factor with levels \code{c("M", "F")}
be labeled using \code{c("Male", "Female")} in a plot. Or, sometimes we
start with a factor whose levels are fully spelled out
(e.g., \code{c("strongly disagree", "disagree", "neutral", "agree", "strongly agree")}),
only to find that the level labels overlap in graphic displays.
The strucplot framework in \pkg{vcd} provides an extremely large variety of
functions and options for controlling almost all details of text labels in mosaics
and other plots. See \code{help(labelings)} for an overview.
For example, in \secref{sec:ordered-factors} we showed how to rearrange the dimensions
of the \code{UCBAdmissions} table, change the names of the table variables, and
relabel the levels of one of the table variables.
The code below changes the actual table for plotting purposes, but we pointed out that
these changes can create other problems in analysis.
<>=
UCB <- aperm(UCBAdmissions, c(2, 1, 3))
names(dimnames(UCB)) <- c("Sex", "Admit?", "Department")
dimnames(UCB)[[2]] <- c("Yes", "No")
@
The same effects can be achieved \emph{without} modifying the data
using the \verb|set_varnames| and
\verb|set_labels| options in \codefun{mosaic} as follows:
<>=
vnames <- list(set_varnames = c(Admit="Admission", Gender="Sex", Dept="Department"))
lnames <- list(Admit = c("Yes", "No"),
Gender = c("Males", "Females"),
Dept = LETTERS[1:6])
mosaic(UCBAdmissions, labeling_args=vnames, set_labels=lnames)
@
In some cases, it may be sufficient to abbreviate (or clip, or rotate) level names to avoid
overlap. For example, the statements below produce another version of
\figref{fig:TV-mosaic} with days of the week abbreviated to their first three letters.
Section 4 in the \code{vignette("strucplot")} provides many other examples.
<>=
dimnames(TV2)$Time <- c("8", "9", "10") # re-level for mosaic display
mosaic(~ Day + Network + Time, data=TV2, expected=~Day:Time + Network,
legend=FALSE, gp=shading_Friendly,
labeling_args=list(abbreviate=c(Day=3)) )
@
%\subsubsection{Fitting complex models with glm() and gnm()}
\section[Continuous predictors]{Continuous predictors}\label{sec:contin}
When continuous predictors are available---and potentially important--- in explaining a
categorical outcome, models for that outcome include:
logistic regression (binary response),
the proportional odds model (ordered polytomous response),
multinomial (generalized) logistic regression.
Many of these are special cases of the generalized linear model using the
\code{"poisson"} or \code{"binomial"} family and their relatives.
\subsection{Spine and conditional density plots}\label{sec:spine}
I don't go into fitting such models here, but I would be remiss not to illustrate some
visualizations in \pkg{vcd} that are helpful here.
The first of these is the spine plot or spinogram \citep{vcd:Hummel:1996}
(produced with \codefun{spine}).
These are special cases of mosaic plots with
specific spacing and shading to show how a categorical response varies with
a continuous or categorical predictor.
They are also a generalization of stacked bar plots where not the heights but
the \emph{widths} of the bars corresponds to the relative frequencies of \code{x}. The heights
of the bars then correspond to the conditional relative frequencies of {y} in
every \code{x} group.
\Example
For the \data{Arthritis} data, we can see how \code{Improved} varies with \code{Age}
as follows. \codefun{spine} takes a formula of the form
\verb|y ~ x| with a single dependent factor and a single explanatory variable \code{x}
(a numeric variable or a factor).
The range of a numeric variable\code{x} is divided into intervals based on the
\code{breaks} argument, and stacked bars are drawn to show the distribution of
\code{y} as \code{x} varies. As shown below, the discrete table that is visualized
is returned by the function.
<>=
(spine(Improved ~ Age, data = Arthritis, breaks = 3))
(spine(Improved ~ Age, data = Arthritis, breaks = "Scott"))
@
\setkeys{Gin}{width=0.49\textwidth}
<>=
(spine(Improved ~ Age, data = Arthritis, breaks = 3))
@
<>=
(spine(Improved ~ Age, data = Arthritis, breaks = "Scott"))
@
The conditional density plot \citep{vcd:Hofmann+Theus} is a further generalization.
This visualization technique is similar to spinograms, but uses a smoothing approach
rather than discretizing the explanatory variable. As well, it uses
the original \code{x} axis and not a distorted one.
\setkeys{Gin}{width=0.6\textwidth}
\begin{figure}[htb]
\begin{center}
<>=
cdplot(Improved ~ Age, data = Arthritis)
with(Arthritis, rug(jitter(Age), col="white", quiet=TRUE))
@
\caption{Conditional density plot for the \data{Arthritis} data
showing the variation of Improved with Age.}
\label{fig:cd-plot}
\end{center}
\end{figure}
In such plots, it is useful to also see the distribution of the observations
across the horizontal axis, e.g., with a \codefun{rug} plot.
\figref{fig:cd-plot} uses \codefun{cdplot} from the \pkg{graphics} package
rather than \verb|cd_plot()| from \pkg{vcd}, and is produced with
<>=
cdplot(Improved ~ Age, data = Arthritis)
with(Arthritis, rug(jitter(Age), col="white", quiet=TRUE))
@
From \figref{fig:cd-plot} it can be easily seen that the proportion
of patients reporting Some or Marked improvement increases with Age,
but there are some peculiar bumps in the distribution.
These may be real or artifactual, but they would be hard to see
with most other visualization methods.
When we switch from non-parametric data exploration to parametric
statistical models, such effects are easily missed.
\subsection[Model-based plots]{Model-based plots: effect plots and \pkg{ggplot2} plots}\label{sec:modelplots}
The nonparametric conditional density plot uses smoothing methods
to convey the distributions of the response variable,
but displays that are simpler to interpret can often be obtained by plotting
the predicted response from a parametric model.
For complex \codefun{glm} models with interaction effects, the \pkg{effects}
package provides the most useful displays,
plotting the predicted values for a given term, averaging over other
predictors not included in that term. I don't illustrate this here, but
see \citet{effects:1,effects:2} and \code{help(package="effects")}.
Here I just briefly illustrate the capabilities of the \pkg{ggplot2} package
for model-smoothed plots of categorical responses in \codefun{glm} models.
\Example
The \data{Donner} data frame in \pkg{vcdExtra} gives details on the survival
of 90 members of the Donner party,
a group of people who attempted to migrate to California in 1846.
They were trapped by an early blizzard on the eastern side of the
Sierra Nevada mountains, and before they could be rescued,
nearly half of the party had died.
What factors affected who lived and who died?
<>=
data(Donner, package="vcdExtra")
str(Donner)
@
A potential model of interest
is the logistic regression model for $Pr(survived)$, allowing separate
fits for males and females as a function of \code{age}.
The key to this is the \verb|stat_smooth()| function, using
\code{method = "glm", method.args = list(family = binomial)}. The \verb|formula = y ~ x|
specifies a linear fit on the logit scale (\figref{fig:donner3}, left)
<>=
# separate linear fits on age for M/F
ggplot(Donner, aes(age, survived, color = sex)) +
geom_point(position = position_jitter(height = 0.02, width = 0)) +
stat_smooth(method = "glm", method.args = list(family = binomial), formula = y ~ x,
alpha = 0.2, size=2, aes(fill = sex))
@
Alternatively, we can allow a quadratic relation with \code{age}
by specifying \verb|formula = y ~ poly(x,2)| (\figref{fig:donner3}, right).
<>=
# separate quadratics
ggplot(Donner, aes(age, survived, color = sex)) +
geom_point(position = position_jitter(height = 0.02, width = 0)) +
stat_smooth(method = "glm", method.args = list(family = binomial), formula = y ~ poly(x,2),
alpha = 0.2, size=2, aes(fill = sex))
@
\setkeys{Gin}{width=0.49\textwidth}
\begin{figure}[htb]
\begin{center}
<>=
ggplot(Donner, aes(age, survived, color = sex)) +
geom_point(position = position_jitter(height = 0.02, width = 0)) +
stat_smooth(method = "glm", method.args = list(family = binomial), formula = y ~ x,
alpha = 0.2, size=2, aes(fill = sex))
@
<>=
# separate quadratics
ggplot(Donner, aes(age, survived, color = sex)) +
geom_point(position = position_jitter(height = 0.02, width = 0)) +
stat_smooth(method = "glm", method.args = list(family = binomial), formula = y ~ poly(x,2),
alpha = 0.2, size=2, aes(fill = sex))
@
\caption{Logistic regression plots for the \data{Donner} data
showing survival vs. age, by sex. Left: linear logistic model; right: quadratic model}
\label{fig:donner3}
\end{center}
\end{figure}
These plots very nicely show (a) the fitted $Pr(survived)$ for males and females;
(b) confidence bands around the smoothed model fits and (c) the individual
observations by jittered points at 0 and 1 for those who died and survived, respectively.
\bibliography{vcd,vcdExtra}
\end{document}
vcdExtra/inst/doc/vcd-tutorial.pdf 0000644 0001762 0000144 00002060011 14202474402 016663 0 ustar ligges users %PDF-1.5
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## Extensions and additions to vcd: Visualizing Categorical Data
Version 0.7-6
This package provides additional data sets, documentation, and
a few functions designed to extend the [vcd](https://CRAN.R-project.org/package=vcd) package for *Visualizing Categorical Data*
and the [gnm](https://CRAN.R-project.org/package=gnm) package for *Generalized Nonlinear Models*.
In particular, `vcdExtra` extends mosaic, assoc and sieve plots from vcd to handle `glm()` and `gnm()` models and
adds a 3D version in `mosaic3d()`.
`vcdExtra` is now a support package for the book [*Discrete Data Analysis with R*](http://ddar.datavis.ca) by Michael Friendly and David Meyer.
### Details
The main purpose of this package is to serve as a sandbox for
introducing extensions of
mosaic plots and related graphical methods
that apply to loglinear models fitted using
`glm()` and related, generalized nonlinear models fitted
with `gnm()` in the gnm package.
A related purpose is to fill in some holes in the analysis of
categorical data in R, not provided in base R, vcd,
or other commonly used packages.
* The method `mosaic.glm()`
extends the `mosaic.loglm()` method in the vcd
package to this wider class of models. This method also works for
the generalized nonlinear models fit with the gnm package,
including models for square tables and models with multiplicative associations.
* `mosaic3d()`
introduces a 3D generalization of mosaic displays using the
[rgl](https://CRAN.R-project.org/package=rgl) package.
* A new class, `glmlist`, is introduced for working with
collections of glm objects, e.g., `Kway()` for fitting
all K-way models from a basic marginal model, and `LRstats()`
for brief statistical summaries of goodness-of-fit for a collection of
models.
* For square tables with ordered factors, `Crossings()` supplements the
specification of terms in model formulas using
`Symm()`,
`Diag()`,
`Topo(),` etc. in the [gnm](https://CRAN.R-project.org/package=gnm) package.
* In addition, there are many new data sets, a tutorial vignette,
_Working with categorical data with R and the vcd package_, `vignette("vcd-tutorial", package = "vcdExtra")`,
and a few useful utility functions for manipulating categorical data sets and working with models for
categorical data.
## Installation
Get the released version from CRAN:
install.packages("vcdExtra")
The development version can be installed to your R library directly from this repo via:
if (!require(remotes)) install.packages("remotes")
remotes::install_github("friendly/vcdExtra")
vcdExtra/data/ 0000755 0001762 0000144 00000000000 13164013351 012740 5 ustar ligges users vcdExtra/data/Titanicp.rda 0000644 0001762 0000144 00000007436 14202474403 015220 0 ustar ligges users BZh91AY&SYW,t U\^]X?ؘP 0
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