Revision ade4b694b3f416d6b195ddcd584d6f89ef36ea2f authored by M. Helena Gon\xe7alves on 28 January 2012, 00:00:00 UTC, committed by Gabor Csardi on 28 January 2012, 00:00:00 UTC
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airpollution.rd
\name{airpollution}
\alias{airpollution}
\docType{data}
\title{Air Pollution}
\description{This example is a subset of data from Six Cities study, a longitudinal study of the health effects of
air pollution (Ware, J. H. et al., 1984)}
\usage{data(airpollution)}
\format{
A data frame with 128 observations on the following 5 variables.
\describe{
\item{\code{id}}{identifies de number of the individual profile. This vector contains observations of 32 individual profiles.}
\item{\code{wheeze}}{a numeric vector that identify the wheezing status (1="yes", 0="no") of a child at each occasion.}
\item{\code{age}}{a numeric vector corresponding to the \code{age} in years since the child's 9th birthday.}
\item{\code{smoking}}{a factor that identify if the mother smoke (1="smoke", 0="no smoke").}
\item{\code{counts}}{a numeric vector corresponding to the replications of each individual profile.}
}
}
\details{The data set presented by Fitzmaurice and Laird (1993) contains complete records
on 537 children from Steubnville, Ohio, each woman was examined annually at ages 7 through 10.
The repeated binary response is the wheezing status (1="yes", 0="no") of a child at each occasion.
Although mother's smoking status could vary with time, it was determined in the first interview and
was treated as a time-independent covariate. Maternal smoking was categorized as 1 if the mother
smoked regularly and 0 otherwise.}
\source{ Fitzmaurice, G. M. and Laird, N. M. (1993).
A Likelihood-Based Method for analyzing Longitudinal Binary Response.
\emph{Biometrika}, 80, 141-51.}

\references{  Ware, J. H., Dockery, D. W., Spiro,  A. III, Speizer, F. E. and Ferris, B. G., Jr. (1984).
Passive smoking, gas cooking and respiratory health in children
living in six cities. \emph{Am. Rev. Respir. dis.}, 129, 366-74.
}
\examples{
str(airpollution)

#####  dependence="MC2"
air2 <- bild(wheeze~age+smoking, data=airpollution, trace=TRUE, time="age",
aggregate=smoking, dependence="MC2")

summary(air2)
getAIC(air2)
getLogLik(air2)

plot(air2)

#####  dependence="MC2R"
air2r <- bild(wheeze~age+smoking, data=airpollution, trace=TRUE, time="age",
aggregate=smoking, dependence="MC2R")

summary(air2r)
getAIC(air2r)
getLogLik(air2r)

plot(air2r)

plot(air2r, which=6, subSET=smoking=="0", main="smoking==0", ident=TRUE)

}
\keyword{datasets}



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