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\name{epilepsy}
\alias{epilepsy}
\docType{data}
\title{Epilepsy Attacks Data Set}
\description{Data from a clinical trial of 59 patients with epilepsy
(Breslow, 1996) in order to illustrate diagnostic techniques in
Poisson regression.
}
\usage{data(epilepsy, package="robustbase")}
\format{
A data frame with 59 observations on the following 11 variables.
\describe{
\item{\code{ID}}{Patient identification number}
\item{\code{Y1}}{Number of epilepsy attacks patients have during the
first follow-up period}
\item{\code{Y2}}{Number of epilepsy attacks patients have during the
second follow-up period}
\item{\code{Y3}}{Number of epilepsy attacks patients have during the
third follow-up period}
\item{\code{Y4}}{Number of epilepsy attacks patients have during the
forth follow-up period}
\item{\code{Base}}{Number of epileptic attacks
recorded during 8 week period prior to randomization}
\item{\code{Age}}{Age of the patients}
\item{\code{Trt}}{a factor with levels \code{placebo}
\code{progabide} indicating whether the anti-epilepsy
drug Progabide has been applied or not}
\item{\code{Ysum}}{Total number of epilepsy attacks patients have
during the four follow-up periods }
\item{\code{Age10}}{Age of the patients devided by 10}
\item{\code{Base4}}{Variable \code{Base} devided by 4}
}
}
\details{Thall and Vail reported data from a clinical trial of 59 patients
with epilepsy, 31 of whom were randomized to receive the anti-epilepsy
drug Progabide and 28 of whom received a placebo. Baseline data
consisted of the patient's age and the number of epileptic seizures
recorded during 8 week period prior to randomization. The response
consisted of counts of seizures occuring during the four consecutive
follow-up periods of two weeks each.
}
\source{
Thall, P.F. and Vail S.C. (1990)
Some covariance models for longitudinal count data with overdispersion.
\emph{Biometrics} \bold{46}, 657--671.
}
\references{
Diggle, P.J., Liang, K.Y., and Zeger, S.L. (1994)
\emph{Analysis of Longitudinal Data}; Clarendon Press.
Breslow N. E. (1996)
Generalized linear models: Checking assumptions and strengthening
conclusions.
\emph{Statistica Applicata} \bold{8}, 23--41.
}
\examples{
data(epilepsy)
str(epilepsy)
pairs(epilepsy[,c("Ysum","Base4","Trt","Age10")])
Efit1 <- glm(Ysum ~ Age10 + Base4*Trt, family=poisson, data=epilepsy)
summary(Efit1)
## Robust Fit :
Efit2 <- glmrob(Ysum ~ Age10 + Base4*Trt, family=poisson, data=epilepsy,
method = "Mqle",
tcc=1.2, maxit=100)
summary(Efit2)
}
\keyword{datasets}
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