\HeaderA{epil}{Seizure Counts for Epileptics}{epil}
\keyword{datasets}{epil}
\begin{Description}\relax
Thall and Vail (1990) give a data set on two-week seizure counts for
59 epileptics.  The number of seizures was recorded for a baseline
period of 8 weeks, and then patients were randomly assigned to a
treatment group or a control group.  Counts were then recorded for
four successive two-week periods. The subject's age is the only
covariate.
\end{Description}
\begin{Usage}
\begin{verbatim}
epil
\end{verbatim}
\end{Usage}
\begin{Format}\relax
This data frame has 236 rows and the following 9 columns:
\describe{
\item[\code{y}] the count for the 2-week period.

\item[\code{trt}] treatment, \code{"placebo"} or \code{"progabide"}.

\item[\code{base}] the counts in the baseline 8-week period.

\item[\code{age}] subject's age, in years.

\item[\code{V4}] \code{0/1} indicator variable of period 4.

\item[\code{subject}] subject number, 1 to 59.

\item[\code{period}] period, 1 to 4.

\item[\code{lbase}] log-counts for the baseline period, centred to have zero mean.

\item[\code{lage}] log-ages, centred to have zero mean.

}
\end{Format}
\begin{Source}\relax
Thall, P. F. and Vail, S. C. (1990)
Some covariance models for longitudinal count data with over-dispersion.
\emph{Biometrics} \bold{46}, 657--671.
\end{Source}
\begin{References}\relax
Venables, W. N. and Ripley, B. D. (2002)
\emph{Modern Applied Statistics with S.} Fourth Edition. Springer.
\end{References}
\begin{Examples}
\begin{ExampleCode}
summary(glm(y ~ lbase*trt + lage + V4, family = poisson,
            data = epil), cor = FALSE)
epil2 <- epil[epil$period == 1, ]
epil2["period"] <- rep(0, 59); epil2["y"] <- epil2["base"]
epil["time"] <- 1; epil2["time"] <- 4
epil2 <- rbind(epil, epil2)
epil2$pred <- unclass(epil2$trt) * (epil2$period > 0)
epil2$subject <- factor(epil2$subject)
epil3 <- aggregate(epil2, list(epil2$subject, epil2$period > 0),
   function(x) if(is.numeric(x)) sum(x) else x[1])
epil3$pred <- factor(epil3$pred,
   labels = c("base", "placebo", "drug"))

contrasts(epil3$pred) <- structure(contr.sdif(3),
    dimnames = list(NULL, c("placebo-base", "drug-placebo")))
summary(glm(y ~ pred + factor(subject) + offset(log(time)),
            family = poisson, data = epil3), cor = FALSE)

summary(glmmPQL(y ~ lbase*trt + lage + V4,
                random = ~ 1 | subject,
                family = poisson, data = epil))
summary(glmmPQL(y ~ pred, random = ~1 | subject,
                family = poisson, data = epil3))
\end{ExampleCode}
\end{Examples}

