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\name{LongitudinalOverdispersedCounts}
\alias{LongitudinalOverdispersedCounts}
\alias{longData}
\alias{wideData}
\docType{data}
\title{
Longitudinal, Overdispersed Count Data
}
\description{
Four-timepoint longitudinal data generated from an arbitrary Monte Carlo simulation, for 1000 simulees. The response variable is a discrete count variable. There are three time-invariant covariates. The data are available in both "wide" and "long" format.
}
\usage{data("LongitudinalOverdispersedCounts")}
\format{
The "long" format dataframe, \code{longData}, has 4000 rows and the following variables (columns):
\enumerate{
\item{\code{id}: Factor; simulee ID code.}
\item{\code{tiem}: Numeric; represents the time metric, wave of assessment.}
\item{\code{x1}: Numeric; time-invariant covariate.}
\item{\code{x2}: Numeric; time-invariant covariate.}
\item{\code{x3}: Numeric; time-invariant covariate.}
\item{\code{y}: Numeric; the response ("dependent") variable.}
}
The "wide" format dataset, \code{wideData}, is a numeric 1000x12 matrix containing the following variables (columns):
\enumerate{
\item{\code{id}: Simulee ID code.}
\item{\code{x1}: Time-invariant covariate.}
\item{\code{x3}: Time-invariant covariate.}
\item{\code{x3}: Time-invariant covariate.}
\item{\code{y0}: Response at initial wave of assessment.}
\item{\code{y1}: Response at first follow-up.}
\item{\code{y2}: Response at second follow-up.}
\item{\code{y3}: Response at third follow-up.}
\item{\code{t0}: Time variable at initial wave of assessment (in this case, 0).}
\item{\code{t1}: Time variable at first follow-up (in this case, 1).}
\item{\code{t2}: Time variable at second follow-up (in this case, 2).}
\item{\code{t3}: Time variable at third follow-up (in this case, 3).}
}
}
\examples{
data(LongitudinalOverdispersedCounts)
head(wideData)
str(longData)
#Let's try ordinary least-squares (OLS) regression:
olsmod <- lm(y~tiem+x1+x2+x3, data=longData)
#We will see in the diagnostic plots that the residuals are poorly approximated by normality,
#and are heteroskedastic. We also know that the residuals are not independent of one another,
#because we have repeated-measures data:
plot(olsmod)
#In the summary, it looks like all of the regression coefficients are significantly different
#from zero, but we know that because the assumptions of OLS regression are violated that
#we should not trust its results:
summary(olsmod)
#Let's try a generalized linear model (GLM). We'll use the quasi-Poisson quasilikelihood
#function to see how well the y variable is approximated by a Poisson distribution
#(conditional on time and covariates):
glm.mod <- glm(y~tiem+x1+x2+x3, data=longData, family="quasipoisson")
#The estimate of the dispersion parameter should be about 1.0 if the data are
#conditionally Poisson. We can see that it is actually greater than 2,
#indicating overdispersion:
summary(glm.mod)
}
\keyword{datasets}
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