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# mysim.r
# Table 6.5 : Hilbe, Negative Binomial Regression, 2 ed, Cambridge Univ Press
# Monte Carlo simulation - Poisson
mysim <- function()
{
nobs <- 50000
x1 <-runif(nobs)
x2 <-runif(nobs)
py <- rpois(nobs, exp(2 + .75*x1 - 1.25*x2))
poi <- glm(py ~ x1 + x2, family=poisson)
pr <- sum(residuals(poi, type="pearson")^2)
prdisp <- pr/poi$df.residual
beta <- poi$coef
list(beta,prdisp)
}
B <- replicate(100, mysim())
apply(matrix(unlist(B[1,]),3,100),1,mean)
mean(unlist(B[2,]))
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