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# hilbe.NBR2.F6.4.r
# Conditional effects Poisson plot with user specified mean values
# From Hilbe, Negative Binomial regression, 2nd ed, Cambridge Univ. Press
# Table 6.17; Figure 6.4
# User to amend data, model variables, and effects for graphing
load("c://source/rwm5yr.RData")
eppoi <- glm(docvis ~ outwork+age+female+married+edlevel2+edlevel3+ edlevel4, family=poisson, data=rwm5yr)
rest <- eppoi$coef[4]*mean(rwm5yr$female) + eppoi$coef[5]*mean(rwm5yr$married) +
eppoi$coef[6]*mean(rwm5yr$edlevel2) + eppoi$coef[7]*mean(rwm5yr$edlevel3) +
eppoi$coef[8]*mean(rwm5yr$edlevel4)
out0 <- eppoi$coef[1] + eppoi$coef[3]*rwm5yr$age + rest
out1 <- eppoi$coef[1] + eppoi$coef[2]*1 + eppoi$coef[3]*rwm5yr$age + rest
eout1 <- exp(out1)
eout0 <- exp(out0)
matplot(cbind(rwm5yr$age, rwm5yr$age), cbind(eout0, eout1),
pch=1:2, col=1:2, xlab='Count', ylab='Frequency?')
matplot(cbind(rwm5yr$age, rwm5yr$age), cbind(eout0, eout1), type='l',
lty=1:2, col=1:2, xlab='Doctor visits', ylab='Frequency')
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