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# hilbe.NBR2.F9.5.r
# From Hilbe, Negative Binomial regression, 2nd ed, Cambridge Univ. Press
# observed vs predicted number of afffairs
# Figure 9.5 user may amend as required for own model
#
load("c://source/mdvis.RData")
poimd <- glm(numvisit ~ reform+ badh+age+educ+loginc,family=poisson, data=mdvis)
mu <- fitted.values(poimd)
p <- NULL
avgp <- NULL
for (i in 0:20) {
p[[i+1]] <- exp(-mu)*(mu^i)/factorial(i)
avgp[i+1] <- mean(p[[i+1]])
}
nCases <- dim(mdvis)
n<- NULL
propObs<- NULL
probFit<- NULL
yFitMean<- NULL
for (i in 0:20) { #possible values for naffairs
bLos<- mdvis$numvisit==i #selector for naffairs=i
n[i+1]<- sum(bLos) #number for naffairs=i
propObs[i+1]<- n[i+1]/nCases[1] #observed proportion for naffairs=i
}
Diff <- propObs*100 - avgp*100
data.frame(LOS=0:20, ObsProp=propObs*100, avgp*100, Diff)
plot(0:20, avgp, type="b", xlim=c(0,20),
main = "Observed vs Predicted Visits",
xlab = "Number Visits", ylab = "Probability of Visits")
lines(0:20, propObs, type = "b", pch = 2)
legend("topright", legend = c("Predicted Visits","Observed Visits"),
lty = c(1,1), pch = c(1,2))
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