Nothing
# hilbe.NBR2.F6.2alt.r
# Table 6.15 + added code
# Table of Poisson observed vs predicted mean counts and diffrence;
# graphic of observed vs predicted counts
# From Hilbe, Negative Binomial regression, 2nd ed, Cambridge Univ. Press
# Table 6.15; Figure 6.2 Alternative
# User to amend default dataset, response, and number of counts
#
load("c://source/medpar.RData")
mdpar <- glm(los ~ hmo+white+type2+type3,family=poisson, data=medpar)
mu <- fitted.values(mdpar)
p <- NULL
avgp <- NULL
for (i in 0:25) {
p[[i+1]] <- exp(-mu)*(mu^i)/factorial(i)
avgp[i+1] <- mean(p[[i+1]])
}
nCases <- dim(medpar)
n<- NULL
propObs<- NULL
probFit<- NULL
yFitMean<- NULL
for (i in 0:25) { #possible values for LOS
bLos<- medpar$los==i #selector for los=i
n[i+1]<- sum(bLos) #number for los=i
propObs[i+1]<- n[i+1]/nCases[1] #observed proportion for LOS=i
}
Diff <- propObs*100 - avgp*100
data.frame(LOS=0:25, ObsProp=propObs*100, avgp*100, Diff)
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.