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library(boot)
data(nuclear)
# take column 1, 2, 5, 7, 8, 10, and 11 of nuclear data frame.
# column 1 -> cost, column 2 -> date
# column 5 -> cap, column 7 -> ne
# column 8 -> ct, column 10 -> cum.n
# column 11 -> pt
nuke<-nuclear[,c(1,2,5,7,8,10,11)]
# glm is used to fit generalized linear models
# formula for this problem set is "log(cost)~date+log(cap)+ne+ct+log(cum.n)+pt
# data is from nuke, the data frame we constructed earlier
nuke.lm<-glm(log(cost)~date+log(cap)+ne+ct+log(cum.n)+pt, data=nuke)
# more generalized linear model diagnostics
nuke.diag<-glm.diag(nuke.lm)
nuke.res<-nuke.diag$res*nuke.diag$sd
nuke.res<-nuke.res-mean(nuke.res)
# construct a data frame based on the diagnostics
nuke.data<-data.frame(nuke,resid=nuke.res,fit=fitted(nuke.lm))
# create a data frame with the following variables:
# cost=1, date=73.00, cap=886, ne=0, ct=0, cum.n=11, pt=1
# these values are used in nuke.fun defined later
new.data<-data.frame(cost=1,date=73.00, cap=886, ne=0, ct=0, cum.n=11, pt=1)
# nuke.lm is the model object for which prediction is desired.
# new.data contain variables used in the nuke.lm model
new.fit<-predict(nuke.lm, new.data)
nuke.fun<-function(dat, inds, i.pred, fit.pred, x.pred) {
assign(".inds", inds, envir=.GlobalEnv)
lm.b<-glm(fit+resid[.inds] ~date+log(cap)+ne+ct+log(cum.n)+pt, data=dat)
pred.b <-predict(lm.b, x.pred)
remove(".inds", envir=.GlobalEnv)
c(coef(lm.b), pred.b-(fit.pred+dat$resid[i.pred]))
}
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