# NOT RUN {
library(boot)
library(spdep)
data(nc.sids)
sids<-data.frame(Observed=nc.sids$SID74)
sids<-cbind(sids, Expected=nc.sids$BIR74*sum(nc.sids$SID74)/sum(nc.sids$BIR74) )
sids<-cbind(sids, x=nc.sids$x, y=nc.sids$y)
#Calculate neighbours based on distance
coords<-as.matrix(sids[,c("x", "y")])
dlist<-dnearneigh(coords, 0, Inf)
dlist<-include.self(dlist)
dlist.d<-nbdists(dlist, coords)
#Calculate weights. They are globally standardised but it doesn't
#change significance.
col.W.tango<-nb2listw(dlist, glist=lapply(dlist.d, function(x) {exp(-x)}),
style="C")
niter<-100
#Permutation model
tn.boot<-boot(sids, statistic=tango.boot, R=niter, listw=col.W.tango,
zero.policy=TRUE)
plot(tn.boot)#Display results
#Multinomial model
tn.mboot<-boot(sids, statistic=tango.pboot, sim="parametric",
ran.gen=multinom.sim, R=niter, listw=col.W.tango, zero.policy=TRUE)
plot(tn.mboot)#Display results
#Poisson model
tn.pboot<-boot(sids, statistic=tango.pboot, sim="parametric",
ran.gen=poisson.sim, R=niter, listw=col.W.tango, zero.policy=TRUE)
plot(tn.pboot)#Display results
#Poisson-Gamma model
tn.pgboot<-boot(sids, statistic=tango.pboot, sim="parametric",
ran.gen=negbin.sim, R=niter, listw=col.W.tango, zero.policy=TRUE)
plot(tn.pgboot)#Display results
# }
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