whittermore.boot {DCluster}

Generate Bootstrap Replicates of Whittermore's Statistic

Description

Generate bootstrap replicates of Whittermore's statistic by means of function boot from boot library. Notice that these functions should not be used separately but as argument statistic when calling function boot.

whittermore.boot is used to perform a non-parametric bootstrap

whittermore.pboot is used when using parametric bootstrap.

Usage

whittermore.boot(data, i, ...)
whittermore.pboot(...)

Arguments

data

A dataframe with the data as explained in DCluster.

i

Permutation generated by the non-parametric bootstrap procedure.

...

Additional arguments passed when performing a bootstrap.

Value

Both functions return the value of the statistic.

References

Whittermore, A. S. and Friend, N. and Byron, W. and Brown, J. R. and Holly, E. A. (1987). A test to detect clusters of disease. Biometrika 74, 631-635.

See Also

DCluster, boot, whittermore, whittermore.stat

Examples

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.whitt<-nb2listw(dlist, glist=dlist.d, style="C")

niter<-100

#Permutation model
wt.boot<-boot(sids, statistic=whittermore.boot, R=niter, listw=col.W.whitt,
	zero.policy=TRUE)
plot(wt.boot)#Display results

#Multinomial model
wt.mboot<-boot(sids, statistic=whittermore.pboot, sim="parametric", 
	ran.gen=multinom.sim,  R=niter,  listw=col.W.whitt, zero.policy=TRUE)
		
plot(wt.mboot)#Display results

#Poisson model
wt.pboot<-boot(sids, statistic=whittermore.pboot, sim="parametric", 
	ran.gen=poisson.sim,  R=niter,  listw=col.W.whitt, zero.policy=TRUE)
		
plot(wt.pboot)#Display results

#Poisson-Gamma model
wt.pgboot<-boot(sids, statistic=whittermore.pboot, sim="parametric", 
	ran.gen=negbin.sim, R=niter, listw=col.W.whitt, zero.policy=TRUE)
plot(wt.pgboot)#Display results