stone.boot: Generate Boostrap Replicates of Stone's Statistic

Description

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

stone.boot is used when performing a non-parametric bootstrap.

stone.pboot is used when performing a parametric bootstrap.

Usage

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

Arguments

data

A dataframe with all the data, as explained in the DCluster manual page.

Permutation created in non-parametric bootstrap.

...

Additional arguments passed to the functions.

Value

Both functions return the value of the statistic.

References

Stone, R. A. (1988). Investigating of excess environmental risks around putative sources: Statistical problems and a proposed test. Statistics in Medicine 7,649-660.

Examples

Run this code

# NOT RUN {
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)

niter<-100

#All Tests are performed around county 78.


#Permutation  model
st.perboot<-boot(sids, statistic=stone.boot, R=niter, region=78)
plot(st.perboot)#Display results

#Multinomial model
st.mboot<-boot(sids, statistic=stone.pboot, sim="parametric",
  ran.gen=multinom.sim,  R=niter, region=78)
plot(st.mboot)#Display results

#Poisson model
st.pboot<-boot(sids, statistic=stone.pboot, sim="parametric",
  ran.gen=poisson.sim,  R=niter, region=78)
plot(st.pboot)#Display results

#Poisson-Gamma model
st.pgboot<-boot(sids, statistic=stone.pboot, sim="parametric",
  ran.gen=negbin.sim, R=niter, region=78)
plot(st.pgboot)#Display results


# }