stone.boot: Generate Boostrap Replicates of Stone's Statistic In DCluster: Functions for the Detection of Spatial Clusters of Diseases

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

 ```1 2``` ```stone.boot(data, i, ...) stone.pboot(...) ```

Arguments

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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31``` ```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 ```