Besag & Newell's statistic looks for clusters of size k, i. e., where the number of observed cases is k. At every area where a case has appeared, the number of neighbouring regions needed to reach $k$ cases is calculated. If this number is too small, that is, too many observed cases in just a few regions with low expected cases, then it is marked as a cluster.
Besag, J. and Newell, J.(1991). The detection of clusters in rare diseases. Journal of the Royal Statistical Society A 154, 143-155.
DCluster, besagnewell.stat, besagnewell.boot, besagnewell.pboot, bn.iscluster
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#B&N must use the centroids as grid. #The size of teh cluster is 20. #100 bootstrap simulations are performed #Poisson is the model used in the bootstrap simulations to generate the #observations. #Signifiance level is 0'05, even though multiple tests are made. 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) bnresults<-opgam(sids, thegrid=sids[,c("x","y")], alpha=.05, iscluster=bn.iscluster, set.idxorder=TRUE, k=20, model="poisson", R=100, mle=calculate.mle(sids) ) #Plot all the centroids plot(sids$x, sids$y) #Plot signifiant centroids in red points(bnresults$x, bnresults$y, col="red", pch=19)
Loading required package: boot Loading required package: spdep Loading required package: sp Loading required package: Matrix Loading required package: MASS
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