get.knclusters: Get Areas in a Cluster Detected with Kulldorff's Statistic

Description

When kn.iscluster is called from opgam to use Kulldorff's scan statistic for the detection of clusters of disease, get.knclusters can be used to get the areas included in each cluster. opgam only returns the cluster centres, size and related information but not the areas in the cluster.

Usage

get.knclusters(d, knresults)

Arguments

Data frame with the data, used in the call to opgam.

knresults

Data frame returned by a call to opgam.

Value

Returns a list with the same length as the number of rows in 'knresults'. Each element in the list is a vector of integers with the row indices of 'd' of the areas in the cluster. The order of the indices reflects the distance to the cluster centre.

References

Kulldorff, Martin and Nagarwalla, Neville (1995). Spatial Disease Clusters: Detection and Inference. Statistics in Medicine 14, 799-810.

Examples

Run this code

# 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, Population=nc.sids$BIR74, x=nc.sids$x, y=nc.sids$y)

#K&N's method over the centroids
mle<-calculate.mle(sids, model="poisson")
knresults<-opgam(data=sids, thegrid=sids[,c("x","y")], alpha=.05, 
	iscluster=kn.iscluster, fractpop=.15, R=99, model="poisson", mle=mle)

#Plot all centroids and significant ones in red
plot(sids$x, sids$y, main="Kulldorff and Nagarwalla's method")
points(knresults$x, knresults$y, col="red", pch=19)

#Plot first cluster with the highest likelihood ratio test in green
clusters<-get.knclusters(sids, knresults)
idx<-which.max(knresults$statistic)
points(sids$x[clusters[[idx]]], sids$y[clusters[[idx]]], col="green", pch=19)
# }