1\name{besagnewell}
2
3\alias{besagnewell}
4
5\title{Besag and Newell's Statistic for Spatial Clustering}
6
7\description{
8Besag & Newell's statistic looks for clusters of size \emph{k}, i. e., where
9the number of observed cases is \emph{k}. At every area where a case has
10appeared, the number of neighbouring regions needed to reach $k$ cases is
11calculated.  If this number is too small, that is, too many observed cases in
12just a few regions with low expected cases, then it is marked as a cluster.
13}
14
15
16\seealso{
17DCluster, besagnewell.stat, besagnewell.boot, besagnewell.pboot, bn.iscluster
18}
19
20\references{
21Besag, J. and Newell, J.(1991). The detection of clusters in rare diseases.
22Journal of the Royal Statistical Society A  154, 143-155.
23}
24
25\examples{
26#B&N must use the centroids as grid.
27#The size of teh cluster is 20.
28#100  bootstrap simulations are performed
29#Poisson is the model used in the bootstrap simulations to generate the
30#observations.
31#Signifiance level is 0'05, even though multiple tests are made.
32
33library(boot)
34library(spdep)
35
36data(nc.sids)
37
38sids<-data.frame(Observed=nc.sids$SID74)
39sids<-cbind(sids, Expected=nc.sids$BIR74*sum(nc.sids$SID74)/sum(nc.sids$BIR74))
40sids<-cbind(sids, x=nc.sids$x, y=nc.sids$y)
41
42bnresults<-opgam(sids, thegrid=sids[,c("x","y")], alpha=.05,
43	iscluster=bn.iscluster, set.idxorder=TRUE, k=20, model="poisson",
44	R=100, mle=calculate.mle(sids) )
45
46#Plot all the centroids
47plot(sids$x, sids$y)
48
49#Plot signifiant centroids in red
50points(bnresults$x, bnresults$y, col="red", pch=19)
51}
52
53\keyword{spatial}
54