[R-sig-Geo] DCluster questions
James Rooney
ROONEYJ4 at tcd.ie
Fri Sep 6 17:15:32 CEST 2013
Hi all,
I'm wondering if I can get a little advice on using DCluster.
I have produced a map of areal incidence rates and I'd like to try and detect clusters.
I have also implemented Bayesian smoothing and have therefore pre and post smoothing maps.
I have some code as follow for detecting clusters using the Kuldorff-Nagarwalla analysis (code based on Fig11.18 in the ASDAR book and this useful question and answer: http://r-sig-geo.2731867.n2.nabble.com/Strange-results-in-DCluster-package-td7326832.html )
# get centroids of polygons
Areas$x<-coordinates(Areas)[,1]
Areas$y<-coordinates(Areas)[,2]
#Create the data frame demanded by DCluster
sa<-data.frame(Observed=Areas$obs)
sa<-cbind(sa, Expected=mean(Areas$exp))
sa<-cbind(sa, x=Areas$x, y=Areas$y)
sa$Observed<-as.numeric(sa$Observed)
#Kuldorff-Nagarwalla analysis
mle<-calculate.mle(sa, model="poisson")
knres<-opgam(data=sa, thegrid=sa[,c("x", "y")], alpha=.1, R=20,
iscluster=kn.iscluster, fractpop=.10, model="poisson", mle=mle, log.v=TRUE)
This seems to work ok however there are a few things I'm confused on:
1) Should I be using this algorithm on my incidence rates pre or post Bayesian smoothing ? I'm thinking that the mle expression above includes a smooth (do I understand that correctly ?) - but I' prefer to utilise my hard-won Bayesian smooth if possible.
2) Will opgam/kn.iscluster only detect "hotspots" or will they also detect "coldspots" i.e. areas of statistically unlikely lower incidence rates ?
3) I'm not familiar with bootstrapping - how many bootstraps should I be running and why (i.e. - what should I set R to) ?
4) How do I decide what the correct value for fractpop is ? I initially had it set to .25 and I was getting cluster of 50% of my cases which made no sense.
5) Is there any correction for multiple testing in the opgam() command ? I have over 3000 areas - do I need to set a very low alpha ?
I would be grateful for any enlightenment anyone may have to pass on :)
Many thanks,
James
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