Hello everyone,
I am looking for a R code to simulate an spatial distribution of multinomial
classes (like colors classes) in a map.
The problem is find a a multivariate distribution of the color classes
(covariate: map colors) and a continous variable (like rain or ph) to study
different kriging (cokriging) methodologies to make cross-validation
predictions in order to test the effect on prediction which helps solve some
of the data I now encounter.
Does somebody know, what to do in such a situation?
Does somebody know, what to do treat multinomial classes covariate in
spacetime models using kriging?
I found this (DCluster package), but I am not sure if my way is wrong:
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library(DCluster)
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$y)
#K&N's method over the centroids
mle<-calculate.mle(sids, model="poisson")
knresults<-opgam(data=sids, thegrid=sids[,c("x","y")], alpha=.05,
+ iscluster=kn.iscluster, fractpop=.5, R=100, model="multinomial", mle=mle)
#Plot all centroids and significant ones in red
plot(sids$x, sids$y, main="Kulldorff and Nagarwalla's method")
points(knresults$x, knresults$y, col="red", pch=19)
dev.off()
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Thank you very much in advance! Any tips are appreciated much!
cheers,
Toni Monleón
UB
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