Dear Virgilio, Many thanks - yes that is helpful! However in my own dataset I am finding those values lead to an oversmoothed model - i.e. the output is very uniform - practically flat across all areas. But I know from BUGS smoothing and also from cluster analysis that there should be areas of variation. What is if that controls the "degree of smoothing" for want of a better term!
Many thanks, James ________________________________________ From: VIRGILIO GOMEZ RUBIO [virgilio.go...@uclm.es] Sent: 05 May 2014 23:40 To: James Rooney Cc: r-sig-geo@r-project.org Subject: Re: [R-sig-Geo] BYM model in R-INLA - how to specify priors individually Dear James, You should be able to set the prior of your random effects using hyper= in the call to f(). In your particular case, you can try: #Create areas IDs to match the values in nc.adj nc.sids$ID<-1:100 nc.sids$ID2<-1:100 hyper.besag <-list(prec=list(prior="loggamma", params=c(.1, .1))) hyper.iid<-list(prec=list(prior="loggamma", params=c(.001, .001))) #Besag model with random spatial effect (i.e. BYM model) m2<-inla(SID74~NWPROP+ f(nc.sids$ID, model="besag", graph="nc.adj", hyper=hyper.besag)+ f(nc.sids$ID2, model="iid", hyper=hyper.iid), family="poisson", E=nc.sids$EXP, data=as.data.frame(nc.sids), control.predictor=list(compute=TRUE)) Note that I have defined two different indices. You can probably use model="bym" to simplify the formula in your model. Also, run inla.models()$latent$bym to know the default definitions of priors of the hyperparameters. Hope this helps. Virgilio _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo
