MCMCglmm random intercept/slope model and credible intervals
Hi, There is a bug in predict.MCMCglmm for random regression models. I've corrected it in the unreleased version, but you can change it easy enough: Change M[,which(rm.v),]<-0 on L65 to M[,which(rm.v)]<-0 Also, this model will not give sensible results. You need to use something like: R = list(V = 1, fix=1) in the prior for the residual variance (it cannot be estimated from the data with categorical data) Also, G = list(G1 = list(V = diag(2), nu = 6))) is pretty informative, unless you have a lot of data and replication at the right level. Cheers, Jarrod Quoting Maya <maiski at maiski.net> on Mon, 7 Oct 2013 12:52:06 +0000 (UTC):
Hello all, i have the following problem. I want to fit a GLMM with a numerical fixed effect, a factorial random effect, containing subject ids and a binary outcome. My code is: fam='categorical' priors1<-list(R = list(V = 1, nu = 0.002), G = list(G1 = list(V = diag(2), nu = 6))) modelGlmm1<- MCMCglmm(toModel ~ fixedEffect, random=~us(1+fixedEffect):randomEffect, data=data, verbose=FALSE, family=fam, pr=TRUE, pl=TRUE, nitt=nittM, thin=thinM, burnin=burninM, prior=priors1) mp1 = predict(modelGlmm1, interval = 'confidence') But as I try to get the credible intervals I get the following error: Error in M[, which(rm.v), ] <- 0 : incorrect number of subscripts. Could anybody give me some hints or enlighten me on the subject? I cannot figure it out by myself :/ I have also another question: How are the credible intervals constructed: pointwise or simultaniously? Thanks in advance! Greets, Maya
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