Dear List Members,
I'm wondering if you have experience (if it's passible in the first place)
in fitting a mixed model with multiple multinomial responses in MCMCglmm. I
was trying to fit such model but I bumped into the truble of what the
"trait" dummy variable does actually index in such a model?
In a "univariate" multinomial model (which actually already is
multivariate) like the one below the trait variable indexes single odds
ratios for 2nd/3rd... category against the 1st category. Is it possible to
extend this into a situation with more than one multinomial variables?
cheers,
Szymek
require(MCMCglmm)
mydata <- data.frame(response1 = c(sample(c("a","b","c"), replace=T, size =
100)),
covariate = rnorm(100), random =
gl(20,5,labels=letters[1:20]))
K <- length(levels(mydata$response1))
I <- diag(K-1)
J <- matrix(0,K-1,K-1) + 1
IJ <- 1/K*(I+J)
size=2
prior <- list(R=list(V=IJ, fix=1),
G=list(G1=list(V=IJ, nu=K-1+0.002)),
B=list(mu=rep(0,size*(K-1)), V=kronecker(IJ,
diag(size))*(1.7+pi^2/3)))
model <- MCMCglmm(response1~trait*covariate-1, random=~us(trait):random,
rcov=~us(trait):units,
family="categorical", data=mydata, prior=prior)
#######################################
## now what to do with the 2nd response variable?
mydata$response2 <- as.factor(c(sample(c("w","x","y","z"), replace=T, size
= 100)))
--
*Dr Szymon Drobniak*
Anthropological Institute and Museum
Office Y42-K-66
University of Z?rich - Irchel
Winterthurerstrasse 190
CH-8057 Z?rich, Switzerland
[[alternative HTML version deleted]]