Dear list,

I am using MCMCglmm to fit a model to metadata on learning in animals. The
response is the proportion of animals in a group that solve a task, the
random effects are the species and the study, and I have eight fixed
effects. The dataset is quite small, at 62 observations. The model
specification
is as follows:

# setting up the prior

p.var <- var(I(subdata2$n_solve/subdata2$group_size), na.rm=TRUE)

b <- 1e+08

B1_V <- diag(9)*b; B1_mu <- rep(0,9); B1_V; B1_mu

prior <- list(R=list(V=diag(1)*p.var*0.1, n=1, nu=0.002), G=list(G1=list(V
=diag(1)*p.var*0.6, n=Gn1), G2=list(V=diag(1)*p.var*0.2, n=Gn2)), B=list(V=
B1_V, mu=B1_mu))


# fitting the model

model <- MCMCglmm(cbind(n_solve, group_size-n_solve) ~ var1 + var2 + var3 +
var4 + var5 + var6 + var7 + var8,

family="multinomial2", random= ~animal + study,

start=list(QUASI=TRUE),

prior=prior, data=subdata2, pedigree=myreducedtree_3,

thin=100, nitt=150000, burnin=50000, verbose=F, pr=T, pl=T, nodes="TIPS")
The model converges and everything looks ok so I now want to do model
selection. I read in the help file for the MuMIn package that it now
accepts MCMCglmm objects with DIC as the rank function but I get an error
message when I run the following code:

model.dredge <- dredge(model, rank="DIC")

Error in dredge(model, rank = "DIC") :

  could not retrieve the call to 'global.model'

I will do model selection manually if needs be, but it would be good not to
have to. I also looked for a generic implementation of RJMCMC in R but
didn't come up with anything. Any pointers or ideas on why this might not
be working will be very much appreciated.

Thanks in advance,
Theoni

-- 
Theoni Photopoulou, Research Fellow
Centre for Social Learning and Cognitive Evolution
Bute Medical Building, University of St Andrews, Scotland KY16 9TS, UK
tel. +44 1334 467230 / skype. theoni_p / email. tp14@st-andrews.ac.uk

The University of St Andrews is a charity registered in Scotland : No
SC013532
" Be silly. Be honest. Be kind " Ralph Waldo Emerson

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