[R-sig-ME] Predict and plot lines estimated from MCMCglmm?

[Jesse Delia]

Dear list,

I am a grad student and am trying to plot the results of a comparative
field experiment. I've been struggling to figure out how predict and plot
lines estimated using MCMCglmm. I've read the course notes, spent several
days googling, and have been looking for downloadable script from
publications, with no luck. Does anyone know how to (or could point me in
the direction for an example) to plot lines for each of a 2-level predictor
after accounting for random effects using MCMCglmm?

I have pasted my script below, for which I am trying to evaluate how
evolutionary changes in parental care alter offspring survival. Ideally,
I'd like to plot a line for each type of 'careduration' (binary predictor)
over the raw data after accounting for random effects of phylogeny and
within species variation:

Prior<- list(R=list(V= 1e 10,nu=-1), G=list(G1=list(V=1,nu=1,alpha.mu=0,
alpha.V=25^2), G2=list(V=1,nu=1,alpha.mu=0,alpha.V=25^2)))

Model1<-MCMCglmm(cbind(mortality, clutchsize-mortality) ~careduration*
raindpo3, random=~species+animal, family ="multinomial2", ginverse=list(
animal=inv.phylo$Ainv), prior=prior1, data=data, nitt=3000000, burnin=10
00000, thin = 500, pr=TRUE)
One additional question: my response is a proportional estimate of egg
clutch mortality. There are lots of zeros, as many clutches did not
experience any mortality -- can "multinomial2" handle proportional data
with lots of zeros? I get similar results with the above model as I do
using a beta-binomial model using glmmADMB (and will present both models in
the publication).

Thanks for your time,

Jesse

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