I posted a confusing version of this question a few weeks ago, so here's an
attempt at clarifying:

In short, I'm trying to replicate an intercept-only (ie, no predictors)
model output of polr() using MCMCglmm as an initial step before building up
a more complex model family that includes varying intercept and slope
effects (ie, random, clustering, multi-level effects) and multiple
predictors.

To estimate the intercepts of an ordinal model with no predictors, lets say
it's specified as follows in polr():

m1 <- polr( as.ordered(h) ~ 1, data=d1, Hess=T, method="probit"),

where h is a 7 category response for household hunger.  Before building in
predictors, I wish to compare the cumulative link estimation of intercepts.
 My question is whether the following specification in MCMCglmm will
approximate the above polr fit:

prior <- list(R=list(V=1, fix=1))
m2 <- MCMCglmm( as.ordered(h)
~ 1, family="ordinal", pl=F, pr=F, data=d1,prior=prior).

The second part of this question regards how to interpret the MCMCglmm
cutpoints in terms of intercepts.  I've read the few posts from you and Ben
Bolker answering similar questions, but I'm not following the process.
 Specifically, I don't understand which value of a latent variable used in
the cumulative probability distribution function is used to compute the k-1
intercepts from the cutpoint outputs of MCMCglmm.

Apologies for my difficulty, but your help is much appreciated.  Thanks
very much, and good day.

jon

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