[R-sig-ME] two questions about clmm

[Malcolm Fairbrother]

Dear Rune (and list),

I've been making use of clmm, and have two (potentially over-ambitious) questions. If Rune or anyone else can offer any insights about either, that would be much appreciated.

First, I noticed something intriguing in the ordinal package documentation: the "## Binomial example with data from the lme4-package example", for clmm2. This suggests a way of shortening large datasets (with one Bernoulli trial per row) into shorter ones (with counts on each row, representing many trials), rather like "glmer(cbind(incidence, size - incidence)…" does for lme4. This obviously speeds up model fitting tremendously. However, I was wondering if there's any way to do this for outcomes with more than two levels (i.e., not just binomial, but multinomial)? This may not be possible or even make sense, but I thought I'd ask, given the example that was in the documentation.

Second, I often use "simulate" with fitted mer objects (from lme4), to get confidence intervals for quantities of interest. (Using "refit" and "simulate" together is fast.) Is there any similar way to simulate and refit fitted clmm objects?

Many thanks,
Malcolm


Dr Malcolm Fairbrother
School of Geographical Sciences
University of Bristol