[R-sig-ME] trouble specifying cumulative-link mixed model

[Dan McCloy]

I'm having a little trouble figuring out how to set up my model. The data
are about pronunciation of vowel sounds in speech: 21556 observations of an
ordered categorical outcome ("none", "devoiced", "deleted"). I *think* what
I want is a cumulative-link mixed model, which I can get with the ordinal
package's clmm() function. However, the outcome is very unbalanced (which
may or may not be the source of my problems):

table(cleandata$reduction)
##     none devoiced  deleted
##    20776      360      420

I have random effects for nuisance variables speaker (n=6) and word
(n=1204). Observations within these two are fairly well-balanced (i.e.,
there is not much missing data; the vast majority of speaker-word pairings
have exactly 3 observations):

table(with(cleandata, table(word, speaker)))
##    0    1    2    3
##   14   11   52 7147

What I really care about is preceding consonant type (6 levels), following
consonant type (also 6 levels), and whether following consonant is itself
followed by another consonant ("coda", binary). Another predictor we expect
to be important is whether or not preceding and/or following consonant are
"aspirated", which is a property of only 2 of the 6 levels. The identity of
the vowel itself (10 levels) is not of primary interest but definitely
ought to be included; I am open to including it as a random effect, but
slightly prefer being able to see estimates for each level of the vowel if
possible.

What I'm struggling with is how to specify the fixed effects. If I include
everything (precedingCons + followingCons * coda + aspirated + random
effects), I get "numerically singular Hessian" problems, regardless of
whether I specify preceding / following consonant as factors, or set up
binary variables like "preceding.stop", "following.stop",
"preceding.fricative", etc. (which I think are equivalent anyway, since the
factor was treatment-coded).  I can get the model to converge if I do the
binary variables method but only include 3 of the 6 levels for preceding
and following consonant (plus "aspirated" & random effects).

My questions:
1. is CLMM the right modeling choice?  In principle I can collapse
"reduction" to binary and do a logit-link glmm, by collapsing "devoiced"
and "deleted" into one category, but really don't want to have to resort to
that.
2. is the imbalance in my outcome causing the problems with the modeling?
Do I need some sort of zero-inflation model (which I've heard talked about
on this list, but don't really understand yet)?
3. any suggestions for how to specify the fixed effects (i.e., factor
coding)?

Some additional tables showing distribution of the response levels are
included in a GitHub gist here:
https://gist.github.com/drammock/bf7a6d634bbd179b328f

thanks,
-- dan

Daniel McCloy
http://dan.mccloy.info/
Postdoctoral Research Fellow
Institute for Learning and Brain Sciences
University of Washington

	[[alternative HTML version deleted]]