Hi,

If you are willing to move outside R,
mixed effects multinomial models can be fit with the
commercial software AD Model Builder:

http://otter-rsch.com/admbre/admbre.html

Ordered and unordered categorical responses can
be handled, and there is no limit on the number
of nesting levels (in principle). For an example see:

http://otter-rsch.com/admbre/examples/socatt/socatt.html

It is easy to call an AD Model Builder program from R:

http://otter-rsch.com/admbre/examples/glmmadmb/glmmADMB.html

There obviously are limitations on the size of the model,
and I will be happy to clarify if your problem at hand is within reach.

hans


>Hello!
>
>I and several of my colleagues are wondering whether it is possible to
>use any of the methods of lme4 as it exists now to fit a mixed effects
>model with a response variable drawn from a multinomial distribution.
>glm does not include a multinomial family, so if it is possible to
>accomplish this I'm not sure how to do so.  Packages that do allow
>multinomial response variables (like multinomRob) don't seem to allow
>for the inclusion of random effects.
>
>If it is not currently possible to fit a data set with a categorical
>dependent variable with more than two levels, might this be possible in
>the forthcoming update to lme4?
>
>Finally, if it isn't possible now and won't be in the next version of
>the package either, would someone be willing to explain the conceptual
>or technical difficulties associated with including a response variable
>from a multinomial distribution in a mixed effects model?
>
>Thanks for any help,
>/au

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