[R-sig-ME] mixed model with non-continuous numeric response

[Robert Kushler]

Daniel,

I think one of the ordinal response models discussed in sections 2-4
of chapter 7 of Agresti's "Categorical Data Analysis" (second edition)
would be appropriate here.  Section 12.4 briefly discusses adding random
effects to such models.  It's not clear to me how to accomplish this
using the lme4 package (but of course "This is R.  There is no if. ...").

A fallback strategy would be to run various mixed-effect binary response
models (using different cut points on the ordinal scale) separately, and see
how consistent the results are.

You may find the "lrm" function in Frank Harrell's Design package useful
for some of the alternatives to the cumulative logit model (though it doesn't
handle random effects).

Regards,   Rob Kushler


Daniel Ezra Johnson wrote:
> I don't think this is count data, is it???
> 
> On Mon, Dec 22, 2008 at 12:40 PM, Reinhold Kliegl
> <reinhold.kliegl at gmail.com> wrote:
>> ( ...,  family="poisson")  is the most used option for count data
>>
>> Reinhold Kliegl
>>
>> On Mon, Dec 22, 2008 at 12:54 PM, Daniel Ezra Johnson
>> <danielezrajohnson at gmail.com> wrote:
>>> Dear all,
>>>
>>> I have survey results where the response is 1, 2, 3, or 4. These can
>>> be thought of as equally-spaced points on a scale, I don't have a
>>> problem with that. (They're actually more like "not at all", "some",
>>> "mostly", "totally"; the subject is judging a stimulus.)
>>>
>>> I want to model crossed random effects for Subject and Item. Am I way
>>> off base in modeling this data with a lmer(family="gaussian") model? I
>>> know it's not perfect, but is it really bad? If so, what could I do
>>> instead? (The error certainly wouldn't be binomial, right?)
>>>
>>> Thanks,
>>> Daniel
>>>
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> 
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