[R-sig-ME] how reliable are inferences drawn from binomial modelsfor small datasets fitted with lme4?
rlevy at ling.ucsd.edu
Mon Jul 6 19:47:55 CEST 2009
On Jul 5, 2009, at 10:38 PM, David Duffy wrote:
> On Sun, 5 Jul 2009, Roger Levy wrote:
>> This post may be of interest in light of the recent discussion of
>> PQL versus Laplace-approximated likelihood. I'm facing an
>> interestingly challenging analysis of a relatively small (190-
>> observation) binary-response dataset with a single two-level
>> treatment and two crossed random factors (call them F1 and F2).
>> The question of current interest is whether I can infer a
>> difference in fixed effect of treatment ... [SNIP]
> Although F1 has an effect, F2 doesn't seem as impressive:
> For Response, Tarone score test for extrabinomial variance gives
> F1 3.86 (P=0.0493), F2 0.54 (P=0.4616).
> So it seems reasonable just to ignore F2. Then the conditional
> logistic regression stratifying on F1 is nicely significant:
> clogit(Response ~ Treatment + strata(F1), method="exact", data = x)
> coef exp(coef) se(coef) z p
> Treatment2 2.73 15.3 1.10 2.49 0.013
> Likelihood ratio test=10.9 on 1 df, p=0.000957 n= 190
> (and equivalent score test 9.054, P=0.0026).
> The conditional logistic should be fairly robust, and at least
> gives some kind of benchmark for other models.
Many thanks for your input -- I'm not familiar with the Tarone score
test (looking around -- is this Tarone 1979, Biometrika?) or with
stratified conditional logistic models. Could you perhaps give me
pointers to R code for the former, and references for both?
Best & many thanks again.
Roger Levy Email: rlevy at ling.ucsd.edu
Assistant Professor Phone: 858-534-7219
Department of Linguistics Fax: 858-534-4789
UC San Diego Web: http://ling.ucsd.edu/~rlevy
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