how reliable are inferences drawn from binomial models for small datasets fitted with lme4?

Hello,

but for m01 I think the model output:

####

print (m01 <- lmer(Response ~ Treatment + (Treatment - 1 | F1) +

(1  | F2), dat, family=binomial))
Generalized linear mixed model fit by the Laplace approximation
Formula: Response ~ Treatment + (Treatment - 1 | F1) + (1 | F2)
   Data: dat
   AIC   BIC logLik deviance
 87.58 107.1 -37.79    75.58
Random effects:
 Groups Name        Variance   Std.Dev.   Corr
 F2     (Intercept) 1.6467e-12 1.2832e-06
 F1     Treatment1  9.0240e+00 3.0040e+00
        Treatment2  3.4832e+00 1.8663e+00 -1.000
Number of obs: 190, groups: F2, 24; F1, 16
###


indicates that there is not enough information to estimate that  
model (Corr=-1.00) and that the random effects part should be  
simplified notwithstanding the logLik value? Sorry fpr adding a  
question and hope this is relevant.

Dear Luca,

Many thanks for your thoughts on this -- I did notice the correlation  
for F1 but in my thinking, this was sensible behavior by the model and  
didn't necessarily indicate a need to simplify the F1 random effects  
component.  Let me explain my reasoning.  Going back to the marginal  
mean responses per F1/Treatment combination:

 > with(dat,tapply(Response, list(F1, Treatment), mean))
            1  2
1  1.0000000  1
2  0.5714286  1
3  0.8888889  1
4  1.0000000 NA
5  0.0000000  1
6  1.0000000 NA
7  0.6666667  1
8  1.0000000  1
9  1.0000000  1
10 1.0000000  1
11 0.8181818  1
12 0.6000000  1
13 1.0000000 NA
14 1.0000000  1
15 1.0000000  1
16 1.0000000  1

it seems to me that the only way a random effect can contribute to the  
likelihood is by bringing down the linear predictor in Treatment for  
levels 2, 3, 7, 11, and 12 of F1.  So the random effects for these  
levels in Treatment 1 must be negative.  Now, given this constraint,  
what choice random effects for these levels in Treatment 2 maximize  
the likelihood?  Since there are never any Treatment 2 failures, these  
random effects should be as high as possible.  This leads to an  
inferred correlation of -1 for the random effects of F1.

The surprise to me isn't really that the random-effect correlation is  
so extreme, but rather that the model log-likelihoods seem to indicate  
that there's sufficient evidence to go with the model with more  
complex random-effects structure.  Which leads me to ask about how  
reliable log-likelihood differences for mixed-effects logit models on  
small datasets like this may be.

Many thanks once more!

Roger

--

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