[R-sig-ME] error: model is nearly unidentifiable

[David Duffy]

> The experiment is a two choice habitat ("Fchoice": poor [0] vs rich
> [1]) for frogs under two-state treatments, lets say F > and C. Then
> I have as potential variables frog size ("SUL"), air temperature
> ("temp"), humidity ("hum") and date of experiment ("dateCont" recorded
> as continuous variable starting at day 1...). This is a repeated
> measure design as frogs were tested both in F and C trials (thus id is
> my random effect). I want to know if the choice is affected by treat,
> but also considering SUL, temp, humidity, and date in my model.

Should date rather be a factor? That would make the model even harder to fit, as you would
have too few data for the number of coefficients to be estimated.

Since you have exactly 2 obs per ID, then a generalized estimating equation should be pretty close,
and tends to be a bit more stable.  We can also check comparing the
glmmML fit - it is set up only for a simple RE model like yours, but the fitter often does a better job for those.

library(gee)
summary(gee(Fchoice ~ treat + SUL + temp + hum + dateCont, id=id, data=x,
                         corstr="exchangeable", family="binomial"))
library(glmmML)
summary(glmmML(Fchoice ~ treat + SUL + temp + hum + dateCont, cluster=id, data=x, family="binomial"))

               GEE (exchangeable r=0.08)         glmmML                   glmer
               Estimate Robust S.E.    Robust z     coef se(coef)       z Estimate Std. Error z value 
(Intercept) -0.88719918 10.40645085 -0.08525473 -1.13855 11.88440 -0.0958 -1.14557   11.89332  -0.096 
treatF      -1.15044245  0.64272611 -1.78994198 -1.21411  0.70458 -1.7232 -1.21572    0.70451  -1.726 
SUL         -0.10705958  0.07911966 -1.35313499 -0.11373  0.09252 -1.2293 -0.11384    0.09255  -1.230 
temp         0.25765994  0.35199301  0.73200300  0.28526  0.41123  0.6937  0.28591    0.41136   0.695 
hum         -0.01921368  0.03913854 -0.49091468 -0.02243  0.04662 -0.4812 -0.02252    0.04665  -0.483 
dateCont     0.02696366  0.04181628  0.64481255  0.02962  0.05015  0.5905  0.02965    0.05020   0.591 

Looks like glmer has found a solution close to that accepted by the other approaches. To me, suggests that glmer has worked OK.