[R-sig-ME] lmer, problem with dependent variables with a binomial distribution

[Liliana Martinez]

Hi,
 
I am trying to use the lmer function from the lme4 package in R 2.8.0. to fit a generalized mixed-effects model for a dependent variable with a binomial distribution (for more info on my experiment, look below). However, I encounter some problems due to the nature of my data. 
 
First, some background info without which it is not possible to explain my problem:
I am a linguist trying to analyze the geometry of path encoded by some verbs of motion. In my experiment the subjects drew sketches of the trajectory of a moving car, prompted by stimulus sentences containing certain verbs. The sketches then were categorized according to the extension of the curve drawn by the subject. Thus, my dependent variable was originally intended to be a factor – curve extension – with 6 levels (45, 90, 135, 180, 270 and 360 degrees). I preferred a factor instead of a numeric variable, because it was not always possible to assign a precise angular value, and the only solution was to assign the sketch to one of several cognitively motivated categories. When I sought advice for appropriate means of analysis, it became clear that no known (to my circle of contacts) means exist to analyze this type of data. Therefore I had to restructure my original dependent variable into 6 dependent variables with a binomial distribution (occur
 – no-occur) – one for each of the curve-extension categories. 
 
The problem:
For some of these binary dependent variables some values of the predictor do not elicit any occurrences, e.g., none of the subjects drew a 360-degree curve when the stimulus contained verb A, and many subjects drew loads of 360-degree curves when the stimulus contained verb B, yet the model does not find any significant difference between verb A and verb B (and there is a tremendously big value for Standard Error. 
 
[13:43:20] Liliana Martinez sier : Fixed effects:
                     Estimate Std. Error z value Pr(>|z|)    
(Intercept)            1.5476     0.4260   3.633 0.000280 ***
v_prepobikaliam_ze    -0.1370     0.3691  -0.371 0.710548    
v_prepzaobikaliam_ze  -4.9991     0.5346  -9.352  < 2e-16 ***
v_prepzavivam_kum    -19.6208   525.3207  -0.037 0.970206    
v_prepzavivam_pokrai  -6.6705     0.9123  -7.312 2.64e-13 ***
 
Yet, the difference is striking, and is obvious both in the figures (see attachment) and in crosstab statistics. How can I document and report the difference between the predictor values in such cases? If this method of analysis cannot provide a good solution, can you suggest alternative ways of analysis? 
 
 
best regards
 
Liliana


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