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 a major problem: How is it possible to find the general test statistic and see the relative importance of the predictors? The methods which I found described in Baayen (2008). Analyzing Linguistic Data: A Practical Introduction to Statistics Using Ror on the net did not work out. Here is what I got: 
 
> prec0_va2.lmer
Generalized linear mixed model fit by the Laplace approximation 
Formula: prec_0 ~ (verb + agent)^2 + (1 | subject) 
   Data: risuvane1_binom_tolmer 
   AIC   BIC logLik deviance
 559.7 590.5 -272.8    545.7
Random effects:
 Groups  Name        Variance Std.Dev.
 subject (Intercept) 1.9975   1.4133  
Number of obs: 600, groups: subject, 30
 
Fixed effects:
                         Estimate Std. Error z value Pr(>|z|)    
(Intercept)                3.3120     0.5757   5.753 8.75e-09 ***
verbzaobikaliam           -4.2031     0.5530  -7.601 2.94e-14 ***
verbzavivam               -4.2508     0.5113  -8.313  < 2e-16 ***
agentveh                  -2.7286     0.7219  -3.780 0.000157 ***
verbzaobikaliam:agentveh   1.0255     0.7440   1.378 0.168058    
verbzavivam:agentveh       1.9629     0.6217   3.158 0.001591 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
 
Correlation of Fixed Effects:
            (Intr) vrbzbk vrbzvv agntvh vrbzb:
verbzaobklm -0.644                            
verbzavivam -0.697  0.760                     
agentveh    -0.797  0.513  0.556              
vrbzbklm:gn  0.479 -0.743 -0.565 -0.491       
vrbzvvm:gnt  0.573 -0.625 -0.823 -0.584  0.620
 
> pvals.fnc(prec0_va2.lmer)
Error in pvals.fnc(prec0_va2.lmer) : 
mcmc sampling is not yet implemented for generalized mixed models
 
> mcmcsamp(prec0_va2.lmer, n=500)
Error in .local(object, n, verbose, ...) : Update not yet written
 
Can anyone suggest a solution to this problem?
 
 
best regards
 
Liliana


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