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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