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 â***â 0.001 â**â 0.01 â*â 0.05 â.â 0.1 â â 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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