Hi,

I have three questions concerning GLMMs.
First, I ' m looking for a measure for the significance of the random variable in a glmm.
I'm fitting a glmm (lmer) to telemetry-locations of 12 wildcat-individuals against random locations (binomial response). The individual is the random variable. Now I want to know, if the individual ("TIER") has a significant effect on the model outcome. Does such a measure exist in R?
My second question is, if there is a "predict"-function for glmms in R? Because I would like to produce a predictive habitat-map (someone asked that before, but I think there was no answer so far).
And the third, why the method "laplace" doesn't work with all my models.

thank you very much

nina klar




R output for a model, which works with laplace:

> model4a<-lmer(RESPONSE~ D_TO_FORAL +
+ I((DIST_WATER-200)*(DIST_WATER<200)) +
+ I((DIST_VILL-900)*(DIST_VILL<900)) +
+ (1|TIER), family=binomial, method="Laplace")
> summary(model4a)
Generalized linear mixed model fit using Laplace 
Formula: RESPONSE ~ D_TO_FORAL + I((DIST_WATER - 200) * (DIST_WATER <      200)) + I((DIST_VILL - 900) * (DIST_VILL < 900)) + (1 | TIER) 
 Family: binomial(logit link)
      AIC      BIC    logLik deviance
 3291.247 3326.739 -1639.623 3279.247
Random effects:
     Groups        Name    Variance    Std.Dev. 
       TIER (Intercept)       5e-10  2.2361e-05 
# of obs: 2739, groups: TIER, 12

Estimated scale (compare to 1)  1.476153 

Fixed effects:
                                              Estimate  Std. Error z value  Pr(>|z|)    
(Intercept)                                 0.19516572  0.05812049  3.3580 0.0007852 ***
D_TO_FORAL                                 -0.01091458  0.00113453 -9.6204 < 2.2e-16 ***
I((DIST_WATER - 200) * (DIST_WATER < 200)) -0.00551492  0.00061907 -8.9084 < 2.2e-16 ***
I((DIST_VILL - 900) * (DIST_VILL < 900))    0.00307265  0.00025708 11.9521 < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Correlation of Fixed Effects:
            (Intr) D_TO_F I-2*(<2
D_TO_FORAL  -0.247               
I((DI-2*(<2  0.561 -0.023        
I((DI-9*(<9  0.203  0.047 -0.206 


here is the R-output for a model which doesn't work with laplace:

> model4b<-lmer(RESPONSE~ D_TO_FORAL +  
+ I((DIST_GREEN-300)*(DIST_GREEN<300))+
+ I((DIST_WATER-200)*(DIST_WATER<200)) +
+ I((DIST_VILL-900)*(DIST_VILL<900)) +
+ I((DIST_HOUSE-200)*(DIST_HOUSE<200)) + 
+ (1|TIER), family=binomial, method="Laplace")
Fehler in optim(PQLpars, obj, method = "L-BFGS-B", lower = ifelse(const,  : 
        non-finite finite-difference value [7] 



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