[R] GLMM: measure for significance of random variable?

[Spencer Graves]

	  1.  To evalute the significance of "the random variable" (a random 
effect?) using 'lmer', have you considered fitting models with and 
without that effect, as in the example with 'example(lmer)'?

	  2.  Regarding 'predict.lmer', I tried the following:
 > predict(fm1)
Error in predict(fm1) : no applicable method for "predict"
 > predict.glm(fm1)
NULL

	  However, ' RSiteSearch("predict lmer")' produced 9 hits for me, the 
first of which indicated that glmmPQL in library(MASS) had a predict 
method (http://finzi.psych.upenn.edu/R/Rhelp02a/archive/62139.html).

	  3.  I can't tell you why the "Laplace" method didn't work with all 
your models, but I can guess:  Do you know if the model is even 
estimable?  As a partial test for that, have you tried estimating the 
same fixed effects with "glm", something like the following:

model4b0 <- glm(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)), family=binomial)

[or 'family=quasibinomial']

	  If this fails to give you an answer, it says there is something in 
the model that is not estimable.  I might further try the same thing in 
"lm":

model4b00 <- lm(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)))

	  If this fails also, you can at least add 'singular.ok=TRUE' to find 
out what "lm" will estimate.

  	  If this doesn't answer the question, I suggest you work to develop 
this simplest, self-contained example you can think of that will 
replicate the problem, then send that to this listserve, as suggested in 
the posting guide! 'www.R-project.org/posting-guide.html'.  It's much 
easier for someone else to diagnose a problem if they can replicate it 
on their own computer in a matter of seconds.

	  hope this helps.
	  spencer graves

nina klar wrote:

> 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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> 
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-- 
Spencer Graves, PhD
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