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

From: Spencer Graves <spencer.graves_at_pdf.com>
Date: Fri 02 Dec 2005 - 04:26:54 EST

  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]
>
>
>
> [[alternative HTML version deleted]]
>
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-- 
Spencer Graves, PhD
Senior Development Engineer
PDF Solutions, Inc.
333 West San Carlos Street Suite 700
San Jose, CA 95110, USA

spencer.graves@pdf.com
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Received on Fri Dec 02 05:33:36 2005

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