From: Spencer Graves <spencer.graves_at_pdf.com>

Date: Fri 02 Dec 2005 - 04:26:54 EST

*>
*

*> + I((DIST_WATER-200)*(DIST_WATER<200)) +
*

*> + I((DIST_VILL-900)*(DIST_VILL<900)) +
*

> + (1|TIER), family=binomial, method="Laplace")

*>
*

*>
*

> 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:
*

*>
*

*>
*

*>
*

> + 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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*>
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*> ______________________________________________
*

*> R-help@stat.math.ethz.ch mailing list
*

*> https://stat.ethz.ch/mailman/listinfo/r-help
*

*> PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
*

Date: Fri 02 Dec 2005 - 04:26:54 EST

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

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 findout 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 +

> + (1|TIER), family=binomial, method="Laplace")

>>summary(model4a)

> Generalized linear mixed model fit using Laplace

>>model4b<-lmer(RESPONSE~ D_TO_FORAL +

> + I((DIST_GREEN-300)*(DIST_GREEN<300))+

-- 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 www.pdf.com <http://www.pdf.com> Tel: 408-938-4420 Fax: 408-280-7915 ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.htmlReceived on Fri Dec 02 05:33:36 2005

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