From: <vmuggeo_at_dssm.unipa.it>

Date: Wed 14 Dec 2005 - 23:10:11 EST

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 Received on Wed Dec 14 23:12:47 2005

Date: Wed 14 Dec 2005 - 23:10:11 EST

Hi,

I am not able to explain fully your results..However note that the
deviance obtained in GLM with binary data (i.e Bernoulli 0/1) is
meaningless..you should group your observations to get a valid GoF-type
statistic.

Point estimates are OK, of course.

regards,

vito

> Hello

*>
**> I have a problem when fitting a mixed generalised linear model with the
**> lmer-function in the Matrix package, version 0.98-7. I have a respons
**> variable (sfox) that is 1 or 0, whether a roe deer fawn is killed or not
**> by red fox. This is expected to be related to e.g. the density of red
**> fox (roefoxratio) or other variables. In addition, we account for family
**> effects by adding the mother (fam) of the fawns as random factor. I want
**> to use AIC to select the best model (if no other model selection
**> criterias are suggested).
**>
**> the syntax looks like this:
**> > mod <- lmer(sfox ~ roefoxratio + (1|fam), data=manu2, family=binomial)
**>
**> The output looks ok, except that the deviance is extremely high
**> (1.798e+308).
**>
**> > mod
**> Generalized linear mixed model fit using PQL
**> Formula: sfox ~ roefoxratio + (1 | fam)
**> Data: manu2
**> Family: binomial(logit link)
**> AIC BIC logLik deviance
**> 1.797693e+308 1.797693e+308 -8.988466e+307 1.797693e+308
**> Random effects:
**> Groups Name Variance Std.Dev.
**> fam (Intercept) 17.149 4.1412
**> # of obs: 128, groups: fam, 58
**>
**> Estimated scale (compare to 1) 0.5940245
**>
**> Fixed effects:
**> Estimate Std. Error z value Pr(>|z|)
**> (Intercept) -2.60841 1.06110 -2.45820 0.01396 *
**> roefoxratio 0.51677 0.63866 0.80915 0.41843
**>
**> I suspect this may be due to a local maximum in the ML-fitting, since:
**>
**> > mod@logLik
**> 'log Lik.' -8.988466e+307 (df=4)
**>
**> However,
**>
**> > mod@deviance
**> ML REML
**> 295.4233 295.4562
**>
**> So, my first question is what this second deviance value represent. I
**> have tried to figure out from the lmer-syntax
**> (https://svn.r-project.org/R-packages/trunk/Matrix/R/lmer.R)
**> but I must admit I have problems with this.
**>
**> Second, if the very high deviance is due to local maximum, is there a
**> general procedure to overcome this problem? I have tried to alter the
**> tolerance in the control-parameters. However, I need a very high
**> tolerance value in order to get a more reasonable deviance, e.g.
**>
**> > mod <- lmer(sfox ~ roefoxratio + (1|fam), data=manu2,
**> family=binomial,
**> control=list(tolerance=sqrt(sqrt(sqrt(sqrt(.Machine$double.eps))))))
**> > mod
**> Generalized linear mixed model fit using PQL
**> Formula: sfox ~ roefoxratio + (1 | fam)
**> Data: manu2
**> Family: binomial(logit link)
**> AIC BIC logLik deviance
**> 130.2166 141.6247 -61.10829 122.2166
**> Random effects:
**> Groups Name Variance Std.Dev.
**> fam (Intercept) 15.457 3.9316
**> # of obs: 128, groups: fam, 58
**>
**> Estimated scale (compare to 1) 0.5954664
**>
**> Fixed effects:
**> Estimate Std. Error z value Pr(>|z|)
**> (Intercept) -2.55690 0.98895 -2.58548 0.009724 **
**> roefoxratio 0.50968 0.59810 0.85216 0.394127
**>
**> The tolerance value in this model represent 0.1051 on my machine. Does
**> anyone have an advice how to handle such problems? I find the tolerance
**> needed to achieve reasonable deviances rather high, and makes me not too
**> confident about the estimates and the model. Using the other methods,
**> ("Laplace" or "AGQ") did not help.
**>
**> My system is windows 2000,
**> > version
**> _
**> platform i386-pc-mingw32
**> arch i386
**> os mingw32
**> system i386, mingw32
**> status
**> major 2
**> minor 2.0
**> year 2005
**> month 10
**> day 06
**> svn rev 35749
**> language R
**>
**> Thanks
**>
**> Ivar Herfindal
**>
**> By the way, great thanks to all persons contributing to this package
**> (and other), it makes my research more easy (and fun).
**>
**> ______________________________________________
**> 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
**>
*

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Dec 14 23:12:47 2005

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