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: > > > [EMAIL PROTECTED] > 'log Lik.' -8.988466e+307 (df=4) > > However, > > > [EMAIL PROTECTED] > 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 > ______________________________________________ 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