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). ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html |
If you suspect a local maxima, have you tried different starting to
values to see if the likelihood is maximized in the same place? -----Original Message----- From: [hidden email] [mailto:[hidden email]] On Behalf Of Ivar Herfindal Sent: Wednesday, December 14, 2005 5:34 AM To: [hidden email] Subject: [R] Fitting binomial lmer-model, high deviance and low logLik 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). ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html |
In reply to this post by Ivar Herfindal
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). > > ______________________________________________ > [hidden email] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html > ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html |
In reply to this post by Ivar Herfindal
Try using method="AGQ" to use the adaptive Gaussian quadrature
method. This will generally give a more accurate result than PQL. If this doesn't give a more meaningful result, then it may be your data. Within each mother are the outcomes all identical ? This will give the random effects model a lot of problems. Ken > From: Ivar Herfindal <[hidden email]> > Subject: [R] Fitting binomial lmer-model, high deviance and low logLik > To: [hidden email] > Message-ID: <[hidden email]> > Content-Type: text/plain; charset=us-ascii; format=flowed > > 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). > ______________________________________________ [hidden email] 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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