# Fitting binomial lmer-model, high deviance and low logLik

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## 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-helpPLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
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