[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: GroupsNameVarianceStd.Dev. fam (Intercept) 17.149 4.1412 # of obs: 128, groups: fam, 58 Estimated scale (compare to 1) 0.5940245 - TO GET MORE DETAILS CLICK HERE -- View this message in context: http://r.789695.n4.nabble.com/Fitting-binomial-lmer-model-high-deviance-and-low-logLik-tp4651669.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[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: GroupsNameVarianceStd.Dev. fam (Intercept) 17.149 4.1412 # of obs: 128, groups: fam, 58 Estimated scale (compare to 1) 0.5940245 - TO GET MORE DETAILS CLICK HERE -- View this message in context: http://r.789695.n4.nabble.com/Fitting-binomial-lmer-model-high-deviance-and-low-logLik-tp4651668.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
