[R] Fitting binomial lmer-model, high deviance and low logLik
Ivar Herfindal
ivar.herfindal at bio.ntnu.no
Wed Dec 14 11:34:12 CET 2005
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 at logLik
'log Lik.' -8.988466e+307 (df=4)
However,
> mod at 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).
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