Fitting binomial lmer-model, high deviance and low logLik

Thu, Dec 15, 2005 3:44 AM

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 <ivar.herfindal at bio.ntnu.no>
Subject: [R] Fitting binomial lmer-model, high deviance and low logLik
To: r-help at stat.math.ethz.ch
Message-ID: <439FF524.1040403 at bio.ntnu.no>
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 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).