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:

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[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).

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