Fidelity data - false convergence problem - use of glm instead of glmer

OK, glm instead of lmer.

Now, if I do understand this correctly, a GLM with a binary response is
still based on a logistic regression (family binomial). Thus, the
interpretation of the summary ouput (intercept, z-value, plogis, etc) is the
same as in lmer? Except that I also obtain values of the null deviance and
residual deviance?

anova(fm5,fm4,test="Chsiq") computes the analysis of deviance table (null
deviance vs. residual deviance?). And we're not talking any longer about a
LRT?

Apologies for the lame question - you mentioned the deviance in your
previous email, but I'm still having some trouble in understanding the anova
function in this case: Why does the model incl. SameMate~BreedSuc1+Sex
differs significantly from SameMate~Sex?

Analysis of Deviance Table

Model 1: SameMate ~ Sex
Model 2: SameMate ~ BreedSuc1 + Sex
? Resid. Df ? Resid. Dev ?Df ?Deviance P(>|Chi|)
1 ? ? ? ? ? ?61 ? ? 58.200
2 ? ? ? ? ? ?60 ? ? 53.602 ? ? ? 1 ? ?4.5982 ? 0.03200 *
---
Signif. codes: ?0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1

Does this mean that the model incl. BreedSuc1+Sex fits better than the model
without BreedSuc1? Why if BreedSuc1 apparently doesn't influence SameMate?

summary(fm)

Call:
glm(formula = SameMate ~ BreedSuc1 + Sex, family = binomial,
? ?data = DSS1)

Deviance Residuals:
? ?Min ? ? ? 1Q ? Median ? ? ? 3Q ? ? ?Max
-2.2500 ? 0.3445 ? 0.4072 ? 0.7283 ? 0.8453

Coefficients:
? ? ? ? ? ? ? ? ? Estimate ? Std. Error ?z value ?Pr(>|z|)
(Intercept) ? 0.8453 ? ? ?0.5159 ? ? 1.638 ? ? 0.1013
BreedSuc1 ?1.6030 ? ? ?0.8325 ? ? 1.925 ? ? 0.0542 .
SexM ? ? ? ? ? 0.3463 ? ? 0.6912 ? ? ?0.501 ? ?0.6164
---
Signif. codes: ?0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1

(Dispersion parameter for binomial family taken to be 1)

? ?Null deviance: ? ? 58.352 ?on 62 ?degrees of freedom
Residual deviance: 53.602 ?on 60 ?degrees of freedom
AIC: 59.602

Number of Fisher Scoring iterations: 5

Thanks

Julia




2011/3/8 Douglas Bates <bates at stat.wisc.edu>

On Tue, Mar 8, 2011 at 10:57 AM, Julia Sommerfeld
<Julia.Sommerfeld at utas.edu.au> wrote:

Dear Douglas,

Thanks for your help - very much appreciated. Setting the verbose=TRUE
unfortunately doesn't fix the problem either. Please find attached a csv
file containing the data and the script. It would be great if you could

have

a look at this.

I've also tried to run MCMCglmm:

mate<-MCMCglmm(SameMate~BreedSuc1+Sex,random=~Bird, data=DSS1,

nitt=100000,

family="categorical")

However, every time I run MCMCglmm I get different results for exactly

the

same data, i.e. sometimes the p value is significant, sometimes it is

not...

The problem is that the estimate of the standard deviation of the
random effects is so poorly determined because most birds only have
one observation. ?After the na.omit operation you have 63 observations
on 55 birds. ?47 of the birds have only one observed, binary response.
?That is simply not enough information to determine parameter
estimates with any precision.

If you look at the different fits for the model with BreedSuc1 in it
you will see that the estimate of the standard deviation of the random
effects bounces all over the place at essentially the same value of
the deviance (about 53.6). ?It can be anywhere from 0.21 to 0.70 and
produce values of the deviance that are very close to one another.
This means that it is not determined with any precision at all.
Similarly for the second model a deviance of 59.5 can be achieved with
values of the standard deviation ranging from 0.49 to 1.14.

Your model is overparameterized. ?i would suggest using a glm instead
of a glmm and omit the random-effects term. ?Notice that the deviance
from the glm fits is essentially the same as the deviance from the
glmer fits. ?This indicates that the standard deviation of the random
effects is not significantly different from zero.

--
Julia Sommerfeld - PhD Candidate
Institute for Marine and Antarctic Studies
University of Tasmania
Private Bag 129, Hobart
TAS 7001

Phone: +61 458 247 348
Email: julia.somma at gmx.de
Julia.Sommerfeld at utas.edu.au

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