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@stat.wisc.edu>

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

	[[alternative HTML version deleted]]


