[R] interpreting glmer results

[Kingsford Jones]

On Mon, Oct 5, 2009 at 8:52 AM, Umesh Srinivasan
<umesh.srinivasan at gmail.com> wrote:
> Hi all,
[snip]
>
> Fixed effects:
>             Estimate Std. Error z value Pr(>|z|)
> (Intercept) -138.8423     0.4704  -295.1  < 2e-16 ***
> SpeciesCr     -0.9977     0.6259    -1.6  0.11091
> SpeciesDb     -1.2140     0.6945    -1.7  0.08046 .
> SpeciesHk     -2.0864     1.2134    -1.7  0.08553 .
> SpeciesPa     -2.6245     1.2063    -2.2  0.02958 *
> SpeciesPs      1.3056     0.4027     3.2  0.00119 **
> distancen    121.7170     0.3609   337.3  < 2e-16 ***
> ---
> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> Correlation of Fixed Effects:
>          (Intr) SpcsCr SpcsDb SpcsHk SpecsP SpcsPs
> SpeciesCr -0.423
> SpeciesDb -0.391  0.295
> SpeciesHk -0.223  0.169  0.152
> SpeciesPa -0.222  0.170  0.153  0.088
> SpeciesPs -0.732  0.507  0.458  0.262  0.263
> distancen -0.648 -0.020 -0.002 -0.003 -0.006  0.085
>
> Here, clearly, distance from the tree has an effect, but I want to
> know whether the identity of the species influences seedling numbers
> in general. I am unable, however, to make much sense of the output.


As with other linear model type functions in R the summary method
returns tests based on a factor's contrasts (treatment by default,
comparing other levels to a baseline level).  To get an omnibus test
of a factor, one option is to create a model with and without the
factor and perform an LRT:

library(lme4)
example(glmer)
gm0 <- glmer(cbind(incidence, size - incidence) ~ 1 + (1 | herd),
family = binomial, data = cbpp)
anova(gm0, gm1)

> Also, what does correlation of fixed effects really tell me?
>

These can be of interest for inference (e.g. a confidence region for
two of the coefficients is an ellipse with eccentricity defined by
their correlation).


hth,

Kingsford



> Many thanks for any help.
>
> Cheers,
> Umesh Srinivasan,
> Bangalore, India
>
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