Dear all,

I am analyzing a data set with the 'lmer' function (lme4_0.999375-28).
The dependent variable is binary: occupation status of a breeding site by a
bird species (territory ocupied vs. non-ocupied).
There are clustered observations: observations of the same territory for
several years and observations of different territories in the same year. So
I considered two random factors: territory identity and year.
I considered several predictors which are standardized.
I am using aic values to obtain the final model.

The problem is that the program generates the warning message in most of the
fitted models:
In mer_finalize(ans) : false convergence (8)

This probably happens because many territories are either occupied or
non-occupied for many or all years in the study. [there are no problems when
I include the random factor year in the formula, and I do not include the
random factor territory identity]

I added the  option 'verbose=TRUE', and  I found that the printed value of
deviance does not match with that saved by the model object (I extract it
using 'attr(summary(nameofthemodel),"AIC")$AIC').

My questions are:

(1) are the results (estimated parameters and se) of the fitted model
correct? Can I use them?

(2) how can I extract the deviance value of the printed table given by the
'verbose' option? I mean extract, as a R object. I need this because I am
performing a large set of models and I consider those with better aic.

Thank you very much in advance,


Toni Hernandez

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Antonio Hernandez Matias

Departament de Biologia Animal (Vertebrats)
Facultat de Biologia
Universitat de Barcelona

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