Hi everybody,



This is my first post to r-sig-mixed-models project and I hope I am
sending my message to the appropriate place.



I have used a binomial GLMM with a logit link function to assess possible
significant effect of variables (fixed effects: patch density, sex ratio,
…) on the probability for a weevil to move from its local patch to a
neighboring patch.

I have repeated measures (of the same individuals) of the binary response
of the weevils (to move or not to move) to variations in the local
conditions of patches.

To take into account the individual variation (the propensity of a
particular individuals to respond 1 or 0), I added an individual-level
random term (the Id of the individuals) to my model. If I have weel
understood, it is also a way to deal with overdispersion in the data.



In the summary of GLMM (I used the glmer function of the lme4 package), I
found the estimate of the variance of the individual-level random term.

My questions are : Can I interpret this variance as an (real) estimation
of the variability between individuals in the response (the dispersal
decision) ?  Can I use this value to simulate individual variability in an
Individual-Based Model ?



In the animal model (Wilson et al 2009) used mixed models to assess
additive genetic variance and heritability (through variance partitioning
in random terms). Should I interpret my individual random effect as a good
estimator of variability among individuals (which can be genetically based
or not).



Let me know if I need to bring more information on may dataset and models.



I take advantage of this first post to thank authors (and users!) of mixed
models R package and of this project for the great help they bring.



Best wishes,

Dominique





Dominique Carval (PhD) - Ecologue

UR26 Système de Culture à base de Bananiers, Plantain et Ananas

CIRAD

Campus Agro-environnemental Caraïbes

Quartier Petit Morne – BP214

97285 Le Lamentin Cedex 2

Tel : (+596) 0596 42 30 28

Fax : (+596) 0596 42 30 01




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