[R-sig-ME] residual variances in glmer
Herve.Chapuis at tours.inra.fr
Mon Dec 8 15:47:29 CET 2008
Hello every one.
I am a real R-mix models-newbie. A colleague told me I should ask the
Well, when dealing with discrete traits in animal genetics, we have
many possibilities :
- use an home-made program based, for instance, on Gianola & Foulley
- treat the data as a classical gaussian performance, use a linear mixed
model (lmer works fine) and then compute the heritability coefficient on
the observed scale as h² = 4 x sire_variance (sire_variance +
dam_variance + residual_variance).
After that, use the Dempster & Lerner formula to obtain the heritability
on the underlying scale.
- or use directly a general linear mixed model.
That's what I have done but I have been puzzled by the results.
On simulated data, (I have simulated a vector of gaussian performances
accounting for Mendelian rules, before transforming them into binary
data through a given threshold value) the first two options give me
"good" results and an estimated h² reasonably close to the expected value.
If I use glmer instead of lmer, I still obtain a result but I cannot
safely obtain the h² assuming that the residual variance is 1, can I ?
If so, the estimated h² is very high, if not above 1.
Any hint ?
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