residual variances in glmer
On Mon, 8 Dec 2008, Herv? CHAPUIS wrote:
Hello every one. I am a real R-mix models-newbie. A colleague told me I should ask the list. Well, when dealing with discrete traits in animal genetics, we have many possibilities : - use an home-made program based, for instance, on Gianola & Foulley (1993) algorithm. - 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 h? 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 ?
The problem is that in the binomial GLMM, the phenotypic variance varies according to the value of the intercept, which depends on included fixed effects etc. There is an approximate heritability for this model described in Yazdi et al J. Dairy Sci. 85:1563??1577.
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