Re: [R] Mixed-effects models / heterogeneous covariances

From: Ana Inés Vázquez Saravia <anainesvs_at_hotmail.com>
Date: Thu 02 Feb 2006 - 03:00:15 EST


You can solve your problem assuming independence between the animals (not considering relatives information). This gives you less precision in the results, but is what you can do right now. The variance structures are more for longitudinal data or cases where you can predict in that way the variance structure. In your case the variance structure is determined by the relationships between the animals. Animal is the random effect (you will have as many Z columns as animals, and Zrows as animals*observations per animal).
I am working now in adding the pedigree information, to have ZAZ(Var ui) instead of ZIZ(Var ui) as one of the components of the variance of your response variable (y). To use it you will need to have the animals pedigree. (Note: Z: design matrix for the random effects –the animalsA:  relationship matrix between animals,
ui: random effect i; i=1...n. For a population of n total animals. y: your data points vector of responses)

Best, Ana Ines.

>From: Douglas Bates <dmbates@gmail.com>
>To: "Lutz Ph. Breitling" <lutz.breitling@gmail.com>, Ana Inés Vázquez
>Saravia <anainesvs@hotmail.com>, "Doran, Harold" <HDoran@air.org>, "J.R.
>Lockwood" <lockwood@rand.org>
>CC: r-help@stat.math.ethz.ch
>Subject: Re: [R] Mixed-effects models / heterogeneous covariances
>Date: Tue, 31 Jan 2006 16:56:08 -0600
>
>On 1/31/06, Lutz Ph. Breitling <lutz.breitling@gmail.com> wrote:
> > Dear R-list,
> >
> > maybe someone can help me with the following mixed-effects models
> > problem, as I am unable to figure it out with the 'nlme-bible'.
> >
> > I would like to fit (in R, obviously) a so-called animal model (google
> > e. g. "Heritability and genetic constraints of life-history" by Pettay
> > et al.) to estimate the variance component that is due to genetic
> > effects. The covariances of the genetic random effects between
> > observations are given by the different degrees of relatedness between
> > the individuals examined. (I find it difficult to explain, but Pettay
> > et al. describe it nicely in their methods section...)
> >
> > Is there any straight-forward way to fit such a model with R? I first
> > thought I could handle it somehow with nlme's correlation structures,
> > but these within-group structures are quite a different thing, right?
>
>Sorry to say, yes they are quite a different thing.
>
>I am aware of models like the animal model and the sire model in
>animal breeding. A student in our Animal Sciences Department, Ana
>Inés Vázquez Saravia, is working with me on developing extensions to
>the lmer function to handle such models. The actual calculations are
>not extraordinarily difficult - the difficulty is in deciding how to
>specify the model and in massaging the data to convert the model
>specification to model matrices.
>
>The model specification for an lmer model assumes that each predicted
>response is affected by one and only one random effects vector
>associated with each of the grouping factors. That is, the random
>effects have only an instantaneous effect and there is no "carry-over"
>of random effects from other levels of the grouping factor. This is
>not the case for the animal model or for the sire model. A given
>predicted response is affects by the random effects for each of the
>ancestors of the animal on which the observation is made. The "no
>carry-over" assumption is also violated in longitudinal "value-added"
>models for student achievement where the effect of a teacher in a
>given year can carry over to subsequent years. J.R. Lockwood and
>Harold Doran are very interested in these models.
>
>All of these are important practical models but, as I said, it is
>tricky to decide how to specify the model in these cases.



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