Re: Region by Environment models...

Dear Scott,

Again I will reply without the detailed ASReml-R syntax

The model I used in the previous email was pretty a basic compound
symmetry model.
 
a.asr <- asreml(fixed = gyp ~ Env,
        random = ~Genotype + Region:Genotype + Region:Env:Genotype,
        rcov = ~units)
 
** The RANDOM model can equivalently be written as
        random = ~Genotype + Region:Genotype + Env:Genotype,

I would like to use better corh or fa structures in this analysis.
Ideally, these would be fitted to the entire dataset and then the data
predicted by Region somehow...
For example, I know that a corh model fits much better to the entire set
of environments in this dataset:
 
a.asr <- asreml(fixed = gyp ~ Env,
        random = ~corh(Env):Genotype,
        rcov = ~units)

** The extention from the Simple model is
         random = ~corh(Reg):Genotype + corh(Env):Genotype,
 BUT it does not make sense to allow heterogeneous genetic variance
without allowing heterogeneous error variance.
Given a likely 10fold + range in phenotypic variance, all levels need to
be heterogeneous.
Prediction of Reg x Geno is just as for the simpler model.

Your following suggestions are not appropriate.

However, I would like to be able to predict at each region, so can I
somehow nest the corh model within region, or will I have to run these
separately?
 
a.asr <- asreml(fixed = gyp ~ Env,
        random = ~diag(Region):corh(Env):Genotype,
        rcov = ~units)
 
or perhaps:
 
 a.asr <- asreml(fixed = gyp ~ Env,
        random = ~at(Region, 1):corh(Env):Genotype + at(Region,
2):corh(Env):Genotype,
        rcov = ~units)
 
The problem with these models appears to be that I would need to fit the
corh only to the Envs that occur within each Region. Does that mean I need
to have two new Env factors where they are declared NA for the regions
where they do not occur?

May Jesus Christ be gracious to you in 2008,

Arthur Gilmour, His servant .
 
Mixed model regression mapping for QTL detection in experimental crosses.
Computational Statistics and Data Analysis 51:3749-3764 at
http://dx.doi.org/10.1016/j.csda.2006.12.031

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Received on Mon Mar 01 2008 - 09:29:08 EST