how do I predict across regions and deal with aliasing and nesting in ASREML-R?

On the bottom of page 65 of the ASREML-R manual (release 2.0, Feb 2007),
there is this text:
 
"The present argument enables the construction of means by averaging
only the estimable cells of the hyper-table. It is reguarly used for
nested factors, for example locations nested in regions."
 
Unfortunately, there is no example....
 
I have some across region analyses of crop genotype (G) data with BLUEs
and weights as input. I wish to estimate the hyper-table of Region x
Genotype.
 
 Has anyone got some examples of models/predictions using the present
argument to do this? Whatever model I use in ASREML-R I usually end up
with 'aliasing' and therefore a set of NAs for my predictions. I can
override the aliasing, but am afraid that I might be overfitting in any
case.... Some of the models I've tried are:
 
Fixed = Env, random = ~ G + Reg:G + Reg:Env:G
Fixed = Env, random = ~ corh(Reg):G
Fixed = Env, random = ~ diag(Reg):G + diag(Env):G Am not sure if this
one is legit - am assuming that the regional interaction will be taken
out first... Unless there is some way to nest it within regions...?
 
 
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Scott Chapman
Principal Research Scientist
Crop Adaptation
CSIRO Plant Industry
Queensland Bioscience Precinct, 306 Carmody Rd, St. Lucia, QLD 4067,
Australia
Ph: +61 7 3214 2254 Fax: +61 7 3214 2920 Web:
http://www.csiro.au/people/scott.chapman

 
Received on Thu Feb 25 2008 - 16:22:58 EST