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

From: Scott Chapman <Scott.Chapman_at_CSIRO.AU>
Date: Tue, 26 Feb 2008 10:23:08 +1000

Thanks Arthur. If someone could check my ASREML-R syntax below would
help.... I think it is ok after some 'testing' against raw means...

Taking your example (which is coded the same as my data), I assume that
you take G as random and do not declare Region in the model? So,

PREDICT G Reg !present Reg Exp will predict BLUPs for each G in each
Region?

In this model, do we need both Reg and Exp in the !present? Since they
are nested, I think that only Exp is needed?

######## Below are the calls for my data. Think that they are ok now....
Here is the full call, using input data of fitted genotype BLUEs for gyp
and weights (newwt). Dispersion = the average per trial residual,
Env=Exp (in Arthur's example).
In ASREML-R, it seems that the weights HAVE to be supplied if you have
more than 2 factors in the !present statement, or the predict will not
proceed.

a.asr <- asreml(fixed = gyp ~ Env,
random = ~Genotype + Region:Genotype + Region:Env:Genotype,
rcov = ~units, data = a,
family = asreml.gaussian(link = "identity", dispersion =
0.2258),
weights = newwt, na.method.X = "include")

a.asr <- predict(a.asr, classify=list("Region:Genotype"),
present=list("Region:Genotype"="Env")) # will predict at the mean
level of all Envs. This gives the same result as the default
prediction....

a.asr <- predict(a.asr, classify=list("Region:Genotype"),
present=list("Region:Genotype"=c("Region","Env"))) # will predict at
the mean level of Region. This gives the same result as the default
prediction....

a.asr <- predict(a.asr, classify=list("Region:Genotype"),
present=list("Region:Genotype"=list("Env"),
prwts=rep(1/66,length(levels(a\$Env))))) #allows use of weights for
each environment (i.e. 66 levels of them....)

_____

From: ASReml users discussion group
[mailto:ASREML-L_at_AGRIC.NSW.GOV.AU] On Behalf Of
arthur.gilmour_at_DPI.NSW.GOV.AU
Sent: Tuesday, 26 February 2008 8:46 AM
To: ASREML-L_at_AGRIC.NSW.GOV.AU
Subject: Re: how do I predict across regions and deal with
aliasing and nesting in ASREML-R?

Dear Scott,

re: 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...?

=========
I can answer this with respect to ASReml and hope you can apply
The funcrionality should be equivalent.

First, concerning the models.

Reg:Env is equiavalent to Env if the Environments are coded
uniguely across regions. i.e. if Envoronments are place names, they
will be unique across regions. It is of course possible to code
Environments as 1, 2, 3 etc within regions in which case the levels
of Region (say if environments represented Early/Late or
Irrigated/Dry rather than places)

Second, Aliassing in prediction relates to fixed effects, rather
than random effects,
so assuming you have shown the whole model above, aiassing will
mean that some Environments have no data.

However, in that case !PRESENT Region Environment should
resolve the problem.
More likely you have fitted some other fixed interaction which
has a missing cell so that it cannot be averaged.

Consider the example
Region 1 1 1 1 2 2
Expt 1 2 3 4 5 6

This is the situation envisaged by the User Guide comment.
When calculating Regional means,
for region 1, we want to average over environments 1 2 3 4
for region 2, over environments 5 6

PREDICT G Reg !present Reg Exp

will do that. We do not want to include G in the !PRESENT
list, assuming some G may not appear in some experiments,
because that would mix up Env effects into the comparison of
Genotypes within a Region.

I trust this helps.

the genotype effects a

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 Fri Feb 26 2008 - 10:23:08 EST

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