Re: Heterogeneous variance model in ASREML

Dr Gilmour,
Thanks for sending the correct syntax.Is it easier to code this in
ASREMl-R?

Best Regards,

-Subash
314-694-6364

-----Original Message-----
From: ASReml users discussion group [mailto:ASREML-L_at_DPI.NSW.GOV.AU] On
Behalf Of Arthur Gilmour
Sent: Saturday, August 22, 2009 3:38 AM
To: ASREML-L_at_DPI.NSW.GOV.AU
Subject: Re: Heterogeneous variance model in ASREML

Dear Subash,

Assuming I have interpreted the SAS model correctly,

The corresponding ASReml code is

fruit_set_mean ~ mu pedigree location pedigree.location !r rep.location
3 1
<n1>
<n2>
<n3>

ASReml requires the data be sorted on location and that you have
counted the numbers of records in each location
[which you could do with
  tabulate fruit_set_mean ~ location !COUNT
given as a command line before the model line,
or by using the !SUMMARY qualifier on the data line.]

In this code, 3 is the number of separate locations; equivalently
written as
location 1

<n1> is the number of plots in location 1
<n2> is the number of plots in location 2
<n3> is the number of plots in location 3
--------------

An alternate specification is
fruit_set_mean ~ mu pedigree location pedigree.location ,
 !r rep.location uni(location,1) !GU uni(location,2) !GU

wherein the uni(location,1) model term picks up the difference in
residual variance between loc 1 and loc 3; uni(location,2) picks up the
difference in residual variance between loc 2 and loc 3; and the
reported residual variance will be the variance for location 3.

On Fri, 2009-08-21 at 14:38 -0500, KRISHNAMURTHY, SUBASH [AG/1000]
wrote:
> All,
>
>
>
> I would like to fit a heterogeneous variance model for a data from an
> across location Randomized Complete Block design, equivalent to SA
> PROC MIXED statement as shown below along with the output from SAS. I
> will appreciate if anyone can provide the appropriate syntax in ASREML
> for the R matrix.
>
>
>
> proc mixed data = temp;
>
> class rep location pedigree;
>
> model fruit_set_mean = pedigree location pedigree*location /
> ddfm=satterth;
>
> random rep(location);
>
> repeated / group = location r rcorr;
>
> run;
>
>
>
> Covariance Parameter Estimates
>
>
>

>
> Cov Parm Group Estimate
>
>
>

>
> Rep(Location)
> 0.002700
>
> Residual Location L1
> 0.01740
>
> Residual Location L2
> 0.005008
>
> Residual Location L3 0.004578
>
>
>
> Thanks.
>
> -Subash
>
>
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-- 
Best Wishes,
Arthur Gilmour
Adjunct Professor
School of Computing and Mathematics
Charles Sturt University
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