From: Gustavo A. Gutierrez <ggr03_at_IASTATE.EDU>

Date: Thu, 10 Aug 2006 14:56:34 -0500

Date: Thu, 10 Aug 2006 14:56:34 -0500

Hi Mekonnen HaileMariam,

Could you send me the pedigree and data files of Gilmour's RRM examples?.

sdf01a.ped

sdfwfml.csv

Thanks in advance,

Gustavo

*> Hi ASreml users,
*

*>
*

*> I sent this message to Dr Arthur Gilmour a few days ago. He is out can not
*

*> respond before Aug. 21. I have two questions
*

*> 1. How to fit error variance as a quadratic or other polynomial
*

function, etc?

*> 2. After fitting c spline to DIM how to get the var-covariance on daily
*

basis. I guess I

*> have to use the Z matrix in *.res file.
*

*> When I fit polynomials I use the coefficients (Q) from the *.res file and
*

*> pre and post multiple the variance-covariance (G).
*

*> Ie Q'GQ.
*

*>
*

*> Mekonnen
*

*> ----- Forwarded by Mekonnen HaileMariam/NRE on 08/08/2006 04:13 PM -----
*

*>
*

*>
*

*> Mekonnen HaileMariam
*

*> 07/08/2006 11:23 AM
*

*>
*

*>
*

*> To: arthur.gilmour_at_AGRIC.NSW.GOV.AU
*

*> cc:
*

*> Subject: Re: heterogeneous error structure
*

*>
*

*> Dear Arthur Gilmour,
*

*>
*

*> Dear Arthur Gilmour,
*

*> This is a response you sent to the question that was raised about fitting
*

*> heterogeneous error structure.
*

*> I am trying to analyse test day milk yield data recorded over 18 to 24
*

*> months of period so there are about 18 to 24 independent error variances.
*

*> Instead of fitting 18 or 20 error variance I want to fit the residual
*

*> variance (e.g. as a quadratic or other polynomial function), such that Vr
*

*> is a continuous function of days in milk.
*

*>
*

*> When I try to run the as file examples that you sent to the list I am
*

*> getting error with G or R header lines.
*

*> I could run the situation with homogenous error variance.
*

*>
*

*> My *.as file as below
*

*>
*

*> Random regession analysis of milk yield
*

*> cow 10000 !I
*

*> sire !P
*

*> ys 13 !I
*

*> herdTD 31083 !I
*

*> ageAtTest
*

*> milkY !M0 !*0.1
*

*> DIM
*

*> TestNum
*

*> sirePed
*

*> TestDATA !MAXIT 20 !ASUV
*

*> milkY ~ mu pol(DIM,-6) pol(ageAtTest,-3) !r pol(DIM,5).cow !r
*

*> pol(DIM,4).sire ,
*

*> !f herdTD mv
*

*> 1 2 2 !STEP .01
*

*> 10000 !S2==1
*

*> 18 0 DIAG 5.96 !GP +17
*

*> 5.90
*

*> 5.96
*

*> 5.96
*

*> 5.96
*

*> 5.90
*

*> 5.96
*

*> 5.96
*

*> 5.96
*

*> 5.90
*

*> 5.96
*

*> 5.96
*

*> 5.96
*

*> 5.90
*

*> 4.96
*

*> 4.96
*

*> 4.96
*

*> 4.90
*

*> pol(DIM,1).cow 2
*

*> pol(DIM,1) 0 US 8.974 !+2 !GP
*

*> 4.012 14.87
*

*> 2.033 5.662 9.557
*

*> -0.2175 0.1828 3.151 7.860
*

*> 0.02175 0.01828 0.3151 0.7860 2.1
*

*> 0.002175 0.001828 0.03151 0.07860 0.21 0.11
*

*> cow
*

*>
*

*> pol(DIM,4).sire 2
*

*> pol(DIM,4) 0 US 2.974 !+14 !GP
*

*> 1.012 4.87
*

*> 1.033 1.662 2.557
*

*> 0.02175 0.1828 1.151 2.860
*

*> 0.002175 0.01828 0.01151 0.2860 0.25
*

*> sire
*

*>
*

*> After this I want to try splines instead of pol.
*

*>
*

*> Thanks for your help
*

*> Mekonnen Haile-Mariam
*

*>
*

*>
*

*> ----- Forwarded by Mekonnen HaileMariam/NRE on 07/08/2006 09:57 AM -----
*

*>
*

*>
*

*> arthur.gilmour_at_AGRIC.NSW.GOV.AU
*

*> Sent by: ASREML-L_at_AGRIC.NSW.GOV.AU
*

*> 09/03/2005 12:26 PM
*

*> Please respond to ASREML-L
*

*>
*

*>
*

*> To: ASREML-L_at_AGRIC.NSW.GOV.AU
*

*> cc:
*

*> Subject: Re: heterogeneous error structure
*

*>
*

*>
*

*> Dear Alistair,
*

*>
*

*> I enclose a random regression example for your consideration.
*

*>
*

*>
*

*>
*

*> Following is an amended version of your job.
*

*>
*

*>
*

*> TRAIT ~ mu SEX AGEF !r pol(sAGE,2).ANIMAL pol(sAGE,2).ide(ANIMAL) MOTHER
*

*> !f mv
*

*> 0 0 2
*

*> #1 1 3
*

*>
*

*> #0 0 ID 3 # single homogeneous error variance
*

*>
*

*> pol(sAGE,2).ANIMAL 2
*

*> pol(sAGE,2) 0 US !GP
*

*> 1.64
*

*> 0.96 7.801
*

*> 0.01 0.01 0.01
*

*> ANIMAL 0 AINV
*

*>
*

*> pol(sAGE,2).ide(ANIMAL) 2
*

*> pol(sAGE,2) 0 US !GP
*

*> 1.64
*

*> 0.96 7.801
*

*> 0.01 0.01 0.01
*

*> ide(ANIMAL) 0 ID
*

*>
*

*> #MOTHER 1
*

*> #MOTHER 0 IDV 0.226
*

*>
*

*>
*

*> This assumes an animal model with repeat observations in time
*

*> for the animals. This has a common independent residual variance plus
*

*> the animal level random regression covariance structure at the residual
*

*> and the genetic level.
*

*>
*

*> You could extend it to have a random regression for the maternal
*

*> component.
*

*>
*

*> It is sometimes difficult to get solutions for the US variance structures
*

*> as it is difficult to identify the good starting values. !GP should
*

*> help.
*

*>
*

*>
*

*> Best wishes in the Name of Jesus Christ, my Saviour.
*

*> Check out http://members.dodo.net.au/~cargovale
*

*>
*

*> Arthur Gilmour PhD
*

*> Principal Research Scientist (Biometrics)
*

*> NSW Department of Primary Industries
*

*> Orange Agricultural Institute, Forest Rd, ORANGE, 2800, AUSTRALIA
*

*>
*

*> mailto:Arthur.Gilmour_at_agric.nsw.gov.au
*

*> fax: 02 6391 3899; 02 6391 3922 Australia +61
*

*> telephone work: 02 6391 3815; home: 02 6364 3288;
*

*>
*

*> http://www.agric.nsw.gov.au/reader/17263
*

*> http://www.asreml.co.uk/
*

*> The ASReml discussion group ASREML-L_at_agric.nsw.gov.au
*

*> has reactivated. To join, mailto:arthur.gilmour_at_agric.nsw.gov.au
*

*> Cookbook: http://uncronopio.org/luis/asreml_cookbook.html
*

*>
*

*> Ellen is doing well after her Hip replacement on 24 Feb but
*

*> she will need crutches for a while yet.
*

*> <><><><><><><><><><><><><><><><><><><><><><><><><>
*

*>
*

*> This message is intended for the addressee named and may contain
*

*> confidential information. If you are not the intended recipient or
*

*> received it in error, please delete the message and notify sender. Views
*

*> expressed are those of the individual sender and are not necessarily the
*

*> views of their organisation.
*

*>
*

*>
*

Gustavo A. Gutierrez

Graduate student

Animal Breeding and Genetics

Iowa State University

227 Kildee Hall, Ames

phone office:515-2942712

IA 50011-3150

http://www.ans.iastate.edu/stud/grad/directory/?id=ggr03

Received on Tue Aug 10 2006 - 14:56:34 EST

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