Thank you Arthur and Bruce for your help. Arthurs reply below for the list
>Date: Wed, 2 Feb 2000 16:06:56 +1100 (EST)
>From: Arthur Gilmour <firstname.lastname@example.org>
>Reply-To: Arthur Gilmour <email@example.com>
>Subject: Re: this weeks query is...?
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>> Date: Wed, 02 Feb 2000 15:49:01 +1100
>> To: email@example.com
>> From: Kim Bunter <firstname.lastname@example.org>
>> Subject: this weeks query is...?
>> Mime-Version: 1.0
>> Hi there,
>> I am trying to develop appropriate fixed effects models for a variety of
>> traits (what's new).
>> There are relatively few animals involved although up to 8 repeated records
>> (8 production years) per animal (average no. recs about 3.5). I assume that
>> the correct approach (also more robust?) for assessing the sig or otherwise
>> of various fixed effects is to concurrently fit ide(animal) as a random
>> effect in the model (rather than using SAS and excluding all random
>Yes. Fitting animal means that tests if effects based on groups of animals
>on the correct (animnal level) error variance.
>> Prior analyses indicate that many of the traits I am looking at
>> are quite repeatable. It was suggested to me that I should also check the
>> interaction year*ide(animal) in case this is a problem.
>Isn't year.aniaml simply the residual after fitting animal?
>> I decided to do
>> this, although I would have thought that a significant interaction here
>> would simply co-incide with a trait of low repeatability?
> I do not see how you can do this unless there are repeat observations
>> This gives rise
>> to the question: if a trait is not repeatable do you need to in fact fit
>> ide(animal) to check for sig. fixed effects.
>If it is not repeatable, the animal; variance component will be zero
>and so tests of fixed effects will be against the within animal variance.
>> This is completely apart from
>> the fact that if this were the result my trait of interest would also not
>> appear heritable (which would cause a problem in itself :-)).
>> Anyway, fitting this interaction (year*animal) can be a problem for some
>> traits re convergence (which was not the effect I was trying to check for).
>> What seems to happen is that this effect is bounded at zero if no
>> qualifiers are used. Over-riding this with !GU means I can't get
>> convergence at all (interaction estimate ranges from -ve to +ve). Is it
>> safe to assume that this variance component is probably zero, or is it more
>> correct to assume that there is not enough information in the data to do
>> this test? Do I in fact need to do this test or am I wasting my time (and
>I suspect the interaction is completely confounded with the residual
>which is why you are having trouble. any partitioning you do
>achieve will be completely arbir=trary.
>> Any suggestions?
>> Thank you for your help!
>> Kim Bunter (M.Rur.Sc)
>> PhD Student
>> Animal Genetics and Breeding Unit
>> University of New England
>> Armidale, NSW, 2351
>> Ph: (02) 6773 3788
>> Fax: (02) 6773 3266
>> email: email@example.com
>> Asreml mailinglist archive:
>Arthur Gilmour PhD mailto:Arthur.Gilmour@agric.nsw.gov.au
>Principal Research Scientist (Biometrics) fax: <61> 2 6391 3899
>NSW Agriculture <61> 2 6391 3922
>Orange Agricultural Institute telephone work: <61> 2 6391 3815
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>"Christ Jesus came into the world to save sinners" I Timothy 1:15.
Kim Bunter (M.Rur.Sc)
Animal Genetics and Breeding Unit
University of New England
Armidale, NSW, 2351
Ph: (02) 6773 3788
Fax: (02) 6773 3266
Asreml mailinglist archive: http://www.chiswick.anprod.csiro.au/lists/asreml