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
effects). 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. 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? 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. 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
Thank you for your help!
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
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