Hi all,
I am interested in using asremls feature of identifying outliers to edit my
data - rather than editing my data prior to analyses. So - I thought I
might canvas peoples ideas about what is the most appropriate strategy!
For example, when you are developing a model for analyses, can you use the
number of outliers as an indication of whether your model is getting better
or worse? (in the absence of R2 values and assuming the same data of course)
If you know that your raw data values lie within a sensible distribution
(assuming close to normal distribution), should you then remove outliers
based on their residual solutions once you have the appropriate model
established. (What came first - the best model or the identification of
outliers?)
I know the usual approach is to edit your data before analyses based on raw
values and perhaps within levels of fixed effects if things are getting
hairy. However, this editing is usually done with no knowledge of animal
(random) effects, and when you have unbalanced data it seems to me that
using asreml to identify outliers (fitting both fixed and random effects
simultaneously) may be a better option. Otherwise, I would use SAS
facilities for the fixed effect model development, and asreml to include
random effects.
What do any of you think?
Thanks for any ideas.
Cheers
Kim
Kim Bunter
PhD Student
Animal Genetics and Breeding Unit
University of New England
Armidale, NSW, 2351
Ph: (02) 6773 3788
Fax: (02) 6773 3266
email: kbunter@metz.une.edu.au
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