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*To*: asreml@ram.chiswick.anprod.csiro.au*Subject*: editing outliers*From*: Kim Bunter <kbunter@metz.une.edu.au>*Date*: Wed, 20 Jan 1999 21:43:19 +1100*Sender*: asreml-owner@ram.chiswick.anprod.csiro.au

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 -- Asreml mailinglist archive: http://www.chiswick.anprod.csiro.au/lists/asreml

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