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*To*: bsouthey@bigfoot.com, asreml@chiswick.anprod.csiro.au*Subject*: Re: RE SE's for boundary fixed parameters*From*: Kim Bunter <kbunter@metz.une.edu.au>*Date*: Fri, 25 Feb 2000 10:09:31 +1100*In-Reply-To*: <38B53E3A.812E9EF3@bigfoot.com>*Sender*: asreml-owner@lamb.chiswick.anprod.csiro.au

Thank you Arthur, Julius and Bruce for your replies! With regards to Arthur's and Bruce's comments: there is indeed a large ( around -.8) sampling correlation between additive and pe effects and the log-likelihoods barely change through adding the animal effect for the 'boundary' traits. I had already checked this and had no doubt exactly what was causing the additive to move towards the boundary. My query was just really can you state that boundary=0 estimate and whether SE's are at least as good as any other we may have got had the estimate been close to the boundary (rather than fixed on it)? Unfortunately, Bruce's comments that this model (a+pe) is not appropriate is also correct in the sense that we can properly test whether it is better than fitting a or pe alone (which I had already done). However, ostrich data will never be well structured (at least in the short term) and an estimate of additive effects with a large SE is better than none at all. Think of all those years gone by where parameters were published with large SEs (and probably worse approximations of SEs than that provided by ASREML). With a new industry needing information (and a thesis partially riding on this type of data) I can not afford to be so picky with my models. It is simply the interpretation of results which must be made clear. I also know there are large pe effects from the results so far and it is not possible to ignore these by fitting animal as the only random term (ie BIG bias problem) as Bruce suggested. There are too few animals to dump all the repeated records (this would result in using only 28% of the available records). Ostrich data is also unsuitable for models often used for poultry because the mating structure is not hierachical (ie PAIRS). A moderate proportion of sires and dams are fully confounded so both estimates will be inflated by maternal effects. The confounding I have already tried to deal with by systematically fitting a number of different models to highlight which ones are the most appropriate. With regards to Arthurs comment 'It could be that there is another major source of variation in the data which has been ignored, or that there is an interaction of the genetic effect with time.' I have tested all factors I have information on, but you may well be right that there is still some source of variation which I do not have info on. However, could you give me an example of what you mean by genetic*time interaction? I am not really clear on this one. Anyway, thanks for all the input so far. The moral of this story is 'never do a PhD with data from a new industy' OR 'blood WILL come from a stone if you beat it hard enough, but it won't have the properties of real blood when you finally get it' :-) Cheers Kim At 08:20 24/02/00 -0600, Bruce Southey wrote: >Hi, >Following up Arthur's comments, what is the log likelihood with and >without the additive genetic variance? The difference is probably very >small. Note that the difference is not distributed Chi-squared but >probably a mixture of Chi-squared. > >With a variance component on the boundary condition, I doubt that SE are >valid. But, I don't remember my math stats about this and relates to >the conditions required. > >Given your comments, it does not sound that you should be fitting this >model. Basically, you don't have sufficient information to make any >conclusion about additive genetic variance. What would be informative >is to profile the likelihood with respect to the additive and permanent >variance components. I think that you would see that this is rather >flat with respect to the additive genetic variance. If so, then you are >best to just to fit animal as a random effect. This causes a bias and, >if I recall correctly, John James gave a paper about ignoring repeated >records in an animal model at one of the AAABG meeting around 1990. > >If you have a suitable pedigree structure, you could try a >parent-offspring regression or sire-dam model etc. > >Best of luck, >Bruce Southey >-- >Asreml mailinglist archive: http://www.chiswick.anprod.csiro.au/lists/asreml > Kim Bunter (M.Rur.Sc) PhD Student Animal Genetics and Breeding Unit University of New England Armidale, NSW, 2351 AUSTRALIA 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

**References**:**RE SE's for boundary fixed parameters***From:*Bruce Southey <bsouthey@bigfoot.com>

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