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*To*: bsouthey@iastate.edu, asreml@ram.chiswick.anprod.csiro.au*Subject*: Re: About !GP bending of US matrix*From*: Kim Bunter <kbunter@metz.une.edu.au>*Date*: Wed, 31 Mar 1999 10:13:15 +1000*In-Reply-To*: <199903301433.IAA21623@lush.ansci.iastate.edu>*References*: <Your message of "Tue, 30 Mar 1999 10:42:44 +1000." <3.0.6.32.19990330104244.007ab220@metz.une.edu.au>*Sender*: asreml-owner@ram.chiswick.anprod.csiro.au

Hi all, Bruce has pointed out what I meant, but better! With regards to my example, the two correlations I quote are for traits with heritabilities T1=.32+-.08, T2=.42+-.09, and T3=.45+-.10. The correlations I reported were between t1 and t3 (0.83+-0.07), and t2 and t3(.91+-.04). I quoted these correlations because the number of records used to estimate these correlations and the pedigree are identical. Yes, trait one has a lower heritability than trait two (but even the heritabilities are probably of a similar magnitude for t1 and t2 given the size of the SEs for the heritability estimates). Yes the correlations I report are not significantly different from each other. The changes in magnitude of the SEs are in the expected DIRECTION given the trait heritabilities and the correlations between them. However, their usefulness (based on the actual magnitude) is probably misleading. This is a fault of approximation while violating the assumptions under which the approximation is made. My main point was that the SEs for the high correlations are not very meaningful for exactly Bruces' reasons. If I want to know whether a correlation is different from one, for example, I would not use the SEs of the correlation to prove/disprove this. I would use the likelihood ratio test (LRT). I should have said this earlier (as Greg Dutkowski reminded me). I have done several analyses where if I used the SE I would consider the correlation to be different to one, but this is not supported by the LRT. Do you prefer to be conservative or not? Whether Joao chooses to use the information provided by approximate SEs for correlations close to 1 or -1 (or any value for that matter) depends on whether he (she? - sorry don't know) feels the SEs are meaningful. I do not feel they are when approximated for parameters close to the boundary. As I mentioned earlier, this is an issue of estimation (with approximation of SEs) rather than an issue related to the use of ASREML itself. Anyway, I have dribbled on enough. Good luck with your analyses Joao! Cheers Kim >With your estimates Kim, are the correlations actually different (0.91 is 1 >standard deviation of 0.83 +-0.08)? Without knowing the data structure, traits >and the estimated variance components, it is hard to draw any conclusions >about the results. You may have a part-whole relationship between variables. >Further, you clearly have more statistical information about one of the traits >than the others and it probably has a higher heritability as well. > >Bayesian would overcome some (all) of the problems as the posterior would tell >you considerable amount of information. But introduce other issues e.g >proving that you have converged if using MCMC,... > >Regards >Bruce Southey > Kim Bunter 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: About !GP bending of US matrix***From:*bsouthey@iastate.edu

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