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*To*: asreml@chiswick.anprod.CSIRO.AU*Subject*: ...no subject...*From*: Erwin.Koenen@alg.vf.wau.nl*Date*: Fri, 06 Mar 1998 08:39:26 -0100*Reply-To*: Erwin.Koenen@alg.vf.wau.nl*Sender*: owner-asreml

I am also trying to run a random regression analysis with ASREML. The data I have include body weight observations on heifers. Alle heifers (n=7192) were measured once. I included a extensive pedigree (n=23,434). I fitted orthogonal polynomials (TD0,TD1, etc) to the data. When I compared the likelihoods some questions arised. Model 1: fitting only random intercept (TD0) log lik = - 30347.51 Model 2: fitting random intercept and linear regression (TD0,TD1). In this situation I assumed that the random variables associated with the polynomials were incorrelated. log lik = - 30344.05 Model 3: same as model2, but now I assumed a correlation between the two variables.(see code below) log lik = - 30601.27 The lik. gets better from model 1 to model 2, which I expected. However, going to model 3 the likelihood gets worse. This lik. is even worse than in situation1. What would be a logical explanation for this ? I was first thinking of overparameterisation as I only had 1 record per animal. However, most animals are genetically related, so that it should have been possible to estimate both effects. I would appreciate comments or suggestions on this problem. Best regards, Erwin Koenen BODY WEIGHT ANALYSIS MODEL 3 animal !P sire dam SEA 7 !I LS 21 !I PS 6 !I HF 4 !I PARI 2 !I UBN 560 !I TD0 TD1 TD2 TD3 AGE BW recoded2.ped !MAKE asreml2.dat BW ~ mu SEA LS PS HF AGE PARI !r UBN 1.0, animal.TD0 1.80 animal.TD1 0.08 0 0 1 animal.TD0 2 2 0 US 1.80 0.01 0.08 animal

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