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*To*: asreml@chiswick.anprod.CSIRO.AU*Subject*: Re: fitting random regressions*From*: Ron Crump <rcrump@metz.une.edu.au>*Date*: Thu, 05 Mar 1998 17:56:21 +1000*Sender*: owner-asreml

>I'm interested into analysing repeated measurements of a progeny test >using a random regression model, with a econd order polynomial. The data >structure in the data file is: <snip> It may not be appropriate to suggest this on an ASREML discussion group, but you may find it easier to approach this analysis using the new version of DFREML, which includes a program to do estimation of Covariance Functions. Sorry Arthur. While I am here, I have encountered a couple of problems which someone else may have suggestions about:- (a) a multivariate run which will not remain PD, even with !GP specified. The traits are highly correlated, and eventually the program reports that it cannot form R inverse and gives up. If I use the cholesky decomposition, the problem persists. I suspect the decomposition fails. Does anybody have any experience of forcing these types of problems through? I could try fixing some parameters, but would like a (fairly) robust suggestion to work with as each run takes a long time even if it eventually crashes. (b) I have data on two traits, one of which has repeated records. I can do a bivariate run estimating fixing the residual covariance at zero and fitting a code common to all records on the animal to estimate the residual covariance and part of the permanent environmental effect (thanks, Arthur). This run works fine. In univariate analyses of these traits, I can also fit a second random effect (Service Sire, the traits are number born alive in sows - parity 1 and later parities) with either an identity or using the NRM. If I try to fit this effect in a bivariate model as above it causes a Fortran error (Floating invalid) after estimating the first likelihood. This happens both with an Identity or NRM associated with the service sire effect, and when I fix the between trait service sire covariance to zero (that being the parameter I haven't previously managed to estimate from either the uni or simple bivariate analyses). Again, does anyone have any suggestions? I am running the programs on an Alpha based machine running unix, with the ASREML programs compiled at fixed sizes (ie an executable corresponding to each of S1 to S9). Ron Crump.

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