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*To*: <asreml@chiswick.anprod.csiro.au>*Subject*: RE: R-square statistic*From*: jvanderw@metz.une.edu.au*Date*: Mon, 07 Aug 2000 14:12:57 +1000*In-Reply-To*: <001501c00018$923bedb0$54205f82@ps-ngalway.agric.uwa.edu.au>*References*: <200008062246.IAA28828@apollo.agric.nsw.gov.au>*Sender*: asreml-owner@lamb.chiswick.anprod.csiro.au

> >Percent variance accounted for is an exactly analogous calculation, but >based on mean squares. Still, there is a problem in that the expected >value of MS(Total) is influenced not only by the extent to which the model >explains variation in the response variable, but also by the design of the >experiment, for instance the number of replications of each level of a >treatment factor. I think the Genstat people would say that for this >reason it is better to compare variance components. Which is all very well >for random effects, but what do you do with fixed effects...? > >Nick Galwey Wouldn't the smallest var(e) not be the better criterion for best model? (definitely much better than R2, for reasons just described by Nick) A R2 type criterion (in the lines of what Nick described) could be 1-(var(e)/var(E)) where var(E) is the residual variance under a model with just fitting mu and var(e) the resid. variance under the current/best model. This criterion is just as valid for fixed as for mixed models, and can be easily calculated with asreml. julius -- Asreml mailinglist archive: http://www.chiswick.anprod.csiro.au/lists/asreml

**References**:**Re: R-square statistic***From:*Arthur Gilmour <gilmoua@apollo.agric.nsw.gov.au>

**RE: R-square statistic***From:*"N.W. Galwey" <ngalwey@cyllene.uwa.edu.au>

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