>
>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
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