Dear Haja,
ASREML does not report an Rsqu value but in many cases it will not
be difficult to work one out from the ANOVA table when the first term
is mu and there are no fixed effects absorbed (in the SPARSE equations).
Ignoring the mu line, calculate the relative variance accounted in the model
as the sum of products of df x R-incr. The relative error variance
is the error DF so R-square
Relative Model Variance
-----------------------
SUm of both components.
E.g. in shf example [9.2]
Anova is mu 1 853.96
c(var) 24 13.02
Error 125
RMV = 24*13.02 = 312.48
So R^2 is 312.48/(125 +312.48) = 0.714
Note that Rsqu is a concept from fixed effect models and it is not
obvious to me how it should be defined in multistrata mixed models.
You can of course do the same calculation for GLMM models but I am
not aware of this being widely used and its meaning is a little less
direct becasue there it is with respect to a working variable
which changes as the model changes so has a moving basis.
Others comments are welcome.
Arthur
> X-Authentication-Warning: lamb.chiswick.anprod.csiro.au: petidomo set sender
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> From: "Haja Kadarmideen" <h.kadarmideen@ed.sac.ac.uk>
> To: asreml@chiswick.anprod.csiro.au
> Date: Fri, 4 Aug 2000 11:29:58 +0100
> Subject: R-square statistic
>
> Dear ASREML'ers,
> A very simple question and sorry if this has been clarified before.
> What is the equivalent of R-square (Multiple Correlation Coefficient) in
> ASREML-GLMM and how to get this ? -something that tells us about
> the adequacy of the model fitted, with effect of mean of y eliminated ?
> I am not sure if testing significance of 'fixed' or 'random' effect in
the
> model would neccssarily lead to similar statistic, as they don't give us
> any idea about the appropriateness of the whole model itself ?
> Thanks in advance
> Haja
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Arthur Gilmour PhD mailto:Arthur.Gilmour@agric.nsw.gov.au
Principal Research Scientist (Biometrics) fax: <61> 2 6391 3899
NSW Agriculture <61> 2 6391 3922
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