Hi,

I realize that there is no consensus on a R square for mixed models, and
that the use of it is "controversial". What I want to do is express how
good the model is. This question was asked by the jury of my PhD. I think
the method suggested by Xavier (Nakagawa paper) is a nice way of achieving
this...

Ellen

On 20 February 2013 19:40, Steven J. Pierce <pierces1@msu.edu> wrote:

> Ellen,
>
> My take on this is that the plethora of competing formulas intended to
> compute some analogue of R-square for mixed models (or even more
> problematically, for generalized linear mixed models) is a sign that
> finding
> such a measure is not simple. I have informally compared the results of
> using various formulas on a set of models (fitted to the same data) and
> found that they don’t all agree, and sometimes some of them yield results
> that seem inconsistent with other ways of examining model fit, quality, or
> comparisons between models. I'm not convinced there is a consensus that
> looking for such a measure that generalizes to mixed and generalized mixed
> models is even the right thing to do in the first place.
>
> Perhaps you could tell us what you would actually use the R-square for and
> see if the folks on the list can suggest better methods to accomplish the
> same goal.
>
> Steven J. Pierce, Ph.D.
> Associate Director
> Center for Statistical Training & Consulting (CSTAT)
> Michigan State University
> E-mail: pierces1@msu.edu
> Web: http://www.cstat.msu.edu
>
> -----Original Message-----
> From: Ellen Pape [mailto:ellen.pape@gmail.com]
> Sent: Wednesday, February 20, 2013 2:39 AM
> To: r-sig-mixed-models@r-project.org
> Subject: [R-sig-ME] variance explained by fixed effects in mixed model
>
> Dear all,
>
> What is the best and easiest way to calculate an R² to indicate the
> variance explained by the fixed effects in a mixed model? I have read some
> papers with a lot of seemingly complicated formulas, but I just want to
> know how to do it in R? (because that is not always clear to me :s)
>
> Thanks!
> Ellen
>
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>
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