This is just a quick note to say that Daijiang Li and I just updated the
package rr2 so that it computes three R2s for gls() {nlme} objects.
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R-sig-phylo mailing list - R-sig-phylo@r-project.org
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In case it might be useful, I recently self-published a tutorial on mixed and
phylogenetic models:
Mixed and Phylogenetic Models: A Conceptual Introduction to Correlated Data
Anthony R. Ives
You can download it for free at https://leanpub.com/correlateddata. And please,
do get it for free
Grace,
Maybe this easiest thing to do is just try phylogenetic logistic regression,
which is appropriate for binary dependent variables regardless of the form of
the independent variables. You can use phyloglm in the phylolm package, or
binaryPGLMM in the ape package.
Cheers, Tony
On
Andrea,
I second Liam’s recommendation to use a LRT.
For measurement error, the latest code I have in matlab is MERegPHYSIGv2.m,
which does both measurement error and an OU or Pagel-lambda transform (see
Johnson, M. T. J., A. R. Ives, J. Ahern, and J. P. Salminen. 2014.
Macroevolution of
Matt,
Sorry about only having a matlab implementation. If there is a lot
of interest, I could translate things into R.
There are two other possibilities that I know about. As you mention,
MCMCglmm should work, although it could not be used for standard
frequentist model selection. Matt
Scott,
I would love it if somebody would. If I were doing it now, I'd
simultaneously use an OU or maybe lambda transform. Several people
have code that does, for example, regression while assuming residual
variation has some non-Brownian phylogenetic structure (e.g., Lavins
et al.
Dear Ramona,
There are multiple ways that you could calculate an R2 for GLS. The
best form of R2 is (in matlab code)
R2=1-(e'*invV*e)/((Y-a)'*invV*(Y-a))
where e are the residuals, invV is the inverse of the covariance
matrix, Y contains the data, and a is the GLS mean of Y,
Ben,
I did respond to Isabella, but privately because I sent a big
attachment. Ted Garland and I have matlab programs that do
measurement error for correlation and regression. Ives, A. R., P. E.
Midford, and T. Garland (2007. Within-species variation and
measurement error in phylogenetic
