Another issue is that the tree is not ultrametric. If you use nlme::gls
to fit the model and you have a non-ultrametric tree, you need to use
the weights argument to pass the tip heights. Otherwise gls() assumes
the tree is ultrametric. This could be part of the problem. I wrote a
post about
Dear Oliver & Julien.
> Also, “gls” estimates a correlation rather than a covariance
> structure. On non-ultrametric trees (such as yours) this will lead to
> different results.
This is a great point. If your tree is non-ultrametric you can do
something like:
w<-diag(vcv.phylo(tree))
Hi Oliver,
The "gls" from nlme uses REML by default. I think that caper or phylolm use ML
instead. On small sample size ML estimates (e.g., "lambda") are known to be
biased and you may sometimes not have enough power to estimate them. With
increasing sample size (bigger trees) this is less an
Dear list members:
I tried various R packages to calcuate a PGLS with the data set (csv
and nwk) I have attached to this email. I would like to use the Pagels
lambda model to attain an index that measures whether data exhibit
phylogenetic dependence or not.
While doing so, I came up
