Hello Franz,
I don’t come with a solution but rather a suggestion, you should look at this
paper:
Revell, Harmon and Collar 2008 Phylogenetic signal, evolutionary process and
rate. Systematic Biology 57: 591-601
In brief the authors of that paper point out that comparative methods analyzing
Dear everyone,
I want to test if a trait evolved by random drift only without selection.
My hypothesis regarding a specific trait - I don’t want to tell which if it
turns out to be a good idea ;-) - is,
that there is no selection acting on it and it is evolving by random mutations
only. Thus
Thanks for Emmanual.
Kate, I think the original explanation of dummy variables with independent
contrasts is here:
Garland Jr. T., P.H. Harvey, and A.R. Ives. 1992. Procedures for the analysis
of comparative data using phylogenetically independent contrasts. Systematic
Biology 41:18–32.
Hi Kate,
You can compute PICs for a categorical variable in the same way than
you enter it in a linear model, that is by first computing its
"contrasts" (this is different from the "P-I-Contrasts", though both
have some conceptual similarities). The easiest way to do it is to use
the
Hi Jarod,
In fact you can either choose a fixed root, or a draw from a multivariate
normal with the stationary covariance (random root).
I will change the name of the options in an upcoming release (and on gitHub) to
make it more explicit and make it homogeneous with univariate implementations
Hi Felipe,
You didn't tell us what function you used: I assumed it was ace() in ape.
Often, the fact that SEs cannot be computed by ace() means that the
model is a poor fit and that a simpler model is better. You said that
the LRT is significant (did you use anova() on the outputs of ace?),
