Generate the likelihood function that underlies PGLS
(Phylogenetic Generalised Least Squares). This is a bit of a misnomer
here, as you may not be interested in least squares (e.g., if using
this with mcmc for Bayesian inference).
make.pgls(tree, formula, data, control=list())
A bifurcating phylogenetic tree, in ape
“phylo” format.
A model formula; see lm for details on
formulae; the interface is the same here.
A data frame containing the variables in the model. If
not found in data, the variables are taken from
environment(formula), typically the environment from
which this function is called. That may perform badly with
reconciling with species names, however.
A list of control parameters. Currently the only
option is the key “method” which can be "vcv" for the
traditional variance-covariance approach (slow for large trees) or
"contrasts" for the contrasts-based approach outlined in
Freckleton (2012).
Richard G. FitzJohn
Freckleton R.P. 2012. Fast likelihood calculations for comparative analyses. Methods in Ecology and Evolution 3: 940-947.