1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
|
\name{evol.vcv}
\alias{evol.vcv}
\title{Likelihood test for variation in the evolutionary VCV matrix}
\usage{
evol.vcv(tree, X, maxit=2000, vars=FALSE, ...)
}
\arguments{
\item{tree}{an object of class \code{"simmap"}.}
\item{X}{an \code{n} x \code{m} matrix of tip values for \code{m} continuously valued traits in \code{n} species - row names should be species names.}
\item{maxit}{an optional integer value indicating the maximum number of iterations for optimization - may need to be increased for large trees.}
\item{vars}{an optional logical value indicating whether or not to estimate the variances of the parameter estimates from the Hessian matrix.}
\item{...}{optional arguments.}
}
\description{
This function takes an object of class \code{"simmap"} with a mapped binary or multistate trait and data for an arbitrary number of continuously valued character. It then fits the multiple evolutionary variance-covariance matrix (rate matrix) model of Revell & Collar (2009; \emph{Evolution}).
}
\details{
This function performs optimization by first optimizing the likelihood with respect to the Cholesky matrices using \code{\link{optim}}. Optimization is by \code{method="Nelder-Mead"}. Using box constraints does not make sense here as they would be applied to the Cholesky matrix rather than the target parameters. May have to increase \code{maxit} for large trees and more than 2 traits.
}
\value{
An object of class \code{"evol.vcv"} with the following components:
\item{R.single}{vcv matrix for the single rate matrix model.}
\item{vars.single}{optionally, a matrix containing the variances of the elements of \code{R.single}.}
\item{logL1}{log-likelihood for single matrix model.}
\item{k1}{number of parameters in the single marix model.}
\item{R.multiple}{\code{m} x \code{m} x \code{p} array containing the \code{p} estimated vcv matrices for the \code{p} regimes painted on the tree.}
\item{vars.multiple}{optionally, an array containing the variances of the parameter estimates in \code{R.multiple}.}
\item{logL.multiple}{log-likelihood of the multi-matrix model.}
\item{k2}{number of parameters estimated in this model.}
\item{P.chisq}{P-value of the \eqn{\chi^2} test on the likelihood ratio.}
\item{convergence}{logical value indicating whether or not the optimization has converged.}
}
\references{
Revell, L. J., and D. C. Collar (2009) Phylogenetic analysis of the evolutionary correlation using likelihood. \emph{Evolution}, \bold{63}, 1090-1100.
Revell, L. J. (2012) phytools: An R package for phylogenetic comparative biology (and other things). \emph{Methods Ecol. Evol.}, \bold{3}, 217-223.
}
\author{Liam Revell \email{liam.revell@umb.edu}}
\seealso{
\code{\link{evol.rate.mcmc}}, \code{\link{brownie.lite}}
}
\keyword{phylogenetics}
\keyword{comparative method}
\keyword{maximum likelihood}
|
