\name{ace}
\alias{ace}
+\alias{print.ace}
\alias{logLik.ace}
\alias{deviance.ace}
\alias{AIC.ace}
\alias{anova.ace}
\title{Ancestral Character Estimation}
+\description{
+ This function estimates ancestral character states, and the associated
+ uncertainty, for continuous and discrete characters.
+
+ \code{logLik}, \code{deviance}, and \code{AIC} are generic functions
+ used to extract the log-likelihood, the deviance, or the Akaike
+ information criterion of a fitted object. If no such values are
+ available, \code{NULL} is returned.
+
+ \code{anova} is another generic function which is used to compare
+ nested models: the significance of the additional parameter(s) is
+ tested with likelihood ratio tests. You must ensure that the models
+ are effectively nested (if they are not, the results will be
+ meaningless). It is better to list the models from the smallest to the
+ largest.
+}
\usage{
ace(x, phy, type = "continuous", method = "ML", CI = TRUE,
model = if (type == "continuous") "BM" else "ER",
scaled = TRUE, kappa = 1, corStruct = NULL, ip = 0.1)
+\method{print}{ace}(x, digits = 4, ...)
\method{logLik}{ace}(object, ...)
\method{deviance}{ace}(object, ...)
\method{AIC}{ace}(object, ..., k = 2)
\method{anova}{ace}(object, ...)
}
\arguments{
- \item{x}{a vector or a factor.}
+ \item{x}{a vector or a factor; an object of class \code{"ace"} in the
+ case of \code{print}.}
\item{phy}{an object of class \code{"phylo"}.}
\item{type}{the variable type; either \code{"continuous"} or
\code{"discrete"} (or an abbreviation of these).}
\item{method}{a character specifying the method used for
- estimation. Three choices are possible: \code{"ML"}, \code{"pic"},
- or \code{"GLS"}.}
+ estimation. Four choices are possible: \code{"ML"}, \code{"REML"},
+ \code{"pic"}, or \code{"GLS"}.}
\item{CI}{a logical specifying whether to return the 95\% confidence
intervals of the ancestral state estimates (for continuous
characters) or the likelihood of the different states (for discrete
structure to be used (this also gives the assumed model).}
\item{ip}{the initial value(s) used for the ML estimation procedure
when \code{type == "discrete"} (possibly recycled).}
+ \item{digits}{the number of digits to be printed.}
\item{object}{an object of class \code{"ace"}.}
\item{k}{a numeric value giving the penalty per estimated parameter;
the default is \code{k = 2} which is the classical Akaike
information criterion.}
- \item{...}{further arguments passed to or from other methods.}
-}
-\description{
- This function estimates ancestral character states, and the associated
- uncertainty, for continuous and discrete characters.
-
- \code{logLik}, \code{deviance}, and \code{AIC} are generic functions
- used to extract the log-likelihood, the deviance (-2*logLik), or the
- Akaike information criterion of a tree. If no such values are
- available, \code{NULL} is returned.
-
- \code{anova} is another generic function that is used to compare
- nested models: the significance of the additional parameter(s) is
- tested with likelihood ratio tests. You must ensure that the models
- are effectively nested (if they are not, the results will be
- meaningless). It is better to list the models from the smallest to the
- largest.
+ \item{\dots}{further arguments passed to or from other methods.}
}
\details{
If \code{type = "continuous"}, the default model is Brownian motion
generalized least squares (\code{method = "GLS"}, Martins and Hansen
1997, Cunningham et al. 1998). In the latter case, the specification
of \code{phy} and \code{model} are actually ignored: it is instead
- given through a correlation structure with the option \code{corStruct}.
+ given through a correlation structure with the option
+ \code{corStruct}.
+
+ In the default setting (\code{method = "ML"} and \code{model = "BM"})
+ the maximum likelihood estimation is done simultaneously on the
+ ancestral values and the variance of the Brownian motion process;
+ these estimates are then used to compute the confidence intervals in
+ the standard way. The REML method first estimates the ancestral value
+ at the root (aka, the phylogenetic mean), then the variance of the
+ Brownian motion process is estimated by optimizing the residual
+ log-likelihood. The ancestral values are finally inferred from the
+ likelihood function giving these two parameters. If \code{method =
+ "pic"} or \code{"GLS"}, the confidence intervals are computed using
+ the expected variances under the model, so they depend only on the
+ tree.
+
+ It could be shown that, with a continous character, REML results in
+ unbiased estimates of the variance of the Brownian motion process
+ while ML gives a downward bias. Therefore the former is recommanded,
+ even though it is not the default.
For discrete characters (\code{type = "discrete"}), only maximum
likelihood estimation is available (Pagel 1994). The model is
is determined from the data.
}
\value{
- a list with the following elements:
+ an object of class \code{"ace"} with the following elements:
\item{ace}{if \code{type = "continuous"}, the estimates of the
ancestral character values.}
Likelihood of ancestor states in adaptive radiation. \emph{Evolution},
\bold{51}, 1699--1711.
}
-\author{Emmanuel Paradis \email{Emmanuel.Paradis@mpl.ird.fr}, Ben Bolker
-\email{bolker@zoo.ufl.edu}}
+\author{Emmanuel Paradis, Ben Bolker}
\seealso{
\code{\link{corBrownian}}, \code{\link{corGrafen}},
\code{\link{corMartins}}, \code{\link{compar.ou}},
projects / ape.git / blobdiff