\name{ace}
\alias{ace}
+\alias{print.ace}
\alias{logLik.ace}
\alias{deviance.ace}
\alias{AIC.ace}
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).}
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