character.}
\item{phy}{an object of class \code{"phylo"}.}
\item{node}{a vector giving the number(s) of the node(s) where the
- parameter `theta' (the character optimum) is assumed to change. By
- default there is no change (same optimum thoughout lineages).}
+ parameter `theta' (the trait optimum) is assumed to change. The
+ node(s) can be specified with their labels if \code{phy} has node
+ labels. By default there is no change (same optimum thoughout lineages).}
\item{alpha}{the value of \eqn{\alpha}{alpha} to be used when fitting
the model. By default, this parameter is estimated (see details).}
}
Hansen, T. F. (1997) Stabilizing selection and the comparative
analysis of adaptation. \emph{Evolution}, \bold{51}, 1341--1351.
}
-\author{Emmanuel Paradis \email{Emmanuel.Paradis@mpl.ird.fr}}
+\author{Emmanuel Paradis}
\seealso{
\code{\link{ace}}, \code{\link{compar.lynch}},
\code{\link{corBrownian}}, \code{\link{corMartins}}, \code{\link{pic}}
}
\examples{
-\dontrun{
data(bird.orders)
### This is likely to give you estimates close to 0, 1, and 0
### for alpha, sigma^2, and theta, respectively:
-compar.ou(rnorm(23), bird.orders)
+compar.ou(x <- rnorm(23), bird.orders)
### Much better with a fixed alpha:
-compar.ou(rnorm(23), bird.orders, alpha = 0.1)
+compar.ou(x, bird.orders, alpha = 0.1)
### Let us 'mimick' the effect of different optima
### for the two clades of birds...
x <- c(rnorm(5, 0), rnorm(18, 5))
### ... the model with two optima:
-compar.ou(x, bird.orders, node = -2, alpha = .1)
+compar.ou(x, bird.orders, node = 25, alpha = .1)
### ... and the model with a single optimum:
compar.ou(x, bird.orders, node = NULL, alpha = .1)
### => Compare both models with the difference in deviances
-## with follows a chi^2 with df = 1.
-}
+## which follows a chi^2 with df = 1.
}
\keyword{models}