fixing drop.tip and bionj

[ape.git] / man / ace.Rd

diff --git a/man/ace.Rd b/man/ace.Rd
index 73934dfb303498aacbdcaf7a2fe78bc536f7b532..126a21604047e56ed508433fddae6c55633aa112 100644 (file)
--- a/man/ace.Rd
+++ b/man/ace.Rd
@@ -1,5 +1,6 @@
 \name{ace}
 \alias{ace}
+\alias{print.ace}
 \alias{logLik.ace}
 \alias{deviance.ace}
 \alias{AIC.ace}
@@ -9,13 +10,15 @@
 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).}
@@ -37,6 +40,7 @@ ace(x, phy, type = "continuous", method = "ML", CI = TRUE,
     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
@@ -67,7 +71,17 @@ ace(x, phy, type = "continuous", method = "ML", CI = TRUE,
   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 (i.e., \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 (see the package \pkg{geiger} for a different
+  implementation). 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.
 
   For discrete characters (\code{type = "discrete"}), only maximum
   likelihood estimation is available (Pagel 1994). The model is
@@ -130,8 +144,7 @@ ace(x, phy, type = "continuous", method = "ML", CI = TRUE,
   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 \email{bolker@zoo.ufl.edu}}
 \seealso{
   \code{\link{corBrownian}}, \code{\link{corGrafen}},
   \code{\link{corMartins}}, \code{\link{compar.ou}},