X-Git-Url: https://git.donarmstrong.com/?a=blobdiff_plain;f=man%2Face.Rd;h=15bd38758e42de2c7ede2fb709125350ced28b62;hb=da67dccb93d35408baa48b141fcda921772c8b9c;hp=73934dfb303498aacbdcaf7a2fe78bc536f7b532;hpb=f5793d2af1299540f8ff7ad9ec8c1a6de1fee4cf;p=ape.git

diff --git a/man/ace.Rd b/man/ace.Rd
index 73934df..15bd387 100644
--- a/man/ace.Rd
+++ b/man/ace.Rd
@@ -1,27 +1,46 @@
 \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
@@ -37,28 +56,13 @@ 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
     information criterion.}
   \item{\dots}{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.
-}
 \details{
   If \code{type = "continuous"}, the default model is Brownian motion
   where characters evolve randomly following a random walk. This model
@@ -67,7 +71,26 @@ 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 (\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
@@ -87,7 +110,7 @@ ace(x, phy, type = "continuous", method = "ML", CI = TRUE,
   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.}
@@ -130,8 +153,7 @@ 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}
 \seealso{
   \code{\link{corBrownian}}, \code{\link{corGrafen}},
   \code{\link{corMartins}}, \code{\link{compar.ou}},