provisional version of the new reorder.phylo()

[ape.git] / man / ace.Rd

diff --git a/man/ace.Rd b/man/ace.Rd
index f440027d34297bcf7857f78ab7b879883a06bf23..96860220b2cef50008075f1abe6f72afff7ffac7 100644 (file)
--- a/man/ace.Rd
+++ b/man/ace.Rd
@@ -23,8 +23,8 @@ ace(x, phy, type = "continuous", method = "ML", CI = TRUE,
   \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
@@ -56,7 +56,7 @@ ace(x, phy, type = "continuous", method = "ML", CI = TRUE,
   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
+  \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
@@ -71,7 +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 (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. 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
+  over the latter, even though it is not the default.
 
   For discrete characters (\code{type = "discrete"}), only maximum
   likelihood estimation is available (Pagel 1994). The model is