adding stree.Rd

[ape.git] / man / yule.cov.Rd

diff --git a/man/yule.cov.Rd b/man/yule.cov.Rd
index ac6a749aa8dcd535576087c4fa11ab7d050ce3f3..7e3b1a2fb395989daf87661fa8feed9d04864da2 100644 (file)
--- a/man/yule.cov.Rd
+++ b/man/yule.cov.Rd
@@ -39,6 +39,7 @@ yule.cov(phy, formula, data = NULL)
 
   The function needs three things:
 
+\itemize{
   \item a phylogenetic tree which may contain multichotomies;
 
   \item a formula which specifies the predictors of the model described
@@ -56,18 +57,22 @@ yule.cov(phy, formula, data = NULL)
   order than for the labels, then the values for the nodes sequentially
   from the root to the most terminal nodes (i.e. in the order given by
   \code{phy$edge}).
+}
 
-  The user must obtain the values for the nodes separately.
+The user must obtain the values for the nodes separately.
 
-  Note that the method in its present implementation assumes that the
-  change in a species trait is more or less continuous between two nodes
-  or between a node and a tip. Thus reconstructing the ancestral values
-  with a Brownian motion model may be consistent with the present
-  method. This can be done with the function \code{\link{pic}} but
-  currently needs some hacking!
+Note that the method in its present implementation assumes that the
+change in a species trait is more or less continuous between two nodes
+or between a node and a tip. Thus reconstructing the ancestral values
+with a Brownian motion model may be consistent with the present
+method. This can be done with the function \code{\link{ace}}.
 }
 \value{
-  A NULL value is returned, the results are simply printed.
+  A NULL value is returned, the results are simply printed. The output
+  includes the deviance of the null (intercept-only) model and a
+  likelihood-ratio test of the fitted model against the null model.
+  Note that the deviance of the null model is different from the one
+  returned by \code{\link{yule}} because of the different parametrizations.
 }
 \references{
   Paradis, E. (2005) Statistical analysis of diversification with
@@ -86,11 +91,7 @@ x <- rnorm(45) # the tree has 23 tips and 22 nodes
 ### the standard-error for x should be as large as
 ### the estimated parameter
 yule.cov(bird.orders, ~ x)
-### compare with the simple Yule model, eventually
-### with a likelihood ratio test
-yule(bird.orders)
 ### another example with a tree that has a multichotomy
-### but we cannot run yule() because of this!
 data(bird.families)
 y <- rnorm(272) # 137 tips + 135 nodes
 yule.cov(bird.families, ~ y)