X-Git-Url: https://git.donarmstrong.com/?a=blobdiff_plain;f=man%2Fyule.cov.Rd;h=10313c8d8c0e9bdb66a6af45a13d610851a3f863;hb=21eb56120c84786502f24ff9c27b39d5badfe1f7;hp=dbdc044d68a3f98c8e21e8c01ba304105e88a10c;hpb=762d28a9a2b50774a29b3d58a1e84fde4b6f898f;p=ape.git

diff --git a/man/yule.cov.Rd b/man/yule.cov.Rd
index dbdc044..10313c8 100644
--- a/man/yule.cov.Rd
+++ b/man/yule.cov.Rd
@@ -61,21 +61,24 @@ yule.cov(phy, formula, data = NULL)
 
 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
   species traits. \emph{Evolution}, \bold{59}, 1--12.
 }
-\author{Emmanuel Paradis \email{Emmanuel.Paradis@mpl.ird.fr}}
+\author{Emmanuel Paradis}
 \seealso{
   \code{\link{branching.times}}, \code{\link{diversi.gof}},
   \code{\link{diversi.time}}, \code{\link{ltt.plot}},
@@ -88,11 +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)