X-Git-Url: https://git.donarmstrong.com/?a=blobdiff_plain;f=man%2Fyule.cov.Rd;h=7e3b1a2fb395989daf87661fa8feed9d04864da2;hb=a03a8c554a6fde0dc4313688e3248bfae2e521e4;hp=ac6a749aa8dcd535576087c4fa11ab7d050ce3f3;hpb=c827059eeafc8cbe41c812b26979543ab287803e;p=ape.git diff --git a/man/yule.cov.Rd b/man/yule.cov.Rd index ac6a749..7e3b1a2 100644 --- 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)