X-Git-Url: https://git.donarmstrong.com/?a=blobdiff_plain;f=R%2Fyule.R;h=041bda5deb4d5d0a519c87a8d211c46e1c48563e;hb=a03a8c554a6fde0dc4313688e3248bfae2e521e4;hp=bb3d22664664b0b7f937bf49fdc21e5bde6612d0;hpb=c827059eeafc8cbe41c812b26979543ab287803e;p=ape.git

diff --git a/R/yule.R b/R/yule.R
index bb3d226..041bda5 100644
--- a/R/yule.R
+++ b/R/yule.R
@@ -1,11 +1,11 @@
-## yule.R (2007-10-18)
+## yule.R (2009-06-08)
 
 ##     Fits Yule Model to a Phylogenetic Tree
 
 ## yule: standard Yule model (constant birth rate)
 ## yule.cov: Yule model with covariates
 
-## Copyright 2003-2007 Emmanuel Paradis
+## Copyright 2003-2009 Emmanuel Paradis
 
 ## This file is part of the R-package `ape'.
 ## See the file ../COPYING for licensing issues.
@@ -40,14 +40,14 @@ yule.cov <- function(phy, formula, data = NULL)
     bt <- rev(bt) # branching times from past to present
     ni <- cumsum(rev(table(bt))) + 1
     X <- model.matrix(formula, data)
-    Xi <- X[phy$edge[, 1], ]
-    Xj <- X[phy$edge[, 2], ]
+    Xi <- X[phy$edge[, 1], , drop = FALSE]
+    Xj <- X[phy$edge[, 2], , drop = FALSE]
     dev <- function(b) {
         2 * sum(((1/(1 + exp(-(Xi %*% b)))) +
                  (1/(1 + exp(-(Xj %*% b)))))
                 * phy$edge.length/2) -
          2 * (sum(log(ni[-length(ni)])) +
-              sum(log((1/(1 + exp(-(X[-(1:(n + 1)), ] %*% b)))))))
+              sum(log((1/(1 + exp(-(X[-(1:(n + 1)), , drop = FALSE] %*% b)))))))
     }
     out <- nlm(function(p) dev(p), p = c(rep(0, ncol(X) - 1), -1),
                hessian = TRUE)
@@ -55,10 +55,15 @@ yule.cov <- function(phy, formula, data = NULL)
     para <- matrix(NA, ncol(X), 2)
     para[, 1] <- out$estimate
     if (any(out$gradient == 0))
-      warning("The likelihood gradient seems flat in at least one dimension (null gradient):\ncannot compute the standard-errors of the transition rates.\n")
+      warning("The likelihood gradient seems flat in at least one dimension (null gradient):\ncannot compute the standard-errors of the parameters.\n")
     else para[, 2] <- sqrt(diag(solve(out$hessian)))
     rownames(para) <- colnames(X)
     colnames(para) <- c("Estimate", "StdErr")
+    ## fit the intercept-only model:
+    X <- model.matrix(~ 1, data = data.frame(X))
+    Xi <- X[phy$edge[, 1], , drop = FALSE]
+    Xj <- X[phy$edge[, 2], , drop = FALSE]
+    Dev.null <- nlm(function(p) dev(p), p = -1)$minimum
     cat("\n---- Yule Model with Covariates ----\n\n")
     cat("    Phylogenetic tree:", deparse(substitute(phy)), "\n")
     cat("       Number of tips:", n, "\n")
@@ -68,4 +73,10 @@ yule.cov <- function(phy, formula, data = NULL)
     cat("  Parameter estimates:\n")
     print(para)
     cat("\n")
+    cat("Null Deviance:", Dev.null, "\n")
+    cat("  Test of the fitted model: ")
+    chi <- Dev.null - Dev
+    df <- nrow(para) - 1
+    cat("chi^2 =", round(chi, 3), "  df =", df,
+        "  P =", round(1 - pchisq(chi, df), 3), "\n")
 }