X-Git-Url: https://git.donarmstrong.com/?a=blobdiff_plain;f=R%2Face.R;h=b751c697d63e116b24a972517b294715dbd4fb65;hb=c488b74490ee3d9d200de0e471881f002a18fe4f;hp=0439600832a0f1018af4b75974ea11703f2a6e02;hpb=f5793d2af1299540f8ff7ad9ec8c1a6de1fee4cf;p=ape.git

diff --git a/R/ace.R b/R/ace.R
index 0439600..b751c69 100644
--- a/R/ace.R
+++ b/R/ace.R
@@ -1,39 +1,86 @@
-## ace.R (2009-03-22)
+## ace.R (2013-03-18)
 
-##     Ancestral Character Estimation
+##   Ancestral Character Estimation
 
-## Copyright 2005-2009 Emmanuel Paradis and Ben Bolker
+## Copyright 2005-2013 Emmanuel Paradis and Ben Bolker
 
 ## This file is part of the R-package `ape'.
 ## See the file ../COPYING for licensing issues.
 
-ace <- function(x, phy, type = "continuous", method = "ML", CI = TRUE,
-                model = if (type == "continuous") "BM" else "ER",
-                scaled = TRUE, kappa = 1, corStruct = NULL, ip = 0.1)
+.getSEs <- function(out)
 {
-    if (class(phy) != "phylo")
-      stop('object "phy" is not of class "phylo".')
+    h <- out$hessian
+    if (any(diag(h) == 0)) {
+        warning("The likelihood gradient seems flat in at least one dimension (gradient null):\ncannot compute the standard-errors of the transition rates.\n")
+        se <- rep(NaN, nrow(h))
+    } else {
+        se <- sqrt(diag(solve(h)))
+    }
+    se
+}
+
+ace <-
+  function(x, phy, type = "continuous",
+           method = if (type == "continuous") "REML" else "ML",
+           CI = TRUE, model = if (type == "continuous") "BM" else "ER",
+           scaled = TRUE, kappa = 1, corStruct = NULL, ip = 0.1,
+           use.expm = FALSE)
+{
+    if (!inherits(phy, "phylo"))
+        stop('object "phy" is not of class "phylo"')
     if (is.null(phy$edge.length))
         stop("tree has no branch lengths")
     type <- match.arg(type, c("continuous", "discrete"))
     nb.tip <- length(phy$tip.label)
     nb.node <- phy$Nnode
     if (nb.node != nb.tip - 1)
-      stop('"phy" is not rooted AND fully dichotomous.')
+        stop('"phy" is not rooted AND fully dichotomous.')
     if (length(x) != nb.tip)
-      stop("length of phenotypic and of phylogenetic data do not match.")
+        stop("length of phenotypic and of phylogenetic data do not match.")
     if (!is.null(names(x))) {
         if(all(names(x) %in% phy$tip.label))
           x <- x[phy$tip.label]
-        else warning('the names of argument "x" and the tip labels of the tree
-did not match: the former were ignored in the analysis.')
+        else warning("the names of 'x' and the tip labels of the tree do not match: the former were ignored in the analysis.")
     }
     obj <- list()
     if (kappa != 1) phy$edge.length <- phy$edge.length^kappa
     if (type == "continuous") {
-        if (method == "pic") {
+        switch(method, "REML" = {
+            minusLogLik <- function(sig2) {
+                if (sig2 < 0) return(1e100)
+                V <- sig2 * vcv(phy)
+                ## next three lines borrowed from dmvnorm() in 'mvtnorm'
+                distval <- stats::mahalanobis(x, center = mu, cov = V)
+                logdet <- sum(log(eigen(V, symmetric = TRUE, only.values = TRUE)$values))
+                (nb.tip * log(2 * pi) + logdet + distval)/2
+            }
+            mu <- rep(ace(x, phy, method="pic")$ace[1], nb.tip)
+            out <- nlm(minusLogLik, 1, hessian = TRUE)
+            sigma2 <- out$estimate
+            se_sgi2 <- sqrt(1/out$hessian)
+            tip <- phy$edge[, 2] <= nb.tip
+            minus.REML.BM <- function(p) {
+                x1 <- p[phy$edge[, 1] - nb.tip]
+                x2 <- numeric(length(x1))
+                x2[tip] <- x[phy$edge[tip, 2]]
+                x2[!tip] <- p[phy$edge[!tip, 2] - nb.tip]
+                -(-sum((x1 - x2)^2/phy$edge.length)/(2 * sigma2) -
+                  nb.node * log(sigma2))
+            }
+            out <- nlm(function(p) minus.REML.BM(p),
+                       p = rep(mu[1], nb.node), hessian = TRUE)
+            obj$resloglik <- -out$minimum
+            obj$ace <- out$estimate
+            names(obj$ace) <- nb.tip + 1:nb.node
+            obj$sigma2 <- c(sigma2, se_sgi2)
+            if (CI) {
+                se <- .getSEs(out)
+                tmp <- se * qt(0.025, nb.node)
+                obj$CI95 <- cbind(obj$ace + tmp, obj$ace - tmp)
+            }
+        }, "pic" = {
             if (model != "BM")
-              stop('the "pic" method can be used only with model = "BM".')
+                stop('the "pic" method can be used only with model = "BM".')
             ## See pic.R for some annotations.
             phy <- reorder(phy, "pruningwise")
             phenotype <- numeric(nb.tip + nb.node)
@@ -46,19 +93,17 @@ did not match: the former were ignored in the analysis.')
                       as.integer(CI), as.integer(scaled),
                       PACKAGE = "ape")
             obj$ace <- ans[[6]][-(1:nb.tip)]
-            names(obj$ace) <- (nb.tip + 1):(nb.tip + nb.node)
+            names(obj$ace) <- nb.tip + 1:nb.node
             if (CI) {
                 se <- sqrt(ans[[8]])
-                CI95 <- matrix(NA, nb.node, 2)
-                CI95[, 1] <- obj$ace + se * qnorm(0.025)
-                CI95[, 2] <- obj$ace - se * qnorm(0.025)
-                obj$CI95 <- CI95
+                tmp <- se * qnorm(0.025)
+                obj$CI95 <- cbind(obj$ace + tmp, obj$ace - tmp)
             }
-        }
-        if (method == "ML") {
+        }, "ML" = {
             if (model == "BM") {
                 tip <- phy$edge[, 2] <= nb.tip
                 dev.BM <- function(p) {
+                    if (p[1] < 0) return(1e100) # in case sigma² is negative
                     x1 <- p[-1][phy$edge[, 1] - nb.tip]
                     x2 <- numeric(length(x1))
                     x2[tip] <- x[phy$edge[tip, 2]]
@@ -71,26 +116,22 @@ did not match: the former were ignored in the analysis.')
                 obj$loglik <- -out$minimum / 2
                 obj$ace <- out$estimate[-1]
                 names(obj$ace) <- (nb.tip + 1):(nb.tip + nb.node)
-                se <- sqrt(diag(solve(out$hessian)))
+                se <- .getSEs(out)
                 obj$sigma2 <- c(out$estimate[1], se[1])
-                se <- se[-1]
                 if (CI) {
-                    CI95 <- matrix(NA, nb.node, 2)
-                    CI95[, 1] <- obj$ace + se * qt(0.025, nb.node)
-                    CI95[, 2] <- obj$ace - se * qt(0.025, nb.node)
-                    obj$CI95 <- CI95
+                    tmp <- se[-1] * qt(0.025, nb.node)
+                    obj$CI95 <- cbind(obj$ace + tmp, obj$ace - tmp)
                 }
             }
-        }
-        if (method == "GLS") {
+        }, "GLS" = {
             if (is.null(corStruct))
-              stop('you must give a correlation structure if method = "GLS".')
+                stop('you must give a correlation structure if method = "GLS".')
             if (class(corStruct)[1] == "corMartins")
-              M <- corStruct[1] * dist.nodes(phy)
+                M <- corStruct[1] * dist.nodes(phy)
             if (class(corStruct)[1] == "corGrafen")
-              phy <- compute.brlen(attr(corStruct, "tree"),
-                                   method = "Grafen",
-                                   power = exp(corStruct[1]))
+                phy <- compute.brlen(attr(corStruct, "tree"),
+                                     method = "Grafen",
+                                     power = exp(corStruct[1]))
             if (class(corStruct)[1] %in% c("corBrownian", "corGrafen")) {
                 dis <- dist.nodes(attr(corStruct, "tree"))
                 MRCA <- mrca(attr(corStruct, "tree"), full = TRUE)
@@ -107,16 +148,14 @@ did not match: the former were ignored in the analysis.')
             obj$ace <- drop(varAY %*% invV %*% (x - GM) + GM)
             names(obj$ace) <- (nb.tip + 1):(nb.tip + nb.node)
             if (CI) {
-                CI95 <- matrix(NA, nb.node, 2)
                 se <- sqrt((varA - varAY %*% invV %*% t(varAY))[cbind(1:nb.node, 1:nb.node)])
-                CI95[, 1] <- obj$ace + se * qnorm(0.025)
-                CI95[, 2] <- obj$ace - se * qnorm(0.025)
-                obj$CI95 <- CI95
+                tmp <- se * qnorm(0.025)
+                obj$CI95 <- cbind(obj$ace + tmp, obj$ace - tmp)
             }
-        }
+        })
     } else { # type == "discrete"
         if (method != "ML")
-          stop("only ML estimation is possible for discrete characters.")
+            stop("only ML estimation is possible for discrete characters.")
         if (!is.factor(x)) x <- factor(x)
         nl <- nlevels(x)
         lvls <- levels(x)
@@ -130,64 +169,68 @@ did not match: the former were ignored in the analysis.')
             }
             if (model == "SYM") {
                 np <- nl * (nl - 1)/2
-                rate[col(rate) < row(rate)] <- 1:np
+                sel <- col(rate) < row(rate)
+                rate[sel] <- 1:np
                 rate <- t(rate)
-                rate[col(rate) < row(rate)] <- 1:np
+                rate[sel] <- 1:np
             }
         } else {
             if (ncol(model) != nrow(model))
-              stop("the matrix given as `model' is not square")
+              stop("the matrix given as 'model' is not square")
             if (ncol(model) != nl)
-              stop("the matrix `model' must have as many rows
-as the number of categories in `x'")
+              stop("the matrix 'model' must have as many rows as the number of categories in 'x'")
             rate <- model
             np <- max(rate)
         }
         index.matrix <- rate
-        index.matrix[cbind(1:nl, 1:nl)] <- NA
-        rate[cbind(1:nl, 1:nl)] <- 0
-        rate[rate == 0] <- np + 1 # to avoid 0's since we will use this an numeric indexing
+        tmp <- cbind(1:nl, 1:nl)
+        index.matrix[tmp] <- NA
+        rate[tmp] <- 0
+        rate[rate == 0] <- np + 1 # to avoid 0's since we will use this as numeric indexing
 
         liks <- matrix(0, nb.tip + nb.node, nl)
-        for (i in 1:nb.tip) liks[i, x[i]] <- 1
+        TIPS <- 1:nb.tip
+        liks[cbind(TIPS, x)] <- 1
         phy <- reorder(phy, "pruningwise")
 
+        E <- if (use.expm) expm::expm else ape::matexpo
+
         Q <- matrix(0, nl, nl)
         dev <- function(p, output.liks = FALSE) {
+            if (any(is.nan(p)) || any(is.infinite(p))) return(1e50)
+            ## from Rich FitzJohn:
+            comp <- numeric(nb.tip + nb.node) # Storage...
             Q[] <- c(p, 0)[rate]
             diag(Q) <- -rowSums(Q)
             for (i  in seq(from = 1, by = 2, length.out = nb.node)) {
-                j <- i + 1
+                j <- i + 1L
                 anc <- phy$edge[i, 1]
                 des1 <- phy$edge[i, 2]
                 des2 <- phy$edge[j, 2]
-                tmp <- eigen(Q * phy$edge.length[i], symmetric = FALSE)
-                P1 <- tmp$vectors %*% diag(exp(tmp$values)) %*% solve(tmp$vectors)
-                tmp <- eigen(Q * phy$edge.length[j], symmetric = FALSE)
-                P2 <- tmp$vectors %*% diag(exp(tmp$values)) %*% solve(tmp$vectors)
-                liks[anc, ] <- P1 %*% liks[des1, ] * P2 %*% liks[des2, ]
+                v.l <- E(Q * phy$edge.length[i]) %*% liks[des1, ]
+                v.r <- E(Q * phy$edge.length[j]) %*% liks[des2, ]
+                v <- v.l * v.r
+                comp[anc] <- sum(v)
+                liks[anc, ] <- v/comp[anc]
             }
-            if (output.liks) return(liks[-(1:nb.tip), ])
-            - 2 * log(sum(liks[nb.tip + 1, ]))
+            if (output.liks) return(liks[-TIPS, ])
+            dev <- -2 * sum(log(comp[-TIPS]))
+            if (is.na(dev)) Inf else dev
         }
         out <- nlminb(rep(ip, length.out = np), function(p) dev(p),
-                      lower = rep(0, np), upper = rep(Inf, np))
+                      lower = rep(0, np), upper = rep(1e50, np))
         obj$loglik <- -out$objective/2
         obj$rates <- out$par
         oldwarn <- options("warn")
         options(warn = -1)
-        h <- nlm(function(p) dev(p), p = obj$rates, iterlim = 1,
-                 stepmax = 0, hessian = TRUE)$hessian
+        out.nlm <- nlm(function(p) dev(p), p = obj$rates, iterlim = 1,
+                       stepmax = 0, hessian = TRUE)
         options(oldwarn)
-        if (any(h == 0))
-          warning("The likelihood gradient seems flat in at least one dimension (gradient null):\ncannot compute the standard-errors of the transition rates.\n")
-        else obj$se <- sqrt(diag(solve(h)))
+        obj$se <- .getSEs(out.nlm)
         obj$index.matrix <- index.matrix
         if (CI) {
-            lik.anc <- dev(obj$rates, TRUE)
-            lik.anc <- lik.anc / rowSums(lik.anc)
-            colnames(lik.anc) <- lvls
-            obj$lik.anc <- lik.anc
+            obj$lik.anc <- dev(obj$rates, TRUE)
+            colnames(obj$lik.anc) <- lvls
         }
     }
     obj$call <- match.call()
@@ -220,8 +263,38 @@ anova.ace <- function(object, ...)
     table <- data.frame(ll, df, ddf, dev,
                         pchisq(dev, ddf, lower.tail = FALSE))
     dimnames(table) <- list(1:length(X), c("Log lik.", "Df",
-                                           "Df change", "Deviance",
+                                           "Df change", "Resid. Dev",
                                            "Pr(>|Chi|)"))
     structure(table, heading = "Likelihood Ratio Test Table",
               class = c("anova", "data.frame"))
 }
+
+print.ace <- function(x, digits = 4, ...)
+{
+    cat("\n    Ancestral Character Estimation\n\n")
+    cat("Call: ")
+    print(x$call)
+    cat("\n")
+    if (!is.null(x$loglik))
+        cat("    Log-likelihood:", x$loglik, "\n\n")
+    if (!is.null(x$resloglik))
+        cat("    Residual log-likelihood:", x$resloglik, "\n\n")
+    ratemat <- x$index.matrix
+    if (is.null(ratemat)) { # to be improved
+        class(x) <- NULL
+        x$resloglik <- x$loglik <- x$call <- NULL
+        print(x)
+    } else {
+        dimnames(ratemat)[1:2] <- dimnames(x$lik.anc)[2]
+        cat("Rate index matrix:\n")
+        print(ratemat, na.print = ".")
+        cat("\n")
+        npar <- length(x$rates)
+        estim <- data.frame(1:npar, round(x$rates, digits), round(x$se, digits))
+        cat("Parameter estimates:\n")
+        names(estim) <- c("rate index", "estimate", "std-err")
+        print(estim, row.names = FALSE)
+        cat("\nScaled likelihoods at the root (type '...$lik.anc' to get them for all nodes):\n")
+        print(x$lik.anc[1, ])
+    }
+}