X-Git-Url: https://git.donarmstrong.com/?a=blobdiff_plain;f=R%2Face.R;h=33c72de5d72c60039bcdf7e4ac1ec2a7e055cae2;hb=2741f6e9f61e33c7b499f27c47604606d08f4bea;hp=c38faeb6bdb2ef2b8b489139a841b3b5a440adaf;hpb=6527122e02cccd1449de4ea9c2498075ad405e69;p=ape.git diff --git a/R/ace.R b/R/ace.R index c38faeb..33c72de 100644 --- a/R/ace.R +++ b/R/ace.R @@ -1,8 +1,8 @@ -## ace.R (2009-01-19) +## ace.R (2010-05-12) -## Ancestral Character Estimation +## Ancestral Character Estimation -## Copyright 2005-2009 Emmanuel Paradis and Ben Bolker +## Copyright 2005-2010 Emmanuel Paradis and Ben Bolker ## This file is part of the R-package `ape'. ## See the file ../COPYING for licensing issues. @@ -11,8 +11,8 @@ 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) { - if (class(phy) != "phylo") - stop('object "phy" is not of class "phylo".') + if (!inherits(phy, "phylo")) + stop('object "phy" is not of class "phylo".') if (is.null(phy$edge.length)) stop("tree has no branch lengths") type <- match.arg(type, c("continuous", "discrete")) @@ -25,8 +25,7 @@ ace <- function(x, phy, type = "continuous", method = "ML", CI = TRUE, if (!is.null(names(x))) { if(all(names(x) %in% phy$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$edge.length^kappa @@ -59,6 +58,7 @@ did not match: the former were ignored in the analysis.') if (model == "BM") { tip <- phy$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]] @@ -130,9 +130,10 @@ 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)) @@ -144,45 +145,53 @@ as the number of categories in `x'") 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") 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$vectors) - liks[anc, ] <- P1 %*% liks[des1, ] * P2 %*% liks[des2, ] + v.l <- matexpo(Q * phy$edge.length[i]) %*% liks[des1, ] + v.r <- matexpo(Q * phy$edge.length[j]) %*% liks[des2, ] + v <- v.l * v.r + comp[anc] <- sum(v) + liks[anc, ] <- v/comp[anc] } - if (output.liks) return(liks[-(1:nb.tip), ]) - - 2 * log(sum(liks[nb.tip + 1, ])) + if (output.liks) return(liks[-TIPS, ]) + -2 * sum(log(comp[-TIPS])) } - out <- nlm(function(p) dev(p), p = rep(ip, length.out = np), - hessian = TRUE) - obj$loglik <- -out$minimum / 2 - obj$rates <- out$estimate - if (any(out$gradient == 0)) + out <- nlminb(rep(ip, length.out = np), function(p) dev(p), + 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 + 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$se <- sqrt(diag(solve(out$hessian))) + else obj$se <- sqrt(diag(solve(h))) obj$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() @@ -215,8 +224,36 @@ 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$loglik, "\n\n") + ratemat <- x$index.matrix + if (is.null(ratemat)) { # to be improved + class(x) <- NULL + 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, ]) + } +}