-## ace.R (2009-03-22)
+## ace.R (2011-07-18)
-## Ancestral Character Estimation
+## Ancestral Character Estimation
-## Copyright 2005-2009 Emmanuel Paradis and Ben Bolker
+## Copyright 2005-2011 Emmanuel Paradis and Ben Bolker
## This file is part of the R-package `ape'.
## See the file ../COPYING for licensing issues.
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$edge.length))
stop("tree has no branch lengths")
type <- match.arg(type, c("continuous", "discrete"))
nb.tip <- length(phy$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$edge.length^kappa
if (type == "continuous") {
- if (method == "pic") {
+ switch(method, "REML" = {
+ minusLogLik <- function(sig2) {
+ if (sig2 < 0) return(1e100)
+ V <- sig2 * vcv(phy)
+ ## next three lines borrowed from dmvnorm() in 'mvtnorm'
+ distval <- mahalanobis(x, center = mu, cov = V)
+ logdet <- sum(log(eigen(V, symmetric = TRUE, only.values = TRUE)$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$ace) <- (nb.tip + 1):(nb.tip + nb.node)
+ obj$sigma2 <- c(sigma2, se_sgi2)
+ if (CI) {
+ se <- sqrt(diag(solve(out$hessian)))
+ tmp <- se * qt(0.025, nb.node)
+ obj$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)
names(obj$ace) <- (nb.tip + 1):(nb.tip + nb.node)
if (CI) {
se <- sqrt(ans[[8]])
- CI95 <- matrix(NA, nb.node, 2)
- CI95[, 1] <- obj$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]]
names(obj$ace) <- (nb.tip + 1):(nb.tip + nb.node)
se <- sqrt(diag(solve(out$hessian)))
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)
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)
}
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")
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$edge.length[j]) %*% liks[des2, ]
+ v <- v.l * v.r
+ comp[anc] <- sum(v)
+ liks[anc, ] <- v/comp[anc]
}
- if (output.liks) return(liks[-(1:nb.tip), ])
- - 2 * log(sum(liks[nb.tip + 1, ]))
+ if (output.liks) return(liks[-TIPS, ])
+ dev <- -2 * sum(log(comp[-TIPS]))
+ if (is.na(dev)) Inf else dev
}
out <- nlminb(rep(ip, length.out = np), function(p) dev(p),
- lower = rep(0, np), upper = rep(Inf, np))
+ lower = rep(0, np), upper = rep(1e50, np))
obj$loglik <- -out$objective/2
obj$rates <- out$par
oldwarn <- options("warn")
else obj$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$call <- match.call()
table <- data.frame(ll, df, ddf, dev,
pchisq(dev, ddf, lower.tail = FALSE))
dimnames(table) <- list(1:length(X), c("Log lik.", "Df",
- "Df change", "Deviance",
+ "Df change", "Resid. Dev",
"Pr(>|Chi|)"))
structure(table, heading = "Likelihood Ratio Test Table",
class = c("anova", "data.frame"))
}
+
+print.ace <- function(x, digits = 4, ...)
+{
+ cat("\n Ancestral Character Estimation\n\n")
+ cat("Call: ")
+ print(x$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, ])
+ }
+}