-## ace.R (2009-01-19)
+## ace.R (2010-12-08)
-## 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.
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$edge.length))
stop("tree has no branch lengths")
type <- match.arg(type, c("continuous", "discrete"))
if (!is.null(names(x))) {
if(all(names(x) %in% phy$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$edge.length^kappa
if (model == "BM") {
tip <- phy$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]]
}
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))
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$edge.length[j]) %*% liks[des2, ]
+ v <- v.l * v.r
+ comp[anc] <- sum(v)
+ liks[anc, ] <- v/comp[anc]
}
- if (output.liks) return(liks[-(1:nb.tip), ])
- - 2 * log(sum(liks[nb.tip + 1, ]))
+ if (output.liks) return(liks[-TIPS, ])
+ dev <- -2 * sum(log(comp[-TIPS]))
+ if (is.na(dev)) Inf else dev
}
- out <- nlm(function(p) dev(p), p = rep(ip, length.out = np),
- hessian = TRUE)
- obj$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()
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, ])
+ }
+}
projects / ape.git / blobdiff