Nothing
minEdge <- function(tree, tau=1e-8, enforce_ultrametric=FALSE){
if(tau<0) stop("tau must be >= 0!")
if(any(tree$edge.length < tau) || enforce_ultrametric){
rooted <- is.rooted(tree)
if(enforce_ultrametric) rooted <- TRUE
if(rooted){
nTip <- Ntip(tree)
ind <- seq_len(nTip)
nh <- nodeHeight(tree)[ind]
if(enforce_ultrametric) nh <- rep(0, nTip)
}
tree$edge.length[tree$edge.length < tau] <- tau
if(rooted){
el <- numeric(max(tree$edge))
el[tree$edge[,2]] <- tree$edge.length
nh2 <- nodeHeight(tree)[ind]
el[ind] <- el[ind] + (nh2 - nh)
if(any(el < tau)) el[ind] <- el[ind] + tau - min(el)
tree$edge.length <- el[tree$edge[,2]]
}
}
tree
}
#' @rdname phangorn-internal
#' @importFrom stats cor
#' @export
candidate_tree <- function(x, method=c("unrooted", "ultrametric", "tipdated"),
eps = 1e-8, tip.dates=NULL, ...){
method <- match.arg(method, c("unrooted", "ultrametric", "tipdated"))
enforce_ultrametric <- FALSE
if(method=="ultrametric"){
dm <- dist.ml(x, ...)
tree <- wpgma(dm)
enforce_ultrametric <- TRUE
}
if(method=="unrooted"){
tree <- pratchet(x, maxit=5L, trace=0, perturbation = "stochastic",
all=FALSE)
tree <- multi2di(tree)
tree <- unroot(tree)
tree <- acctran(tree, x)
tree$edge.length <- tree$edge.length / sum(attr(x, "weight"))
}
if(method=="tipdated"){
if(is.null(tip.dates)) stop("Argument tip.dates is missing!")
if(is.null(names(tip.dates))) names(tip.dates) <- names(x)
dm <- dist.ml(x, ...)
tree <- supgma(dm, tip.dates)
}
minEdge(tree, tau=eps, enforce_ultrametric=enforce_ultrametric)
}
# like is.ultrametric
check_tip_dates <- function(tree, tip.dates){
tip.dates <- tip.dates[tree$tip.label]
nh <- node.depth.edgelength(tree)[seq_along(tree$tip.label)]
isTRUE(all.equal(cor(tip.dates,nh), 1))
}
proper_tree <- function(x, tree, method=c("ultrametric", "tipdated"),
tip.dates=NULL, eps = 1e-8, ...){
method <- match.arg(method, c("ultrametric", "tipdated"))
if(!is.null(tree$edge.length)){
if(method=="ultrametric" && is.ultrametric(tree)) return(tree)
if(method=="tipdated" && check_tip_dates(tree, tip.dates)) return(tree)
}
dm <- dist.ml(x, ...)
nnls.tree(dm, tree, method)
}
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