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#' Geometric median tree function
#'
#' Finds the geometric median of a set of trees according to the Kendall Colijn metric.
#'
#' @author Jacob Almagro-Garcia \email{nativecoder@@gmail.com}
#' @author Michelle Kendall \email{michelle.louise.kendall@@gmail.com}
#' @author Thibaut Jombart \email{thibautjombart@@gmail.com}
#'
#' @param x A list of trees of the class multiPhylo, for which the median tree will be computed, \cr
#' OR a matrix of tree vectors as given by \code{treespace$vectors}.
#' @param groups an optional factor defining groups of trees; if provided, one median tree will be found for each group.
#' @param lambda a number in [0,1] which specifies the extent to which topology (default, with lambda=0) or branch lengths (lambda=1) are emphasised. This argument is ignored if \code{return.lambda.function=TRUE} or if the vectors are already supplied as the object \code{x}.
#' @param weights A vector of weights for the trees. Defaults to a vector of 1's so that all trees are equally weighted, but can be used to encode likelihood, posterior probabilities or other characteristics.
#' @param emphasise.tips an optional list of tips whose entries in the tree vectors should be emphasised. Defaults to \code{NULL}.
#' @param emphasise.weight applicable only if a list is supplied to \code{emphasise.tips}, this value (default 2) is the number by which vector entries corresponding to those tips are emphasised.
#' @param return.lambda.function If true, a function that can be invoked with different lambda values is returned.
#' This function returns the vector of metric values for the given lambda. Ignored if the tree vectors are already supplied as the object \code{x}.
#' @param save.memory A flag that saves a lot of memory but increases the execution time (not compatible with return.lambda.function=TRUE). Ignored if the tree vectors are already supplied as the object \code{x}.
#'
#' @return A list of five objects:
#' \itemize{
#' \item $centre is the "central vector", that is, the (weighted) mean of the tree vectors (which typically does not correspond to a tree itself);
#' \item $distances gives the distance of each tree from the central vector;
#' \item $mindist is the minimum of these distances;
#' \item $treenumbers gives the numbers (and, if supplied, names) of the "median tree(s)", that is, the tree(s) which achieve this minimum distance to the centre;
#' \item $trees if trees were supplied then this returns the median trees as a multiPhylo object.
#' }
#' If groups are provided, then one list is returned for each group.
#' If \code{return.lambda.function=TRUE} then a function is returned that produces this list for a given value of lambda.
#'
#'
#' @import ape
#'
#'
#' @examples
#'
#' ## EXAMPLE WITH WOODMICE DATA
#' data(woodmiceTrees)
#'
#' ## LOOKING FOR A SINGLE MEDIAN
#' ## get median tree(s)
#' res <- medTree(woodmiceTrees)
#' res
#'
#' ## plot first tree
#' med.tree <- res$trees[[1]]
#' plot(med.tree)
#'
#' ## LOOKING FOR MEDIANS IN SEVERAL CLUSTERS
#' ## identify 6 clusters
#' groves <- findGroves(woodmiceTrees, nf=3, nclust=6)
#'
#' ## find median trees
#' res.with.grp <- medTree(woodmiceTrees, groves$groups)
#'
#' ## there is one output per cluster
#' names(res.with.grp)
#'
#' ## get the first median of each
#' med.trees <- lapply(res.with.grp, function(e) ladderize(e$trees[[1]]))
#'
#' ## plot trees
#' par(mfrow=c(2,3))
#' for(i in 1:length(med.trees)) plot(med.trees[[i]], main=paste("cluster",i))
#'
#' ## highlight the differences between a pair of median trees
#' plotTreeDiff(med.trees[[1]],med.trees[[5]])
#'
#' @export
medTree <- function(x, groups=NULL, lambda=0, weights=NULL, emphasise.tips=NULL, emphasise.weight=2,
return.lambda.function=FALSE, save.memory=FALSE) {
## CHECK input type ##
if (inherits(x, "multiPhylo")) {
type <- "multiPhylo_object"
}
else if (inherits(x, "matrix")) {
# x is a matrix of tree vectors
type <- "tree_vectors"
}
else stop("x should be a multiphylo object or a matrix of tree vectors")
if (type=="multiPhylo_object") {
## DEFINE MAIN FUNCTION FINDING MEDIAN TREE ##
findMedianPhylo <- function(trees, weights){
## checks, general variables
num_trees <- length(trees)
num_leaves <- length(trees[[1]]$tip.label)
if(is.null(weights)) weights <- rep(1, num_trees)
if(length(weights)!=num_trees) stop("Length of vector of weights must be the same as number of trees")
## Working with numbers (no functions).
if(!return.lambda.function) {
## Here we speed up the computation by storing all vectors (a lot of memory for big trees).
if(!save.memory) {
## Compute the metric vector for all trees.
tree_metrics <- t(sapply(trees, function(tree) {treeVec(tree, lambda, emphasise.tips, emphasise.weight, return.lambda.function=F)}))
## Compute the centre metric vector by weighting the metric vector of each tree.
centre <- (weights %*% tree_metrics)/num_trees
## Distances to the centre.
distances <- apply(tree_metrics, 1, function(m){sqrt(sum((m-centre)^2))})
## Get the indices for the median tree(s).
min_distance <- min(distances)
median_trees <- which(min_distance == distances)
return(list(centre=centre, distances=distances, mindist=min_distance, treenumbers=median_trees, trees=trees[median_trees]))
}
## To save memory we recompute the vectors on the fly (way slower but we don't eat a ton of memory).
## We'll need a first pass to compute the centre and a second pass to compute distances.
else {
## First pass: compute the centre.
centre <- rep(0,(num_leaves*(num_leaves-1)/2) + num_leaves)
for(i in 1:num_trees) {
centre <- centre + treeVec(trees[[i]], lambda, F) * weights[i]
}
centre <- centre/num_trees
## Second pass: compute the distances.
distances <- rep(NA,num_trees)
for(i in 1:num_trees) {
distances[i] <- sqrt(sum((treeVec(trees[[i]], lambda, F) - centre)^2))
}
## Get the indices for the median tree(s).
min_distance <- min(distances)
median_trees <- which(min_distance == distances)
return(list(centre=centre, distances=distances, mindist=min_distance, treenumbers=median_trees, trees=trees[median_trees]))
}
}
## Working with functions.
else {
if(save.memory)
warning("save.memory=TRUE is incompatible with return.lambda.function=TRUE, setting save.memory=FALSE")
## Compute the list of metric functions for all trees.
tree_metric_functions <- sapply(trees, function(tree) {treeVec(tree, lambda, emphasise.tips, emphasise.weight, return.lambda.function=T)})
## Inner function that we'll return, computes the distance matrix given lambda.
compute_median_tree_function <- function(l) {
## Compute the tree metrics for the given lambda.
tree_metrics <- t(sapply(tree_metric_functions, function(tmf){tmf(l)}))
## Compute the centre metric vector by weighting the metric vector of each tree.
centre <- (weights %*% tree_metrics)/num_trees
## Distances to the centre.
distances <- apply(tree_metrics, 1, function(m){sqrt(sum((m-centre)^2))})
## Get the indices for the median tree(s).
min_distance <- min(distances)
median_trees <- which(min_distance == distances)
return(list(centre=centre, distances=distances, mindist=min_distance, treenumbers=median_trees, trees=trees[median_trees]))
}
return(compute_median_tree_function)
}
} # end findMedian
## APPLY FUNCTION TO TREES ##
if(is.null(groups)){ ## no groups provided
out <- findMedianPhylo(x, weights)
} else { ## groups provided
out <- tapply(x, groups, findMedianPhylo, weights)
}
} # end if multiPhylo object
if (type=="tree_vectors"){
## Can define a much simpler version of the function to find a median tree ##
findMedianVectors <- function(vectors, weights){
## checks, general variables
num_trees <- length(vectors[,1])
if(is.null(weights)) {weights <- rep(1,num_trees)}
if(length(weights)!=num_trees) stop("Length of vector of weights must be the same as number of tree vectors")
tree_metrics <- vectors
## Compute the centre metric vector by weighting the metric vector of each tree.
centre <- (weights %*% tree_metrics)/num_trees
## Distances to the centre.
distances <- apply(tree_metrics, 1, function(m){sqrt(sum((m-centre)^2))})
## Get the indices for the median tree(s).
min_distance <- min(distances)
median_trees <- which(min_distance == distances)
## Note we cannot return $trees because the trees were not supplied!
return(list(centre=centre, distances=distances, mindist=min_distance, treenumbers=median_trees))
} # end findMedianVectors
## APPLY FUNCTION TO TREES ##
if(is.null(groups)){ ## no groups provided
out <- findMedianVectors(x, weights)
} else { ## groups provided
# need to first convert the vector matrix into a list:
mylist <- lapply(1:length(x[,1]), function(a) x[a,])
# and then coerce back into matrix within the function.
# Room for improvement here!
out <- tapply(mylist, groups, function(a) {findMedianVectors(t(sapply(a, function(b) b)), weights)})
}
}
## RETURN ##
return(out)
} ## end medTree
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