Nothing
## Extracting a specific rarefaction level
extract.disparity.values <- function(subsets, data, rarefaction, concatenate) {
## Get the rarefaction level
if(rarefaction != FALSE) {
rarefaction = as.numeric(which(lapply(data$subsets[[subsets]][-1], nrow) == rarefaction) + 1)
if(length(rarefaction) == 0) {
## No rarefaction level for this subset
return(NULL)
}
} else {
rarefaction = 2
}
if(concatenate) {
return(list(as.numeric(data$disparity[[subsets]][[rarefaction]])))
} else {
return(lapply(seq_len(ncol(data$disparity[[subsets]][[rarefaction]])), function(col) data$disparity[[subsets]][[rarefaction]][,col]))
}
}
## Remove nulls from a list
clean.list <- function(list) {
nulls <- unlist(lapply(list, is.null))
return(list[!nulls])
}
## Recursive sorting
recursive.sort <- function(data, sort) {
return(data[sort])
}
## Merging two subsets
merge.two.subsets <- function(subs1, subs2, data) {
## Get the list of new subsets
new_subset <- list("elements" = matrix(unique(c(data$subsets[[subs1]]$elements, data$subsets[[subs2]]$elements, ncol = 1))))
## Replace the second subset with the new one
data$subsets[[subs2]] <- new_subset
## Rename it
names(data$subsets)[subs2] <- paste(names(data$subsets)[subs1], names(data$subsets)[subs2], sep = "-")
## Remove the former
data$subsets[[subs1]] <- NULL
return(data)
}
## Check subset availability
check.subsets <- function(subsets, data) {
if(!is.null(data$call$disparity) && data$call$disparity$metrics$between.groups) {
## Numeric subsets
if(is(subsets, "numeric") || is(subsets, "integer")) {
if(any(na_subsets <- is.na(match(subsets, 1:length(data$disparity))))) {
## Subsets not found
stop(paste0(ifelse(length(which(na_subsets)) > 1, "Subsets ", "Subset "), paste0(subsets[which(na_subsets)], collapse = ", "), " not found."))
}
} else {
if(is(subsets, "character")) {
## Get the subset names (searched and available)
subset_search <- unique(unlist(strsplit(subsets, split = ":")))
subset_available <- unique(unlist(strsplit(names(data$disparity), split = ":")))
## Check if the searched ones exist
if(any(na_subsets <- is.na(match(subset_search, subset_available)))) {
## Subsets not found
stop(paste0(ifelse(length(which(na_subsets)) > 1, "Subsets ", "Subset "), paste0(subsets[which(na_subsets)], collapse = ", "), " not found."))
}
} else {
stop("subsets argument must be of class \"numeric\" or \"character\".")
}
}
} else {
if(is(subsets, "list")) {
## Flatten the list for checking for subsets
subsets <- unique(unlist(subsets))
}
if(length(subsets) > length(data$subsets)) {
stop("Not enough subsets in the original data.")
} else {
if(is(subsets, "numeric") || is(subsets, "integer")) {
if(any(na_subsets <- is.na(match(subsets, 1:length(data$subsets))))) {
## Subsets not found
stop(paste0(ifelse(length(which(na_subsets)) > 1, "Subsets ", "Subset "), paste0(subsets[which(na_subsets)], collapse = ", "), " not found."))
}
} else {
if(is(subsets, "character")) {
if(any(is.na(match(subsets, names(data$subsets))))) {
subsets <- subsets[which(is.na(match(subsets, names(data$subsets))))]
orthograph <- ifelse(length(subsets) == 1, "Subset ", "Subsets ")
stop(paste0(orthograph, paste0(subsets, collapse = ", "), " not found."))
}
} else {
stop("subsets argument must be of class \"numeric\" or \"character\".")
}
}
}
}
}
## Detecting the bin age lower or greater than a value
detect.bin.age <- function(data, value, greater = FALSE) {
## Detect the bin before the extinction time
bin_times <- unlist(sapply(names(data$subsets), strsplit, split = " - ", simplify = FALSE), recursive = FALSE)
## Detecting the bin age
detect.bin.ages.lapply <- function(one_bin, value, greater) {
if(greater) {
return(ifelse(as.numeric(one_bin)[2] >= value, TRUE, FALSE))
} else {
return(ifelse(as.numeric(one_bin)[1] <= value, TRUE, FALSE))
}
}
return(unlist(lapply(bin_times, detect.bin.ages.lapply, value, greater)))
}
## Adding dimnames (if necessary)
add.dimnames <- function(one_output, one_subset, data) {
input <- data$matrix[[1]][data$subsets[[one_subset]]$elements, data$call$dimensions]
return(
switch(as.character(sum(which(dim(input) %in% length(one_output)))),
## No matching dim
"0" = one_output,
## Matching rows
"1" = {names(one_output) <- rownames(input); one_output},
## Matching cols
"2" = {names(one_output) <- colnames(input); one_output},
## Matching both (use rows as default)
"3" = {names(one_output) <- rownames(input); one_output}
)
)
}
## Detect the edges containing the requested elements
detect.edges <- function(tree, elements, to.root) {
## Detect which edges to keep
selected_edges <- match(elements, tree$edge[, 2])
## Has an NA (grabbed the root)
if(any(to_drop <- is.na(selected_edges))) {
## Drop the root
selected_edges <- selected_edges[!to_drop]
## Manually set the root
root <- Ntip(tree) + 1
} else {
if(to.root) {
## Manually set the root
root <- Ntip(tree) + 1
} else {
## Get the local root
root <- getMRCA(tree, elements)
}
}
## Get the nodes to test if all edges connect
test_nodes <- tree$edge[selected_edges, 1]
## Remove the root
if(root %in% test_nodes) {
test_nodes <- test_nodes[-c(which(test_nodes == root))]
}
## Check each node
while(length(test_nodes) != 0) {
if(test_nodes[1] %in% tree$edge[selected_edges, 2]) {
## Node is OK
test_nodes <- test_nodes[-1]
} else {
## Node is not connected
selected_edges <- c(selected_edges, which(tree$edge[,2] == test_nodes[1]))
## Get the nodes to test if all edges connect
test_nodes <- tree$edge[selected_edges, 1]
## Remove the root
if(root %in% test_nodes) {
test_nodes <- test_nodes[-c(which(test_nodes == root))]
}
}
}
return(selected_edges)
}
## Get the tree containing requested elements
get.new.tree <- function(elements, tree, to.root) {
## Detect which edges to keep
new_edges <- unique(detect.edges(tree, elements, to.root))
if(length(new_edges) < 2) {
return(NULL)
}
## Tracking the new tree
tree_track <- as.data.frame(tree$edge)
tree_track <- cbind(tree_track, is.tip = tree_track[,2] <= Ntip(tree))
tree_track <- cbind(tree_track, label = c(tree$tip.label,tree$node.label)[tree_track[,2]])
tree_track <- cbind(tree_track, selected = 1:nrow(tree_track) %in% new_edges)
## Get the root node name
root_lab <- tree$node.label[min(tree_track[tree_track$selected, 1])-Ntip(tree)]
## Build the new tree
new_tree <- list(edge = matrix(unlist(tree_track[tree_track$selected, c(1,2)]), ncol = 2),
edge.length = tree$edge.length[new_edges])
## Update tips (some nodes become tips)
new_tips <- c(tree$tip.label,tree$node.label)[tree$edge[new_edges, 2][!(tree$edge[new_edges, 2] %in% tree$edge[new_edges ,1])]]
tree_track$is.tip[tree_track$label %in% new_tips] <- TRUE
## Convert the edge numbers
new_edge_table <- tree_track[tree_track$selected, ]
## Sort the node values
all_nodes <- c(new_edge_table[, 1], new_edge_table[!new_edge_table$is.tip, 2])
all_nodes <- match(all_nodes, unique(all_nodes)) + sum(new_edge_table$is.tip)
## Update the table nodes
new_edge_table[, 1] <- all_nodes[1:nrow(new_edge_table)]
new_edge_table[!new_edge_table$is.tip, 2] <- all_nodes[-c(1:nrow(new_edge_table))]
## Update the table tips
new_edge_table[new_edge_table$is.tip, 2] <- 1:sum(new_edge_table$is.tip)
## Update the edge table
new_tree$edge <- matrix(unlist(new_edge_table[, c(1,2)]), ncol = 2)
## Update the Nnodes
new_tree$Nnode <- sum(!new_edge_table$is.tip) + 1 #+1 is for the root
## Update the tip labels
new_tree$tip.label <- new_edge_table$label[new_edge_table$is.tip]
## Update the node labels
new_tree$node.label <- c(root_lab, new_edge_table$label[!new_edge_table$is.tip])
## Update root.time
if(!is.null(tree$root.time)) {
if(to.root) {
new_tree$root.time <- tree$root.time
} else {
ages <- tree.age(tree)
## Not necessary the first node label?
new_tree$root.time <- ages$ages[which(ages$elements == new_tree$node.label[1])]
}
}
class(new_tree) <- "phylo"
return(new_tree)
}
## Toggle an output to phylo or multiphylo
toggle.multiphylo.list <- function(x) {
if(is(x, "list")) {
## Check if the elements are phylo
elements_class <- unlist(lapply(x, class))
if(length(elements_class) == 1 && elements_class == "phylo") {
return(x[[1]])
} else {
if(length(all_elems <- unique(elements_class)) == 1) {
if(all_elems == "phylo") {
x <- "multiPhylo"
return(x)
}
}
}
}
return(x)
}
## Return a subseted tree
get.one.tree.subset <- function(one_subset, one_tree, to.root) {
## Normal behaviour
output <- lapply(one_subset, function(x, tree, to.root) apply(x, 2, function(x, tree, to.root) get.new.tree(elements = x, tree = tree, to.root = to.root), tree = tree, to.root = to.root), tree = one_tree, to.root = to.root)
## Change the output objects
output <- lapply(output, toggle.multiphylo.list)
return(output)
}
## Slide nodes from the root
#@param bin_age the ages of the bin limits
#@param tree the original tree
#@return a tree with all old nodes slided
slide.node.root <- function(bin_age, tree) {
## Get the age to slide the nodes to
time <- bin_age[1]
## Get all ages
tree_ages <- tree.age(tree)
## Get the yongest age for late
younger <- min(tree_ages$ages)
## Remove the older tips
to_drop <- tree_ages$elements[which(tree_ages[tree_ages$elements %in% tree$tip.label, ]$ages > time)]
tree <- drop.tip(tree, tip = to_drop)
## Update the root time
new_ages <- tree.age(tree)$ages
tree$root.time <- max(new_ages) + (younger - min(new_ages))
# warning("DEBUG slide.node.root")
# plot(tree, main = "dropped old tips") ; nodelabels(); axisPhylo()
# abline(v = 3-c(2, 1), col = "red", lty = 1)
## Recalculate the ages for the nodes
node_ages <- tree.age(tree)
younger <- min(node_ages$ages)
node_ages <- node_ages[!(node_ages$elements %in% tree$tip.label), ]
## Get the nodes that are older than the time
nodes_to_slide <- which(node_ages$ages > time)
## reorder the nodes to slide by age
nodes_to_slide <- nodes_to_slide[match(sort(node_ages$ages[nodes_to_slide]), node_ages$ages[nodes_to_slide])]
## Recursively slide all nodes
while(length(nodes_to_slide) > 0) {
## Get the node name and sliding value
sliding_value <- node_ages$ages[nodes_to_slide[1]] - time
node_name <- node_ages$elements[nodes_to_slide[1]]
## slide it!
tree <- slide.nodes(node_name, tree, slide = sliding_value, allow.negative.root = TRUE)
## Update the root time
new_ages <- tree.age(tree)$ages
tree$root.time <- max(new_ages) + (younger - min(new_ages))
## Update the list of nodes to slide
nodes_to_slide <- nodes_to_slide[-1]
# warning("DEBUG slide.node.root")
# plot(tree, main = "slid one node") ; nodelabels(); axisPhylo()
# abline(v = 3-c(2, 1), col = "red", lty = 1)
}
return(tree)
}
## Get the subset of trees for one tree in an interval
get.interval.subtrees <- function(one_tree, bin_ages, to.root) {
## Slice the right sides of the trees
slice.one.tree <- function(age, tree) {
slice.tree(tree, age[2], model = "acctran", keep.all.ancestors = TRUE)
}
subset_subtrees <- lapply(bin_ages, slice.one.tree, one_tree) # TODO need fix for multiphylo
if(!to.root) {
## Compressing the root of the tree up until the slice lower boundary
subset_subtrees <- mapply(slide.node.root, bin_ages, subset_subtrees, SIMPLIFY = FALSE)
}
## Name the subsets
names(subset_subtrees) <- names(bin_ages)
return(subset_subtrees)
}
## Get the trees from slices
get.slice.subsets <- function(one_subset, data, to.root) {
## ONLY FOR ELEMENTS FOR NOW
subset <- one_subset$elements
## Handeling split slices
if(!is.null(data$call$subsets[[2]]) && length(grep("split", data$call$subsets[[2]])) > 0) {
## Sample the probabilities and collapse the matrix
sample.x <- function(x) {sample(x[1:2], 1, prob = c(x[3], 1-x[3]))}
sampled_subset <- matrix(apply(subset[, 1:3, drop = FALSE], 1, sample.x), ncol = 1)
## remove the sampled
subset <- subset[, -c(1:3), drop = FALSE]
## Sample for multiple trees
while(ncol(subset) > 0) {
sampled_subset <- cbind(sampled_subset, apply(subset[, 1:3, drop = FALSE], 1, sample.x))
subset <- subset[, -c(1:3), drop = FALSE]
}
subset <- sampled_subset
}
trees_list <- data$tree
trees_out <- list()
while(ncol(subset) > 0) {
## Extract the elements for the tree recursively
new_tree <- get.new.tree(subset[,1], trees_list[[1]], to.root)
if(is.null(new_tree)) {
new_tree <- list(NULL)
}
trees_out[[length(trees_out) + 1]] <- new_tree
## Remove the tree and the subset row
subset <- subset[, -1, drop = FALSE]
trees_list[1] <- NULL
}
## Handle the tree output
if(length(trees_out) > 1) {
class(trees_out) <- "multiPhylo"
return(trees_out)
} else {
return(trees_out[[1]])
}
}
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