R/densityHeatmap.R
In zhongmicai/complexHeatmap: Make Complex Heatmaps
# == title
# Visualize Density Distribution by Heatmap
#
# == param
# -data A matrix or a list. If it is a matrix, density is calculated by columns.
# -density_param Parameters send to `stats::density`, ``na.rm`` is enforced to be ``TRUE``.
# -col A vector of colors that density values are mapped to.
# -color_space The color space in which colors are interpolated. Pass to `circlize::colorRamp2`.
# -ylab Label on y-axis.
# -column_title Title of the heatmap.
# -title Same as ``column_title``.
# -ylim Ranges on the y-axis.
# -range Same as ``ylim``.
# -title_gp Graphic parameters for title.
# -ylab_gp Graphic parameters for y-labels.
# -tick_label_gp Graphic parameters for y-ticks.
# -quantile_gp Graphic parameters for the quantiles.
# -column_order Order of columns.
# -column_names_side Pass to `Heatmap`.
# -show_column_names Pass to `Heatmap`.
# -column_names_max_height Pass to `Heatmap`.
# -column_names_gp Pass to `Heatmap`.
# -column_names_rot Pass to `Heatmap`.
# -cluster_columns Whether cluster columns?
# -clustering_distance_columns There is a specific distance method ``ks`` which is the Kolmogorov-Smirnov statistic between two distributions.
# For other methods, the distance is calculated on the density matrix.
# -clustering_method_columns Pass to `Heatmap`.
# -mc.cores Multiple cores for calculating ks distance.
# -... Pass to `Heatmap`.
#
# == details
# To visualize data distribution in a matrix or in a list, we normally use
# boxplot or violinplot. We can also use colors to map the density values and
# visualize distribution of values through a heatmap. It is useful if you have
# huge number of columns in ``data`` to visualize.
#
# The density matrix is generated with 500 rows ranging between the maximun
# and minimal values in all densities.
#
# == value
# A `Heatmap-class` object. It can oly add other heatmaps/annotations vertically.
#
# == seealso
# https://jokergoo.github.io/ComplexHeatmap-reference/book/other-high-level-plots.html#density-heatmap
#
# == author
# Zuguang Gu <z.gu@dkfz.de>
#
# == example
# matrix = matrix(rnorm(100), 10); colnames(matrix) = letters[1:10]
# densityHeatmap(matrix)
#
# lt = list(rnorm(10), rnorm(10))
# densityHeatmap(lt)
#
# ha = HeatmapAnnotation(points = anno_points(runif(10)),
# anno = rep(c("A", "B"), each = 5), col = list(anno = c("A" = "red", "B" = "blue")))
# densityHeatmap(matrix, top_annotation = ha)
# densityHeatmap(matrix, top_annotation = ha) \%v\% Heatmap(matrix, height = unit(6, "cm"))
densityHeatmap = function(data,
density_param = list(na.rm = TRUE),
col = rev(brewer.pal(11, "Spectral")),
color_space = "LAB",
ylab = deparse(substitute(data)),
column_title = paste0("Density heatmap of ", deparse(substitute(data))),
title = column_title,
ylim = c(-Inf, Inf),
range = ylim,
title_gp = gpar(fontsize = 14),
ylab_gp = gpar(fontsize = 12),
tick_label_gp = gpar(fontsize = 10),
quantile_gp = gpar(fontsize = 10),
column_order = NULL,
column_names_side = "bottom",
show_column_names = TRUE,
column_names_max_height = unit(6, "cm"),
column_names_gp = gpar(fontsize = 12),
column_names_rot = 90,
cluster_columns = FALSE,
clustering_distance_columns = "ks",
clustering_method_columns = "complete",
mc.cores = 1,
...) {
arg_list = list(...)
if(length(arg_list)) {
if(any(c("row_km", "row_split", "split", "km") %in% names(arg_list))) {
stop_wrap("density heatmaps do not allow row splitting.")
}
if(any(grepl("row", names(arg_list)))) {
stop_wrap("density heatmaps do not allow to set rows.")
}
if("anno" %in% names(arg_list)) {
stop_wrap("`anno` is removed from the argument. Please directly construct a `HeatmapAnnotation` object and set to `top_annotation` or `bottom_annotation`.")
}
}
ylab = ylab
column_title = column_title
density_param$na.rm = TRUE
if(!is.matrix(data) && !is.data.frame(data) && !is.list(data)) {
stop_wrap("only matrix and list are allowed.")
}
if(is.matrix(data)) {
data2 = as.list(as.data.frame(data))
names(data2) = colnames(data)
data = data2
}
density_list = lapply(data, function(x) do.call(density, c(list(x = x), density_param)))
quantile_list = sapply(data, quantile, na.rm = TRUE)
mean_value = sapply(data, mean, na.rm = TRUE)
n = length(density_list)
nm = names(density_list)
max_x = quantile(unlist(lapply(density_list, function(x) x$x)), 0.99)
min_x = quantile(unlist(lapply(density_list, function(x) x$x)), 0.01)
max_x = min(max_x, range[2])
min_x = max(min_x, range[1])
x = seq(min_x, max_x, length = 500)
mat = lapply(density_list, function(r) {
f = approxfun(r$x, r$y)
res = f(x)
res[is.na(res)] = 0
rev(res)
})
mat = as.matrix(as.data.frame(mat))
colnames(mat) = nm
if(cluster_columns) {
if(clustering_distance_columns == "ks") {
d = ks_dist(mat, mc.cores = mc.cores)
hc = hclust(d, clustering_method_columns)
cluster_columns = hc
}
}
col = colorRamp2(seq(0, max(mat, na.rm = TRUE), length = length(col)), col, space = color_space)
bb = grid.pretty(c(min_x, max_x))
ht = Heatmap(mat, col = col, name = "density",
column_title = title,
column_title_gp = title_gp,
cluster_rows = FALSE,
cluster_columns = cluster_columns,
clustering_distance_columns = clustering_distance_columns,
clustering_method_columns = clustering_method_columns,
column_dend_reorder = mean_value,
column_names_side = column_names_side,
show_column_names = show_column_names,
column_names_max_height = column_names_max_height,
column_names_gp = column_names_gp,
column_names_rot = column_names_rot,
column_order = column_order,
left_annotation = rowAnnotation(axis = anno_empty(border = FALSE,
width = grobHeight(textGrob(ylab, gp = ylab_gp))*2 + max_text_width(bb, gp = tick_label_gp) + unit(4, "mm")),
show_annotation_name = FALSE),
right_annotation = rowAnnotation(quantile = anno_empty(border = FALSE,
width = grobWidth(textGrob("100%", gp = quantile_gp)) + unit(6, "mm")),
show_annotation_name = FALSE),
...
)
random_str = paste(sample(c(letters, LETTERS, 0:9), 8), collapse = "")
ht@name = paste0(ht@name, "_", random_str)
names(ht@left_annotation) = paste0(names(ht@left_annotation), "_", random_str)
names(ht@right_annotation) = paste0(names(ht@right_annotation), "_", random_str)
post_fun = function(ht) {
column_order = column_order(ht)
if(!is.list(column_order)) {
column_order = list(column_order)
}
n_slice = length(column_order)
decorate_annotation(paste0("axis_", random_str), {
grid.text(ylab, x = grobHeight(textGrob(ylab, gp = ylab_gp)), rot = 90)
}, slice = 1)
for(i_slice in 1:n_slice) {
decorate_heatmap_body(paste0("density_", random_str), {
n = length(column_order[[i_slice]])
pushViewport(viewport(xscale = c(0.5, n + 0.5), yscale = c(min_x, max_x), clip = TRUE))
for(i in seq_len(5)) {
grid.lines(1:n, quantile_list[i, column_order[[i_slice]] ], default.units = "native", gp = gpar(lty = 2))
}
grid.lines(1:n, mean_value[ column_order[[i_slice]] ], default.units = "native", gp = gpar(lty = 2, col = "darkred"))
upViewport()
}, column_slice = i_slice)
}
decorate_heatmap_body(paste0("density_", random_str), {
pushViewport(viewport(yscale = c(min_x, max_x), clip = FALSE))
grid.rect(gp = gpar(fill = NA))
grid.yaxis(gp = tick_label_gp)
upViewport()
}, column_slice = 1)
decorate_heatmap_body(paste0("density_", random_str), {
n = length(column_order[[n_slice]])
lq = !apply(quantile_list, 1, function(x) all(x > max_x) || all(x < min_x))
lq = c(lq, !(all(mean_value > max_x) || all(mean_value < min_x)))
if(sum(lq) == 0) {
return(NULL)
}
labels = c(rownames(quantile_list), "mean")
y = c(quantile_list[, column_order[[n_slice]][n] ], mean_value[ column_order[[n_slice]][n] ])
labels = labels[lq]
y = y[lq]
od = order(y)
y = y[od]
labels = labels[od]
pushViewport(viewport(xscale = c(0.5, n + 0.5), yscale = c(min_x, max_x), clip = FALSE))
text_height = convertHeight(grobHeight(textGrob(labels[1])) * (1 + 0.2), "native", valueOnly = TRUE)
h1 = y - text_height*0.5
h2 = y + text_height*0.5
pos = rev(smartAlign(h1, h2, c(min_x, max_x)))
h = (pos[, 1] + pos[, 2])/2
link_width = unit(6, "mm")
n2 = length(labels)
grid.text(labels, unit(1, "npc") + rep(link_width, n2), h, default.units = "native", just = "left", gp = quantile_gp)
link_width = link_width - unit(1, "mm")
ly = y <= max_x & y >= min_x
if(sum(ly)) {
grid.segments(unit(rep(1, n2), "npc")[ly], y[ly], unit(1, "npc") + rep(link_width * (1/3), n2)[ly], y[ly], default.units = "native")
grid.segments(unit(1, "npc") + rep(link_width * (1/3), n2)[ly], y[ly], unit(1, "npc") + rep(link_width * (2/3), n2)[ly], h[ly], default.units = "native")
grid.segments(unit(1, "npc") + rep(link_width * (2/3), n2)[ly], h[ly], unit(1, "npc") + rep(link_width, n2)[ly], h[ly], default.units = "native")
}
upViewport()
}, column_slice = n_slice)
}
ht@heatmap_param$post_fun = post_fun
ht_list = ht %v% NULL
return(ht_list)
}
# https://stackoverflow.com/a/29853834/3425904
ks_dist_pair = function(x, y) {
# if(length(x) > 5000) x = sample(x, 5000)
# if(length(y) > 5000) y = sample(y, 5000)
n <- length(x)
n.x <- as.double(n)
n.y <- length(y)
n <- n.x * n.y/(n.x + n.y)
w <- c(x, y)
z <- cumsum(ifelse(order(w) <= n.x, 1/n.x, -1/n.y))
max(abs(z))
}
# data: a list or a matrix
ks_dist = function(data, mc.cores = 1) {
has_names = TRUE
if(is.matrix(data)) {
has_names = !is.null(colnames(data))
data = as.data.frame(data)
}
nc = length(data)
if(.Platform$OS.type == "windows") {
if(mc.cores > 1) {
message("parallel::mclapply() does not support multiple cores on Windows. mc.cores is reset to 1.")
mc.cores = 1
}
}
ind_mat = expand.grid(seq_len(nc), seq_len(nc))
ind_mat = ind_mat[ ind_mat[, 1] > ind_mat[, 2], , drop = FALSE]
v = mclapply(seq_len(nrow(ind_mat)), function(ind) {
i = ind_mat[ind, 1]
j = ind_mat[ind, 2]
suppressWarnings(d <- ks_dist_pair(data[[i]], data[[j]]))
return(d)
}, mc.cores = mc.cores)
v = unlist(v)
i = ind_mat[, 1]
j = ind_mat[, 2]
ind = (j - 1) * nc + i
d = matrix(0, nrow = nc, ncol = nc)
if(has_names) rownames(d) = colnames(d) = names(data)
d[ind] = v
as.dist(d)
}
# m = matrix(rnorm(200), nc = 10)
# ks_dist(m, mc.cores = 1)
# ks_dist(m, mc.cores = 2)
# ks_dist_1(m)
# lt = lapply(1:10, function(i) rnorm(runif(1, min = 10, max = 20)))
# ks_dist(lt, mc.cores = 1)
# ks_dist(lt, mc.cores = 2)
# ks_dist_1(lt)
ks_dist_1 = function(data) {
has_names = TRUE
if(is.matrix(data)) {
has_names = !is.null(colnames(data))
data = as.data.frame(data)
}
nc = length(data)
d = matrix(NA, nrow = nc, ncol = nc)
if(has_names) rownames(d) = colnames(d) = names(data)
for(i in 2:nc) {
for(j in 1:(nc-1)) {
suppressWarnings(d[i, j] <- ks_dist_pair(data[[i]], data[[j]]))
}
}
as.dist(d)
}
zhongmicai/complexHeatmap documentation built on May 7, 2019, 6:11 a.m.
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# == title
# Visualize Density Distribution by Heatmap
#
# == param
# -data A matrix or a list. If it is a matrix, density is calculated by columns.
# -density_param Parameters send to `stats::density`, ``na.rm`` is enforced to be ``TRUE``.
# -col A vector of colors that density values are mapped to.
# -color_space The color space in which colors are interpolated. Pass to `circlize::colorRamp2`.
# -ylab Label on y-axis.
# -column_title Title of the heatmap.
# -title Same as ``column_title``.
# -ylim Ranges on the y-axis.
# -range Same as ``ylim``.
# -title_gp Graphic parameters for title.
# -ylab_gp Graphic parameters for y-labels.
# -tick_label_gp Graphic parameters for y-ticks.
# -quantile_gp Graphic parameters for the quantiles.
# -column_order Order of columns.
# -column_names_side Pass to `Heatmap`.
# -show_column_names Pass to `Heatmap`.
# -column_names_max_height Pass to `Heatmap`.
# -column_names_gp Pass to `Heatmap`.
# -column_names_rot Pass to `Heatmap`.
# -cluster_columns Whether cluster columns?
# -clustering_distance_columns There is a specific distance method ``ks`` which is the Kolmogorov-Smirnov statistic between two distributions.
# For other methods, the distance is calculated on the density matrix.
# -clustering_method_columns Pass to `Heatmap`.
# -mc.cores Multiple cores for calculating ks distance.
# -... Pass to `Heatmap`.
#
# == details
# To visualize data distribution in a matrix or in a list, we normally use
# boxplot or violinplot. We can also use colors to map the density values and
# visualize distribution of values through a heatmap. It is useful if you have
# huge number of columns in ``data`` to visualize.
#
# The density matrix is generated with 500 rows ranging between the maximun
# and minimal values in all densities.
#
# == value
# A `Heatmap-class` object. It can oly add other heatmaps/annotations vertically.
#
# == seealso
# https://jokergoo.github.io/ComplexHeatmap-reference/book/other-high-level-plots.html#density-heatmap
#
# == author
# Zuguang Gu <z.gu@dkfz.de>
#
# == example
# matrix = matrix(rnorm(100), 10); colnames(matrix) = letters[1:10]
# densityHeatmap(matrix)
#
# lt = list(rnorm(10), rnorm(10))
# densityHeatmap(lt)
#
# ha = HeatmapAnnotation(points = anno_points(runif(10)),
# anno = rep(c("A", "B"), each = 5), col = list(anno = c("A" = "red", "B" = "blue")))
# densityHeatmap(matrix, top_annotation = ha)
# densityHeatmap(matrix, top_annotation = ha) \%v\% Heatmap(matrix, height = unit(6, "cm"))
densityHeatmap = function(data,
density_param = list(na.rm = TRUE),
col = rev(brewer.pal(11, "Spectral")),
color_space = "LAB",
ylab = deparse(substitute(data)),
column_title = paste0("Density heatmap of ", deparse(substitute(data))),
title = column_title,
ylim = c(-Inf, Inf),
range = ylim,
title_gp = gpar(fontsize = 14),
ylab_gp = gpar(fontsize = 12),
tick_label_gp = gpar(fontsize = 10),
quantile_gp = gpar(fontsize = 10),
column_order = NULL,
column_names_side = "bottom",
show_column_names = TRUE,
column_names_max_height = unit(6, "cm"),
column_names_gp = gpar(fontsize = 12),
column_names_rot = 90,
cluster_columns = FALSE,
clustering_distance_columns = "ks",
clustering_method_columns = "complete",
mc.cores = 1,
...) {
arg_list = list(...)
if(length(arg_list)) {
if(any(c("row_km", "row_split", "split", "km") %in% names(arg_list))) {
stop_wrap("density heatmaps do not allow row splitting.")
}
if(any(grepl("row", names(arg_list)))) {
stop_wrap("density heatmaps do not allow to set rows.")
}
if("anno" %in% names(arg_list)) {
stop_wrap("`anno` is removed from the argument. Please directly construct a `HeatmapAnnotation` object and set to `top_annotation` or `bottom_annotation`.")
}
}
ylab = ylab
column_title = column_title
density_param$na.rm = TRUE
if(!is.matrix(data) && !is.data.frame(data) && !is.list(data)) {
stop_wrap("only matrix and list are allowed.")
}
if(is.matrix(data)) {
data2 = as.list(as.data.frame(data))
names(data2) = colnames(data)
data = data2
}
density_list = lapply(data, function(x) do.call(density, c(list(x = x), density_param)))
quantile_list = sapply(data, quantile, na.rm = TRUE)
mean_value = sapply(data, mean, na.rm = TRUE)
n = length(density_list)
nm = names(density_list)
max_x = quantile(unlist(lapply(density_list, function(x) x$x)), 0.99)
min_x = quantile(unlist(lapply(density_list, function(x) x$x)), 0.01)
max_x = min(max_x, range[2])
min_x = max(min_x, range[1])
x = seq(min_x, max_x, length = 500)
mat = lapply(density_list, function(r) {
f = approxfun(r$x, r$y)
res = f(x)
res[is.na(res)] = 0
rev(res)
})
mat = as.matrix(as.data.frame(mat))
colnames(mat) = nm
if(cluster_columns) {
if(clustering_distance_columns == "ks") {
d = ks_dist(mat, mc.cores = mc.cores)
hc = hclust(d, clustering_method_columns)
cluster_columns = hc
}
}
col = colorRamp2(seq(0, max(mat, na.rm = TRUE), length = length(col)), col, space = color_space)
bb = grid.pretty(c(min_x, max_x))
ht = Heatmap(mat, col = col, name = "density",
column_title = title,
column_title_gp = title_gp,
cluster_rows = FALSE,
cluster_columns = cluster_columns,
clustering_distance_columns = clustering_distance_columns,
clustering_method_columns = clustering_method_columns,
column_dend_reorder = mean_value,
column_names_side = column_names_side,
show_column_names = show_column_names,
column_names_max_height = column_names_max_height,
column_names_gp = column_names_gp,
column_names_rot = column_names_rot,
column_order = column_order,
left_annotation = rowAnnotation(axis = anno_empty(border = FALSE,
width = grobHeight(textGrob(ylab, gp = ylab_gp))*2 + max_text_width(bb, gp = tick_label_gp) + unit(4, "mm")),
show_annotation_name = FALSE),
right_annotation = rowAnnotation(quantile = anno_empty(border = FALSE,
width = grobWidth(textGrob("100%", gp = quantile_gp)) + unit(6, "mm")),
show_annotation_name = FALSE),
...
)
random_str = paste(sample(c(letters, LETTERS, 0:9), 8), collapse = "")
ht@name = paste0(ht@name, "_", random_str)
names(ht@left_annotation) = paste0(names(ht@left_annotation), "_", random_str)
names(ht@right_annotation) = paste0(names(ht@right_annotation), "_", random_str)
post_fun = function(ht) {
column_order = column_order(ht)
if(!is.list(column_order)) {
column_order = list(column_order)
}
n_slice = length(column_order)
decorate_annotation(paste0("axis_", random_str), {
grid.text(ylab, x = grobHeight(textGrob(ylab, gp = ylab_gp)), rot = 90)
}, slice = 1)
for(i_slice in 1:n_slice) {
decorate_heatmap_body(paste0("density_", random_str), {
n = length(column_order[[i_slice]])
pushViewport(viewport(xscale = c(0.5, n + 0.5), yscale = c(min_x, max_x), clip = TRUE))
for(i in seq_len(5)) {
grid.lines(1:n, quantile_list[i, column_order[[i_slice]] ], default.units = "native", gp = gpar(lty = 2))
}
grid.lines(1:n, mean_value[ column_order[[i_slice]] ], default.units = "native", gp = gpar(lty = 2, col = "darkred"))
upViewport()
}, column_slice = i_slice)
}
decorate_heatmap_body(paste0("density_", random_str), {
pushViewport(viewport(yscale = c(min_x, max_x), clip = FALSE))
grid.rect(gp = gpar(fill = NA))
grid.yaxis(gp = tick_label_gp)
upViewport()
}, column_slice = 1)
decorate_heatmap_body(paste0("density_", random_str), {
n = length(column_order[[n_slice]])
lq = !apply(quantile_list, 1, function(x) all(x > max_x) || all(x < min_x))
lq = c(lq, !(all(mean_value > max_x) || all(mean_value < min_x)))
if(sum(lq) == 0) {
return(NULL)
}
labels = c(rownames(quantile_list), "mean")
y = c(quantile_list[, column_order[[n_slice]][n] ], mean_value[ column_order[[n_slice]][n] ])
labels = labels[lq]
y = y[lq]
od = order(y)
y = y[od]
labels = labels[od]
pushViewport(viewport(xscale = c(0.5, n + 0.5), yscale = c(min_x, max_x), clip = FALSE))
text_height = convertHeight(grobHeight(textGrob(labels[1])) * (1 + 0.2), "native", valueOnly = TRUE)
h1 = y - text_height*0.5
h2 = y + text_height*0.5
pos = rev(smartAlign(h1, h2, c(min_x, max_x)))
h = (pos[, 1] + pos[, 2])/2
link_width = unit(6, "mm")
n2 = length(labels)
grid.text(labels, unit(1, "npc") + rep(link_width, n2), h, default.units = "native", just = "left", gp = quantile_gp)
link_width = link_width - unit(1, "mm")
ly = y <= max_x & y >= min_x
if(sum(ly)) {
grid.segments(unit(rep(1, n2), "npc")[ly], y[ly], unit(1, "npc") + rep(link_width * (1/3), n2)[ly], y[ly], default.units = "native")
grid.segments(unit(1, "npc") + rep(link_width * (1/3), n2)[ly], y[ly], unit(1, "npc") + rep(link_width * (2/3), n2)[ly], h[ly], default.units = "native")
grid.segments(unit(1, "npc") + rep(link_width * (2/3), n2)[ly], h[ly], unit(1, "npc") + rep(link_width, n2)[ly], h[ly], default.units = "native")
}
upViewport()
}, column_slice = n_slice)
}
ht@heatmap_param$post_fun = post_fun
ht_list = ht %v% NULL
return(ht_list)
}
# https://stackoverflow.com/a/29853834/3425904
ks_dist_pair = function(x, y) {
# if(length(x) > 5000) x = sample(x, 5000)
# if(length(y) > 5000) y = sample(y, 5000)
n <- length(x)
n.x <- as.double(n)
n.y <- length(y)
n <- n.x * n.y/(n.x + n.y)
w <- c(x, y)
z <- cumsum(ifelse(order(w) <= n.x, 1/n.x, -1/n.y))
max(abs(z))
}
# data: a list or a matrix
ks_dist = function(data, mc.cores = 1) {
has_names = TRUE
if(is.matrix(data)) {
has_names = !is.null(colnames(data))
data = as.data.frame(data)
}
nc = length(data)
if(.Platform$OS.type == "windows") {
if(mc.cores > 1) {
message("parallel::mclapply() does not support multiple cores on Windows. mc.cores is reset to 1.")
mc.cores = 1
}
}
ind_mat = expand.grid(seq_len(nc), seq_len(nc))
ind_mat = ind_mat[ ind_mat[, 1] > ind_mat[, 2], , drop = FALSE]
v = mclapply(seq_len(nrow(ind_mat)), function(ind) {
i = ind_mat[ind, 1]
j = ind_mat[ind, 2]
suppressWarnings(d <- ks_dist_pair(data[[i]], data[[j]]))
return(d)
}, mc.cores = mc.cores)
v = unlist(v)
i = ind_mat[, 1]
j = ind_mat[, 2]
ind = (j - 1) * nc + i
d = matrix(0, nrow = nc, ncol = nc)
if(has_names) rownames(d) = colnames(d) = names(data)
d[ind] = v
as.dist(d)
}
# m = matrix(rnorm(200), nc = 10)
# ks_dist(m, mc.cores = 1)
# ks_dist(m, mc.cores = 2)
# ks_dist_1(m)
# lt = lapply(1:10, function(i) rnorm(runif(1, min = 10, max = 20)))
# ks_dist(lt, mc.cores = 1)
# ks_dist(lt, mc.cores = 2)
# ks_dist_1(lt)
ks_dist_1 = function(data) {
has_names = TRUE
if(is.matrix(data)) {
has_names = !is.null(colnames(data))
data = as.data.frame(data)
}
nc = length(data)
d = matrix(NA, nrow = nc, ncol = nc)
if(has_names) rownames(d) = colnames(d) = names(data)
for(i in 2:nc) {
for(j in 1:(nc-1)) {
suppressWarnings(d[i, j] <- ks_dist_pair(data[[i]], data[[j]]))
}
}
as.dist(d)
}
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