Package 'dendextend'

Description

Offers a set of functions for extending 'dendrogram' objects in R, letting you visualize and compare trees of 'hierarchical clusterings'. You can (1) Adjust a tree's graphical parameters - the color, size, type, etc of its branches, nodes and labels. (2) Visually and statistically compare different 'dendrograms' to one another.

Author(s)

Maintainer: Tal Galili [email protected] (https://www.r-statistics.com) [copyright holder]

Authors:

• Gregory Jefferis [email protected] (imported code from his dendroextras package) [contributor]

Other contributors:

• Yoav Benjamini [email protected] [thesis advisor]

• Gavin Simpson [contributor]

• Marco Gallotta (a.k.a: marcog) [contributor]

• Johan Renaudie (https://github.com/plannapus) [contributor]

• The R Core Team (Thanks for the Infastructure, and code in the examples) [contributor]

• Kurt Hornik [contributor]

• Uwe Ligges [contributor]

• Andrej-Nikolai Spiess [contributor]

• Steve Horvath [email protected] [contributor]

• Peter Langfelder [email protected] [contributor]

• skullkey [contributor]

• Mark Van Der Loo [email protected] (https://github.com/markvanderloo d3dendrogram) [contributor]

• Andrie de Vries [email protected] (ggdendro author) [contributor]

• Zuguang Gu [email protected] (circlize author) [contributor]

• Cath (https://github.com/CathG) [contributor]

• John Ma (https://github.com/JohnMCMa) [contributor]

• Krzysiek G (https://github.com/storaged) [contributor]

• Manuela Hummel [email protected] (https://github.com/hummelma) [contributor]

• Chase Clark (https://github.com/chasemc) [contributor]

• Lucas Graybuck (https://github.com/hypercompetent) [contributor]

• jdetribol (https://github.com/jdetribol) [contributor]

• Ben Ho [email protected] (https://github.com/SplitInf) [contributor]

• Samuel Perreault [email protected] (https://github.com/samperochkin) [contributor]

• Christian Hennig [email protected] (http://www.homepages.ucl.ac.uk/~ucakche/) [contributor]

• David Bradley (https://github.com/DBradley27) [contributor]

• Houyun Huang [email protected] (https://github.com/houyunhuang) [contributor]

• Patrick Schupp [email protected] (https://github.com/pschupp) [contributor]

• Alec Buetow [email protected] (https://github.com/alecbuetow) [contributor]

See Also

dendrogram, hclust in stats package.

Description

Given a tree and a k number of clusters, the tree is rotated so that the extra clusters added from k-1 to k clusters are flipped.

This is useful for finding good trees for a tanglegram.

Usage

all_couple_rotations_at_k(dend, k, dend_heights_per_k, ...)

Arguments

Value

A list with dendrogram objects with all the possible rotations for k clusters (beyond the k-1 clusters!).

See Also

tanglegram, match_order_by_labels, entanglement, flip_leaves.

Examples

## Not run: 
dend1 <- USArrests[1:5, ] %>%
  dist() %>%
  hclust() %>%
  as.dendrogram()
dend2 <- all_couple_rotations_at_k(dend1, k = 2)[[2]]
tanglegram(dend1, dend2)
entanglement(dend1, dend2, L = 2) # 0.5

dend2 <- all_couple_rotations_at_k(dend1, k = 3)[[2]]
tanglegram(dend1, dend2)
entanglement(dend1, dend2, L = 2) # 0.4

dend2 <- all_couple_rotations_at_k(dend1, k = 4)[[2]]
tanglegram(dend1, dend2)
entanglement(dend1, dend2, L = 2) # 0.05

## End(Not run)

Description

Checks if all the elements in a vector are unique

Usage

all_unique(x, ...)

Arguments

Value

logical (are all the elements in the vector unique)

Source

https://www.mail-archive.com/[email protected]/msg77592.html OLD (no longer working): https://r.789695.n4.nabble.com/Is-there-a-function-to-test-if-all-the-elements-in-a-vector-are-unique-td931833.html

See Also

unique

Examples

all_unique(c(1:5, 1, 1))
all_unique(c(1, 1, 2))
all_unique(c(1, 1, 2, 3, 3, 3, 3))
all_unique(c(1, 3, 2))
all_unique(c(1:10))

Description

This function makes a global comparison of two or more dendrograms trees.

The function can get two dendlist objects and compare them using all.equal.list. If a dendlist is in only "target" (and not "current"), it will go through the dendlist and compare all of the dendrograms within it to one another.

Usage

## S3 method for class 'dendrogram'
all.equal(target, current,
                                     use.edge.length = TRUE,
                                     use.tip.label.order = FALSE,
                                     use.tip.label = TRUE,
                                     use.topology = TRUE,
                                     tolerance = .Machine$double.eps^0.5,
                                     scale = NULL, ...)

## S3 method for class 'dendlist'
all.equal(target, current, ...)

Arguments

Value

Either TRUE (NULL for attr.all.equal) or a vector of mode "character" describing the differences between target and current.

See Also

all.equal, all.equal.phylo, identical

Examples

## Not run: 

set.seed(23235)
ss <- sample(1:150, 10)
dend1 <- iris[ss, -5] %>%
  dist() %>%
  hclust("com") %>%
  as.dendrogram()
dend2 <- iris[ss, -5] %>%
  dist() %>%
  hclust("single") %>%
  as.dendrogram()
dend3 <- iris[ss, -5] %>%
  dist() %>%
  hclust("ave") %>%
  as.dendrogram()
dend4 <- iris[ss, -5] %>%
  dist() %>%
  hclust("centroid") %>%
  as.dendrogram()
#    cutree(dend1)

all.equal(dend1, dend1)
all.equal(dend1, dend2)
all.equal(dend1, dend2, use.edge.length = FALSE)
all.equal(dend1, dend2, use.edge.length = FALSE, use.topology = FALSE)

all.equal(dend2, dend4, use.edge.length = TRUE)
all.equal(dend2, dend4, use.edge.length = FALSE)

all.equal(dendlist(dend1, dend2, dend3, dend4))
all.equal(dendlist(dend1, dend2, dend3, dend4), use.edge.length = FALSE)
all.equal(dendlist(dend1, dend1, dend1))

## End(Not run)

Description

Convert dendrogram Objects to Class hclust while preserving the call/method/dist.method values of the original hclust object (hc)

Usage

as_hclust_fixed(x, hc, ...)

Arguments

Value

An hclust object (from a dendrogram) with the original hclust call/method/dist.method values

See Also

as.hclust

Examples

hc <- hclust(dist(USArrests[1:3, ]), "ave")
dend <- as.dendrogram(hc)

as.hclust(dend)
as_hclust_fixed(dend, hc)

Description

It removes stuff that are not dendgrogram/dendlist and turns what is left into a dendlist

Usage

as.dendlist(x, ...)

Arguments

Value

A list of class dendlist where each item is a dendrogram

Examples

## Not run: 

dend <- iris[, -5] %>%
  dist() %>%
  hclust() %>%
  as.dendrogram()
dend2 <- iris[, -5] %>%
  dist() %>%
  hclust(method = "single") %>%
  as.dendrogram()

x <- list(dend, 1, dend2)
as.dendlist(x)

## End(Not run)

Description

Based on as.hclust.dendrogram with as.phylo.hclust

In the future I hope a more direct link will be made.

Usage

as.phylo.dendrogram(x, ...)

Arguments

Value

A phylo class object

See Also

as.dendrogram, as.hclust, as.phylo

Examples

## Not run: 

library(dendextend)
library(ape)
dend <- iris[1:30, -5] %>%
  dist() %>%
  hclust() %>%
  as.dendrogram()
dend2 <- as.phylo(dend)
plot(dend2, type = "fan")

library(dendextend)
library(ggplot2)
# no longer needed: library(ggdendro)
dend <- iris[1:30, -5] %>%
   dist() %>%
   hclust() %>%
   as.dendrogram()
# there is a bug in the location of the labels 
# If you want to solve it - please send a Pull Request to:
# https://github.com/talgalili/dendextend/
ggplot(dend) +
   scale_y_reverse(expand = c(0.2, 0)) + coord_polar(start = 1, theta="x") 
   

## End(Not run)


# see: https://github.com/klutometis/roxygen/issues/796
#

Description

Populates dendextend functions into dendextend_options

Usage

assign_dendextend_options()

Description

Go through the dendrogram branches and updates the values inside its edgePar

If the value has Inf then the value in edgePar will not be changed.

Usage

assign_values_to_branches_edgePar(
  dend,
  value,
  edgePar,
  skip_leaves = FALSE,
  warn = dendextend_options("warn"),
  ...
)

Arguments

Value

A dendrogram, after adjusting the edgePar attribute in all of its branches,

See Also

get_root_branches_attr

Examples

# This failed before - now it works fine. (thanks to Martin Maechler)
dend <- 1:2 %>%
  dist() %>%
  hclust() %>%
  as.dendrogram()
dend %>%
  set("branches_lty", 1:2) %>%
  set("branches_col", c("topbranch_never_plots", "black", "orange")) %>%
  plot()
## Not run: 

dend <- USArrests[1:5, ] %>%
  dist() %>%
  hclust() %>%
  as.dendrogram()
plot(dend)
dend <- assign_values_to_branches_edgePar(dend = dend, value = 2, edgePar = "lwd")
plot(dend)
dend <- assign_values_to_branches_edgePar(dend = dend, value = 2, edgePar = "col")
plot(dend)
dend <- assign_values_to_branches_edgePar(dend = dend, value = "orange", edgePar = "col")
plot(dend)
dend2 <- assign_values_to_branches_edgePar(dend = dend, value = 2, edgePar = "lty")
plot(dend2)

dend2 %>%
  unclass() %>%
  str()

## End(Not run)

Description

Go through the dendrogram leaves and updates the values inside its edgePar

If the value has Inf then the value in edgePar will not be changed.

Usage

assign_values_to_leaves_edgePar(
  dend,
  value,
  edgePar,
  warn = dendextend_options("warn"),
  ...
)

Arguments

Value

A dendrogram, after adjusting the edgePar attribute in all of its leaves,

See Also

get_leaves_attr, assign_values_to_leaves_nodePar

Examples

## Not run: 

dend <- USArrests[1:5, ] %>%
  dist() %>%
  hclust("ave") %>%
  as.dendrogram()

plot(dend)
dend <- assign_values_to_leaves_edgePar(dend = dend, value = c(3, 2), edgePar = "col")
plot(dend)
dend <- assign_values_to_leaves_edgePar(dend = dend, value = c(3, 2), edgePar = "lwd")
plot(dend)
dend <- assign_values_to_leaves_edgePar(dend = dend, value = c(3, 2), edgePar = "lty")
plot(dend)

get_leaves_attr(dend, "edgePar", simplify = FALSE)

## End(Not run)

Description

Go through the dendrogram leaves and updates the values inside its nodePar

If the value has Inf then the value in edgePar will not be changed.

Usage

assign_values_to_leaves_nodePar(
  dend,
  value,
  nodePar,
  warn = dendextend_options("warn"),
  ...
)

Arguments

Value

A dendrogram, after adjusting the nodePar attribute in all of its leaves,

See Also

get_leaves_attr

Examples

## Not run: 

dend <- USArrests[1:5, ] %>%
  dist() %>%
  hclust("ave") %>%
  as.dendrogram()

# reproduces "labels_colors<-"
# although it does force us to run through the tree twice,
# hence "labels_colors<-" is better...
plot(dend)
dend <- assign_values_to_leaves_nodePar(dend = dend, value = c(3, 2), nodePar = "lab.col")
plot(dend)

dend <- assign_values_to_leaves_nodePar(dend, 1, "pch")
plot(dend)
# fix the annoying pch=1:
dend <- assign_values_to_leaves_nodePar(dend, NA, "pch")
plot(dend)
# adjust the cex:
dend <- assign_values_to_leaves_nodePar(dend, 19, "pch")
dend <- assign_values_to_leaves_nodePar(dend, 2, "lab.cex")
plot(dend)

str(unclass(dend))

get_leaves_attr(dend, "nodePar", simplify = FALSE)

## End(Not run)

Description

Go through the dendrogram nodes and updates the values inside its nodePar

If the value has Inf then the value in edgePar will not be changed.

Usage

assign_values_to_nodes_nodePar(
  dend,
  value,
  nodePar = c("pch", "cex", "col", "xpd", "bg"),
  warn = dendextend_options("warn"),
  ...
)

Arguments

Value

A dendrogram, after adjusting the nodePar attribute in all of its nodes,

See Also

get_leaves_attr, assign_values_to_leaves_nodePar

Examples

## Not run: 

dend <- USArrests[1:5, ] %>%
  dist() %>%
  hclust("ave") %>%
  as.dendrogram()

# reproduces "labels_colors<-"
# although it does force us to run through the tree twice,
# hence "labels_colors<-" is better...
plot(dend)
dend2 <- dend %>%
  assign_values_to_nodes_nodePar(value = 19, nodePar = "pch") %>%
  assign_values_to_nodes_nodePar(value = c(1, 2), nodePar = "cex") %>%
  assign_values_to_nodes_nodePar(value = c(2, 1), nodePar = "col")
plot(dend2)


### Making sure this works for NA with character.
dend %>%
  assign_values_to_nodes_nodePar(value = 19, nodePar = "pch") %>%
  assign_values_to_nodes_nodePar(value = c("red", NA), nodePar = "col") -> dend2
plot(dend2)

## End(Not run)

Description

Bakers Gamma for two k matrices

Usage

bakers_gamma_for_2_k_matrix(
  k_matrix_dend1,
  k_matrix_dend2,
  to_plot = FALSE,
  ...
)

Arguments

Value

Baker's Gamma coefficient.

See Also

cor_bakers_gamma

Description

Bk is the calculation of Fowlkes-Mallows index for a series of k cuts for two dendrograms.

Usage

Bk(tree1, tree2, k, warn = dendextend_options("warn"), ...)

Arguments

Details

From Wikipedia:

Fowlkes-Mallows index (see references) is an external evaluation method that is used to determine the similarity between two clusterings (clusters obtained after a clustering algorithm). This measure of similarity could be either between two hierarchical clusterings or a clustering and a benchmark classification. A higher the value for the Fowlkes-Mallows index indicates a greater similarity between the clusters and the benchmark classifications.

Value

A list (of k's length) of Fowlkes-Mallows index between two dendrogram for a scalar/vector of k values. The names of the lists' items is the k for which it was calculated.

References

Fowlkes, E. B.; Mallows, C. L. (1 September 1983). "A Method for Comparing Two Hierarchical Clusterings". Journal of the American Statistical Association 78 (383): 553.

https://en.wikipedia.org/wiki/Fowlkes-Mallows_index

See Also

FM_index, cor_bakers_gamma, Bk_plot

Examples

## Not run: 

set.seed(23235)
ss <- TRUE # sample(1:150, 10 )
hc1 <- hclust(dist(iris[ss, -5]), "com")
hc2 <- hclust(dist(iris[ss, -5]), "single")
tree1 <- as.dendrogram(hc1)
tree2 <- as.dendrogram(hc2)
#    cutree(tree1)

Bk(hc1, hc2, k = 3)
Bk(hc1, hc2, k = 2:10)
Bk(hc1, hc2)

Bk(tree1, tree2, k = 3)
Bk(tree1, tree2, k = 2:5)

system.time(Bk(hc1, hc2, k = 2:5)) # 0.01
system.time(Bk(hc1, hc2)) # 1.28
system.time(Bk(tree1, tree2, k = 2:5)) # 0.24 # after fixes.
system.time(Bk(tree1, tree2, k = 2:10)) # 0.31 # after fixes.
system.time(Bk(tree1, tree2)) # 7.85
Bk(tree1, tree2, k = 99:101)

y <- Bk(hc1, hc2, k = 2:10)
plot(unlist(y) ~ c(2:10), type = "b", ylim = c(0, 1))

# can take a few seconds
y <- Bk(hc1, hc2)
plot(unlist(y) ~ as.numeric(names(y)),
  main = "Bk plot", pch = 20,
  xlab = "k", ylab = "FM Index",
  type = "b", ylim = c(0, 1)
)
# we are still missing some hypothesis testing here.
# for this we'll have the Bk_plot function.

## End(Not run)

Description

Bk is the calculation of Fowlkes-Mallows index for a series of k cuts for two dendrograms.

Bk permutation calculates the Bk under the null hypothesis of no similarirty between the two trees by randomally shuffling the labels of the two trees and calculating their Bk.

Usage

Bk_permutations(
  tree1,
  tree2,
  k,
  R = 1000,
  warn = dendextend_options("warn"),
  ...
)

Arguments

Details

From Wikipedia:

Fowlkes-Mallows index (see references) is an external evaluation method that is used to determine the similarity between two clusterings (clusters obtained after a clustering algorithm). This measure of similarity could be either between two hierarchical clusterings or a clustering and a benchmark classification. A higher the value for the Fowlkes-Mallows index indicates a greater similarity between the clusters and the benchmark classifications.

Value

A list (of the length of k's), where each element of the list has R (number of permutations) calculations of Fowlkes-Mallows index between two dendrogram after having their labels shuffled.

The names of the lists' items is the k for which it was calculated.

References

Fowlkes, E. B.; Mallows, C. L. (1 September 1983). "A Method for Comparing Two Hierarchical Clusterings". Journal of the American Statistical Association 78 (383): 553.

https://en.wikipedia.org/wiki/Fowlkes-Mallows_index

See Also

FM_index, Bk

Examples

## Not run: 

set.seed(23235)
ss <- TRUE # sample(1:150, 10 )
hc1 <- hclust(dist(iris[ss, -5]), "com")
hc2 <- hclust(dist(iris[ss, -5]), "single")
# tree1 <- as.treerogram(hc1)
# tree2 <- as.treerogram(hc2)
#    cutree(tree1)

some_Bk <- Bk(hc1, hc2, k = 20)
some_Bk_permu <- Bk_permutations(hc1, hc2, k = 20)

# we can see that the Bk is much higher than the permutation Bks:
plot(
  x = rep(1, 1000), y = some_Bk_permu[[1]],
  main = "Bk distribution under H0",
  ylim = c(0, 1)
)
points(1, y = some_Bk, pch = 19, col = 2)

## End(Not run)

Description

Bk is the calculation of Fowlkes-Mallows index for a series of k cuts for two dendrograms. A Bk plot is simply a scatter plot of Bk versus k. This plot helps in identifiying the similarity between two dendrograms in different levels of k (number of clusters).

Usage

Bk_plot(
  tree1,
  tree2,
  k,
  add_E = TRUE,
  rejection_line_asymptotic = TRUE,
  rejection_line_permutation = FALSE,
  R = 1000,
  k_permutation,
  conf.level = 0.95,
  p.adjust.methods = c("none", "bonferroni"),
  col_line_Bk = 1,
  col_line_asymptotic = 2,
  col_line_permutation = 4,
  warn = dendextend_options("warn"),
  main = "Bk plot",
  xlab = "k (number of clusters)",
  ylab = "Bk (Fowlkes-Mallows Index)",
  xlim,
  ylim = c(0, 1),
  try_cutree_hclust = TRUE,
  ...
)

Arguments

Details

From Wikipedia:

Fowlkes-Mallows index (see references) is an external evaluation method that is used to determine the similarity between two clusterings (clusters obtained after a clustering algorithm). This measure of similarity could be either between two hierarchical clusterings or a clustering and a benchmark classification. A higher the value for the Fowlkes-Mallows index indicates a greater similarity between the clusters and the benchmark classifications.

The default Bk plot comes with a line with dots (type "b") of the Bk values. Also with a fragmented (lty=2) line (of the same color) of the expected Bk line under H0, And a solid red line of the upper critical Bk values for rejection

Value

After plotting the Bk plot. Returns (invisible) the output of the elements used for constructing the plot: The Bk values, Bk permutations (if used), Bk theoratical values, etc.

References

Fowlkes, E. B.; Mallows, C. L. (1 September 1983). "A Method for Comparing Two Hierarchical Clusterings". Journal of the American Statistical Association 78 (383): 553.

https://en.wikipedia.org/wiki/Fowlkes-Mallows_index

See Also

FM_index, Bk, Bk_permutations

Examples

## Not run: 

set.seed(23235)
ss <- TRUE # sample(1:150, 10 )
hc1 <- hclust(dist(iris[ss, -5]), "com")
hc2 <- hclust(dist(iris[ss, -5]), "single")
# tree1 <- as.treerogram(hc1)
# tree2 <- as.treerogram(hc2)
#    cutree(tree1)

Bk_plot(hc1, hc2, k = 2:20, xlim = c(2, 149))
Bk_plot(hc1, hc2)

Bk_plot(hc1, hc2, k = 3)
Bk_plot(hc1, hc2, k = 3:10)
Bk_plot(hc1, hc2)
Bk_plot(hc1, hc2, p.adjust.methods = "bonferroni") # higher rejection lines

# this one can take a bit of time:
Bk_plot(hc1, hc2,
  rejection_line_permutation = TRUE,
  k_permutation = c(2, 4, 6, 8, 10, 20, 30, 40, 50), R = 100
)
# we can see that the permutation line is VERY close to the asymptotic line.
# This is great since it means one can often use the asymptotic results
# Without having to do many simulations.

# works just as well for dendrograms:
dend1 <- as.dendrogram(hc1)
dend2 <- as.dendrogram(hc2)
Bk_plot(dend1, dend2, k = 2:3, try_cutree_hclust = FALSE) # slower than hclust, but works...
Bk_plot(hc1, dend2, k = 2:3, try_cutree_hclust = FALSE) # slower than hclust, but works...
Bk_plot(dend1, dend1, k = 2:3, try_cutree_hclust = TRUE) # slower than hclust, but works...
Bk_plot(hc1, hc1, k = 2:3) # slower than hclust, but works...
# for some reason it can't turn dend2 back to hclust :(
a <- Bk_plot(hc1, hc2, k = 2:3, try_cutree_hclust = TRUE) # slower than hclust, but works...

hc1_mixed <- as.hclust(sample(as.dendrogram(hc1)))
Bk_plot(
  tree1 = hc1, tree2 = hc1_mixed,
  add_E = FALSE,
  rejection_line_permutation = TRUE, k_permutation = c(2, 4, 6, 8, 10, 20, 30, 40, 50), R = 100
)

## End(Not run)

Description

The user supplies a dend, a vector of clusters, and what to modify (and how).

And the function returns a dendrogram with branches col/lwd/lty accordingly. (the function assumes unique labels)

Usage

branches_attr_by_clusters(
  dend,
  clusters,
  values,
  attr = c("col", "lwd", "lty"),
  branches_changed_have_which_labels = c("any", "all"),
  ...
)

Arguments

Details

This is probably NOT a very fast implementation of the function, but it works.

This function was designed to enable the manipulation (mainly coloring) of branches, based on the results from the cutreeDynamic function.

Value

A dendrogram with modified branches (col/lwd/lty).

See Also

branches_attr_by_labels, get_leaves_attr, nnodes, nleaves cutreeDynamic, plotDendroAndColors

Examples

## Not run: 

### Getting the hc object
iris_dist <- iris[, -5] %>% dist()
hc <- iris_dist %>% hclust()
# This is how it looks without any colors:
dend <- as.dendrogram(hc)
plot(dend)

# Both functions give the same outcome
# options 1:
dend %>%
  set("branches_k_color", k = 4) %>%
  plot()
# options 2:
clusters <- cutree(dend, 4)[order.dendrogram(dend)]
dend %>%
  branches_attr_by_clusters(clusters) %>%
  plot()

# and the second option is much slower:
system.time(set(dend, "branches_k_color", k = 4)) # 0.26 sec
system.time(branches_attr_by_clusters(dend, clusters)) # 1.61 sec
# BUT, it also allows us to do more flaxible things!

#--------------------------
#   Plotting dynamicTreeCut
#--------------------------

# let's get the clusters
library(dynamicTreeCut)
clusters <- cutreeDynamic(hc, distM = as.matrix(iris_dist))
# we need to sort them to the order of the dendrogram:
clusters <- clusters[order.dendrogram(dend)]

# get some functions:
library(colorspace)
no0_unique <- function(x) {
  u_x <- unique(x)
  u_x[u_x != 0]
}

clusters_numbers <- no0_unique(clusters)
n_clusters <- length(clusters_numbers)
cols <- rainbow_hcl(n_clusters)
dend2 <- branches_attr_by_clusters(dend, clusters, values = cols)
# dend2 <- branches_attr_by_clusters(dend, clusters)
plot(dend2)
# add colored bars:
ord_cols <- rainbow_hcl(n_clusters)[order(clusters_numbers)]
tmp_cols <- rep(1, length(clusters))
tmp_cols[clusters != 0] <- ord_cols[clusters != 0][clusters]
colored_bars(tmp_cols, y_shift = -1.1, rowLabels = "")
# all of the ordering is to handle the fact that the cluster numbers are not ascending...

# How is this compared with the usual cutree?
dend3 <- color_branches(dend, k = n_clusters)
labels(dend2) <- as.character(labels(dend2))
# this needs fixing, since the labels are not character!
# Well, both cluster solutions are not perfect, but at least they are interesting...
tanglegram(dend2, dend3,
  main_left = "cutreeDynamic", main_right = "cutree",
  columns_width = c(5, .5, 5),
  color_lines = cols[iris[order.dendrogram(dend2), 5]]
)
# (Notice how the color_lines is of the true Species of each Iris)
# The main difference is at the bottom,

## End(Not run)

Description

The user supplies a dend, labels, and type of condition (all/any), and TF_values And the function returns a dendrogram with branches col/lwd/lty accordingly

Usage

branches_attr_by_labels(
  dend,
  labels,
  TF_values = c(2, Inf),
  attr = c("col", "lwd", "lty"),
  type = c("all", "any"),
  ...
)

Arguments

Value

A dendrogram with modified branches (col/lwd/lty).

See Also

noded_with_condition, get_leaves_attr, nnodes, nleaves

Examples

## Not run: 

library(dendextend)

set.seed(23235)
ss <- sample(1:150, 10)

# Getting the dend dend
dend <- iris[ss, -5] %>%
  dist() %>%
  hclust() %>%
  as.dendrogram()
dend %>% plot()

dend %>%
  branches_attr_by_labels(c("123", "126", "23", "29")) %>%
  plot()
dend %>%
  branches_attr_by_labels(c("123", "126", "23", "29"), "all") %>%
  plot() # the same as above
dend %>%
  branches_attr_by_labels(c("123", "126", "23", "29"), "any") %>%
  plot()

dend %>%
  branches_attr_by_labels(
    c("123", "126", "23", "29"),
    "any", "col", c("blue", "red")
  ) %>%
  plot()
dend %>%
  branches_attr_by_labels(
    c("123", "126", "23", "29"),
    "any", "lwd", c(4, 1)
  ) %>%
  plot()
dend %>%
  branches_attr_by_labels(
    c("123", "126", "23", "29"),
    "any", "lty", c(2, 1)
  ) %>%
  plot()

## End(Not run)

Description

The user supplies a dend, lists, and type of condition (all/any), and TF_values And the function returns a dendrogram with branches col/lwd/lty accordingly

Usage

branches_attr_by_lists(
  dend,
  lists,
  TF_values = c(2, 1),
  attr = c("col", "lwd", "lty"),
  ...
)

Arguments

Value

A dendrogram with modified branches (col/lwd/lty).

See Also

branches_attr_by_labels

Examples

## Not run: 

library(dendextend)

set.seed(23235)
ss <- sample(1:150, 10)

# Getting the dend dend
dend <- iris[ss, -5] %>%
  dist() %>%
  hclust() %>%
  as.dendrogram()
dend %>% plot()

# define a list of nodes
L <- list(c("109", "123", "126", "145"), "29", c("59", "67", "97"))
dend %>%
  branches_attr_by_lists(L) %>%
  plot()

# choose different color, and also change lwd and lty
dend %>%
  branches_attr_by_lists(L, TF_value = "blue") %>%
  branches_attr_by_lists(L, attr = "lwd", TF_value = 4) %>%
  branches_attr_by_lists(L, attr = "lty", TF_value = 3) %>%
  plot()

## End(Not run)

Description

Plot a circlized dendrograms using the circlize package (must be installed for the function to work).

This type of plot is also sometimes called fan tree plot (although the name fan-plot is also used for a different plot in time series analysis), radial tree plot, polar tree plot, circular tree plot, and probably other names as well.

An advantage for using the circlize package directly is for plotting a circular dendrogram so that you can add more graphics for the elements in the tree just by adding more tracks using circos.track.

Usage

circlize_dendrogram(
  dend,
  facing = c("outside", "inside"),
  labels = TRUE,
  labels_track_height = 0.1,
  dend_track_height = 0.5,
  ...
)

Arguments

Value

The dend that was used for plotting.

Author(s)

Zuguang Gu, Tal Galili

Source

This code is based on the work of Zuguang Gu. If you use the function, please cite both dendextend (see: citation("dendextend")), as well as the circlize package (see: citation("circlize")).

See Also

circos.dendrogram

Examples

## Not run: 

dend <- iris[1:40, -5] %>%
  dist() %>%
  hclust() %>%
  as.dendrogram() %>%
  set("branches_k_color", k = 3) %>%
  set("branches_lwd", c(5, 2, 1.5)) %>%
  set("branches_lty", c(1, 1, 3, 1, 1, 2)) %>%
  set("labels_colors") %>%
  set("labels_cex", c(.9, 1.2)) %>%
  set("nodes_pch", 19) %>%
  set("nodes_col", c("orange", "black", "plum", NA))

circlize_dendrogram(dend)
circlize_dendrogram(dend, labels = FALSE)
circlize_dendrogram(dend, facing = "inside", labels = FALSE)


# In the following we get the dendrogram but can also get extra information on top of it
circos.initialize("foo", xlim = c(0, 40))
circos.track(ylim = c(0, 1), panel.fun = function(x, y) {
  circos.rect(1:40 - 0.8, rep(0, 40), 1:40 - 0.2, runif(40), col = rand_color(40), border = NA)
}, bg.border = NA)
circos.track(ylim = c(0, 1), panel.fun = function(x, y) {
  circos.text(1:40 - 0.5, rep(0, 40), labels(dend),
    col = labels_colors(dend),
    facing = "clockwise", niceFacing = TRUE, adj = c(0, 0.5)
  )
}, bg.border = NA, track.height = 0.1)
max_height <- attr(dend, "height")
circos.track(ylim = c(0, max_height), panel.fun = function(x, y) {
  circos.dendrogram(dend, max_height = max_height)
}, track.height = 0.5, bg.border = NA)
circos.clear()

## End(Not run)

Description

Lets te user click a plot of dendrogram and rotates the tree based on the location of the click.

Code for mouse selection of (sub-)cluster to be rotated

Usage

click_rotate(x, ...)

## Default S3 method:
click_rotate(x, ...)

## S3 method for class 'dendrogram'
click_rotate(
  x,
  plot = TRUE,
  plot_after = plot,
  horiz = FALSE,
  continue = FALSE,
  ...
)

Arguments

Value

A rotated tree object

Author(s)

Andrej-Nikolai Spiess, Tal Galili

See Also

rotate.dendrogram

Examples

# create the dend:
dend <- USArrests %>%
  dist() %>%
  hclust("ave") %>%
  as.dendrogram() %>%
  color_labels()
## Not run: 
# play with the rotation once
dend <- click_rotate(dend)
dend <- click_rotate(dend, horiz = TRUE)
# keep playing with the rotation:
while (TRUE) dend <- click_rotate(dend)
# the same as
dend <- click_rotate(dend, continue = TRUE)

## End(Not run)

Description

Collapse branches under a tolerance level

Usage

collapse_branch(dend, tol = 1e-08, lower = TRUE, ...)

Arguments

Value

A dendrogram with both of the root's branches of the same height

See Also

multi2di

Examples

# # ladderize is like sort(..., type = "node")
dend <- iris[1:5, -5] %>%
  dist() %>%
  hclust() %>%
  as.dendrogram()
par(mfrow = c(1, 3))
dend %>%
  ladderize() %>%
  plot(horiz = TRUE)
abline(v = .2, col = 2, lty = 2)
dend %>%
  collapse_branch(tol = 0.2) %>%
  ladderize() %>%
  plot(horiz = TRUE)
dend %>%
  collapse_branch(tol = 0.2) %>%
  ladderize() %>%
  hang.dendrogram(hang = 0) %>%
  plot(horiz = TRUE)

par(mfrow = c(1, 2))
dend %>%
  collapse_branch(tol = 0.2, lower = FALSE) %>%
  plot(horiz = TRUE, main = "dendrogram")
library(ape)
dend %>%
  as.phylo() %>%
  di2multi(tol = 0.2) %>%
  plot(main = "phylo")

Description

Given a dendrogram object, and a set of labels that are in the same sub-dendrogram, the function performs a recursive DFS algorithm to determine the sub-dendrogram which is composed of (exactly) all 'selected_labels'. It then squashes this sub-dendrogram, and returns the original dendrogram with the squashed dendrogram with it.

Usage

collapse_labels(dend, selected_labels, ...)

Arguments

Value

Either the original dend. Or, if the labels properly are in the dend by each other, a dend with a squashed sub-dendrogram inside it.

Examples

library("dendextend")

set.seed(23235)
ss <- sample(1:150, 5)

# Getting the dend object
dend25 <- iris[ss, -5] %>%
   dist() %>%
   hclust() %>%
   as.dendrogram() %>% 
   set("labels", letters[1:5])

par(mfrow = c(1,4))
plot(dend25)
plot(collapse_labels(dend25, c("d", "e")))
plot(collapse_labels(dend25, c("c", "d", "e")))
plot(collapse_labels(dend25, c("c", "d", "e"), squashed_original_height=TRUE))

Description

This function is for dendrogram and hclust objects. This function colors both the terminal leaves of a dend's cluster and the edges leading to those leaves. The edgePar attribute of nodes will be augmented by a new list item col. The groups will be defined by a call to cutree using the k or h parameters.

If col is a color vector with a different length than the number of clusters (k) - then a recycled color vector will be used.

Usage

color_branches(
  dend,
  k = NULL,
  h = NULL,
  col,
  groupLabels = NULL,
  clusters,
  warn = dendextend_options("warn"),
  ...
)

Arguments

Details

If groupLabels=TRUE then numeric group labels will be added to each cluster. If a vector is supplied then these entries will be used as the group labels. If a function is supplied then it will be passed a numeric vector of groups (e.g. 1:5) and must return the formatted group labels.

If the labels of the dendrogram are NOT character (but, for example integers) - they are coerced into character. This step is essential for the proper operation of the function. A dendrogram labels might happen to be integers if they are based on an hclust performed on a dist of an object without rownames.

Value

a tree object of class dendrogram.

Author(s)

Tal Galili, extensively based on code by Gregory Jefferis

Source

This function is a derived work from the color_clusters function, with some ideas from the slice function - both are from the dendroextras package by jefferis.

It extends it by using cutree.dendrogram - allowing the function to work for trees that hclust can not handle (unbranched and non-ultrametric trees). Also, it allows REPEATED cluster color assignments to branches on to the same tree. Something which the original function was not able to handle.

See Also

cutree,dendrogram, hclust, labels_colors, branches_attr_by_clusters, get_leaves_branches_col, color_labels

Examples

## Not run: 
par(mfrow = c(1, 2))
dend <- USArrests %>%
  dist() %>%
  hclust(method = "ave") %>%
  as.dendrogram()
d1 <- color_branches(dend, k = 5, col = c(3, 1, 1, 4, 1))
plot(d1) # selective coloring of branches :)
d2 <- color_branches(dend, 5)
plot(d2)

par(mfrow = c(1, 2))
d1 <- color_branches(dend, 5, col = c(3, 1, 1, 4, 1), groupLabels = TRUE)
plot(d1) # selective coloring of branches :)
d2 <- color_branches(dend, 5, groupLabels = TRUE)
plot(d2)

par(mfrow = c(1, 3))
d5 <- color_branches(dend, 5)
plot(d5)
d5g <- color_branches(dend, 5, groupLabels = TRUE)
plot(d5g)
d5gr <- color_branches(dend, 5, groupLabels = as.roman)
plot(d5gr)

par(mfrow = c(1, 1))

# messy - but interesting:
dend_override <- color_branches(dend, 2, groupLabels = as.roman)
dend_override <- color_branches(dend_override, 4, groupLabels = as.roman)
dend_override <- color_branches(dend_override, 7, groupLabels = as.roman)
plot(dend_override)

d5 <- color_branches(dend = dend[[1]], k = 5)


library(dendextend)
data(iris, envir = environment())
d_iris <- dist(iris[, -5])
hc_iris <- hclust(d_iris)
dend_iris <- as.dendrogram(hc_iris)
dend_iris <- color_branches(dend_iris, k = 3)

library(colorspace)
labels_colors(dend_iris) <-
  rainbow_hcl(3)[sort_levels_values(
    as.numeric(iris[, 5])[order.dendrogram(dend_iris)]
  )]

plot(dend_iris,
  main = "Clustered Iris dataset",
  sub = "labels are colored based on the true cluster"
)



# cutree(dend_iris,k=3, order_clusters_as_data=FALSE,
#  try_cutree_hclust=FALSE)
# cutree(dend_iris,k=3, order_clusters_as_data=FALSE)

library(colorspace)

data(iris, envir = environment())
d_iris <- dist(iris[, -5])
hc_iris <- hclust(d_iris)
labels(hc_iris) # no labels, because "iris" has no row names
dend_iris <- as.dendrogram(hc_iris)
is.integer(labels(dend_iris)) # this could cause problems...

iris_species <- rev(levels(iris[, 5]))
dend_iris <- color_branches(dend_iris, k = 3, groupLabels = iris_species)
is.character(labels(dend_iris)) # labels are no longer "integer"

# have the labels match the real classification of the flowers:
labels_colors(dend_iris) <-
  rainbow_hcl(3)[sort_levels_values(
    as.numeric(iris[, 5])[order.dendrogram(dend_iris)]
  )]

# We'll add the flower type
labels(dend_iris) <- paste(as.character(iris[, 5])[order.dendrogram(dend_iris)],
  "(", labels(dend_iris), ")",
  sep = ""
)

dend_iris <- hang.dendrogram(dend_iris, hang_height = 0.1)

# reduce the size of the labels:
dend_iris <- assign_values_to_leaves_nodePar(dend_iris, 0.5, "lab.cex")

par(mar = c(3, 3, 3, 7))
plot(dend_iris,
  main = "Clustered Iris dataset
     (the labels give the true flower species)",
  horiz = TRUE, nodePar = list(cex = .007)
)
legend("topleft", legend = iris_species, fill = rainbow_hcl(3))
a <- dend_iris[[1]]
dend_iris1 <- color_branches(a, k = 3)
plot(dend_iris1)

# str(dendrapply(d2, unclass))
# unclass(d1)

c(1:5) %>% # take some data
  dist() %>% # calculate a distance matrix,
  # on it compute hierarchical clustering using the "average" method,
  hclust(method = "single") %>%
  as.dendrogram() %>%
  color_branches(k = 3) %>%
  plot() # nice, returns the tree as is...


# Example of the "clusters" parameter
par(mfrow = c(1, 2))
dend <- c(1:5) %>%
  dist() %>%
  hclust() %>%
  as.dendrogram()
dend %>%
  color_branches(k = 3) %>%
  plot()
dend %>%
  color_branches(clusters = c(1, 1, 2, 2, 3)) %>%
  plot()


# another example, based on the question here:
# https://stackoverflow.com/q/45432271/256662


library(cluster)
set.seed(999)
iris2 <- iris[sample(x = 1:150, size = 50, replace = F), ]
clust <- diana(iris2)
dend <- as.dendrogram(clust)

temp_col <- c("red", "blue", "green")[as.numeric(iris2$Species)]
temp_col <- temp_col[order.dendrogram(dend)]
temp_col <- factor(temp_col, unique(temp_col))

library(dendextend)
dend %>%
  color_branches(clusters = as.numeric(temp_col), col = levels(temp_col)) %>%
  set("labels_colors", as.character(temp_col)) %>%
  plot()

## End(Not run)

Description

This function is for dendrogram and hclust objects. This function colors tree's labels.

The groups will be defined by a call to cutree using the k or h parameters.

If col is a color vector with a different length than the number of clusters (k) - then a recycled color vector will be used.

Usage

color_labels(
  dend,
  k = NULL,
  h = NULL,
  labels,
  col,
  warn = dendextend_options("warn"),
  ...
)

Arguments

Value

a tree object of class dendrogram.

Source

This function is in the style of color_branches, and based on labels_colors.

See Also

cutree,dendrogram, hclust, labels_colors, color_branches, assign_values_to_leaves_edgePar

Examples

## Not run: 
hc <- hclust(dist(USArrests), "ave")
dend <- as.dendrogram(hc)
dend <- color_labels(dend, 5, col = c(3, 1, 1, 4, 1))
dend <- color_branches(dend, 5, col = c(3, 1, 1, 4, 1))
plot(dend) # selective coloring of branches AND labels :)

# coloring some labels, based on label names:
dend <- color_labels(dend, col = "red", labels = labels(dend)[c(4, 16)])
plot(dend) # selective coloring of branches AND labels :)

d5 <- color_branches(dend, 5)
plot(d5)
d5g <- color_branches(dend, 5, groupLabels = TRUE)
plot(d5g)
d5gr <- color_branches(dend, 5, groupLabels = as.roman)
plot(d5gr)

## End(Not run)

Description

Color unique labels in a dendrogram

Usage

color_unique_labels(dend, ...)

Arguments

Value

A dendrogram after the colors of its labels have been updated (a different color for each unique label).

Examples

x <- c(2011, 2011, 2012, 2012, 2015, 2015, 2015)
names(x) <- x
dend <- as.dendrogram(hclust(dist(x)))

par(mfrow = c(1, 2))
plot(dend)
dend2 <- color_unique_labels(dend)
plot(dend2)

Description

Add colored bars to a dendrogram, usually corresponding to either clusters or some outside categorization.

Usage

colored_bars(
  colors,
  dend,
  rowLabels = NULL,
  cex.rowLabels = 0.9,
  add = TRUE,
  y_scale,
  y_shift,
  text_shift = 1,
  sort_by_labels_order = TRUE,
  horiz = FALSE,
  ...
)

Arguments

Details

You will often needs to adjust the y_scale, y_shift and the text_shift parameters, in order to get the bars in the location you would want.

(this can probably be done automatically, but will require more work. since it has to do with the current mar settings, the number of groups, and each computer's specific graphic device. patches for smarter defaults will be appreciated)

Value

An invisible vector/matrix with the ordered colors.

Author(s)

Steve Horvath [email protected], Peter Langfelder [email protected], Tal Galili [email protected]

Source

This function is based on the plotHclustColors from the moduleColor R package. It was modified so that it would work with dendrograms (and not just hclust objects), as well allow to add the colored bars on top of an existing plot (and not only as a seperate plot).

See: https://cran.r-project.org/package=moduleColor For more details.

See Also

branches_attr_by_clusters, plotDendroAndColors

Examples

rows_picking <- c(1:5, 25:30)
dend <- (iris[rows_picking, -5] * 10) %>%
  dist() %>%
  hclust() %>%
  as.dendrogram()
odd_numbers <- rows_picking %% 2
cols <- c("gold", "grey")[odd_numbers + 1]
# scale is off
plot(dend)
colored_bars(cols, dend)
# move and scale a bit
plot(dend)
colored_bars(cols, dend,
  y_shift = -1,
  rowLabels = "Odd\n numbers"
)
# Now let's cut the tree and add that info to the plot:
k2 <- cutree(dend, k = 2)
cols2 <- c("#0082CE", "#CC476B")[k2]
plot(dend)
colored_bars(cbind(cols2, cols), dend,
  rowLabels = c("2 clusters", "Odd numbers")
)

# The same, but with an horizontal plot!
par(mar = c(6, 2, 2, 4))
plot(dend, horiz = TRUE)
colored_bars(cbind(cols2, cols), dend,
  rowLabels = c("2 clusters", "Odd numbers"),
  horiz = TRUE
)



# let's add clusters color
# notice how we need to play with the colors a bit
# this is because color_branches places colors from
# left to right. Which means we need to give colored_bars
# the colors of the items so that ofter sorting they would be
# from left to right. Here is how it can be done:
the_k <- 3
library(colorspace)
cols3 <- rainbow_hcl(the_k, c = 90, l = 50)
dend %>%
  set("branches_k_color", k = the_k, with = cols3) %>%
  plot()

kx <- cutree(dend, k = the_k)
ord <- order.dendrogram(dend)
kx <- sort_levels_values(kx[ord])
kx <- kx[match(seq_along(ord), ord)]

par(mar = c(5, 5, 2, 2))
plot(dend)
colored_bars(cbind(cols3[kx], cols2, cols), dend,
  rowLabels = c("3 clusters", "2 clusters", "Odd numbers")
)



## mtcars example

# Create the dend:
dend <- as.dendrogram(hclust(dist(mtcars)))

# Create a vector giving a color for each car to which company it belongs to
car_type <- rep("Other", length(rownames(mtcars)))
is_x <- grepl("Merc", rownames(mtcars))
car_type[is_x] <- "Mercedes"
is_x <- grepl("Mazda", rownames(mtcars))
car_type[is_x] <- "Mazda"
is_x <- grepl("Toyota", rownames(mtcars))
car_type[is_x] <- "Toyota"
car_type <- factor(car_type)
n_car_types <- length(unique(car_type))
col_car_type <- colorspace::rainbow_hcl(n_car_types, c = 70, l = 50)[car_type]

# extra: showing the various clusters cuts
k234 <- cutree(dend, k = 2:4)

# color labels by car company:
labels_colors(dend) <- col_car_type[order.dendrogram(dend)]
# color branches based on cutting the tree into 4 clusters:
dend <- color_branches(dend, k = 4)

### plots
par(mar = c(12, 4, 1, 1))
plot(dend)
colored_bars(cbind(k234[, 3:1], col_car_type), dend,
  rowLabels = c(paste0("k = ", 4:2), "Car Type")
)

# horiz version:
par(mar = c(4, 1, 1, 12))
plot(dend, horiz = TRUE)
colored_bars(cbind(k234[, 3:1], col_car_type), dend,
  rowLabels = c(paste0("k = ", 4:2), "Car Type"), horiz = TRUE
)

Description

Add colored dots next to a dendrogram, usually corresponding to either clusters or some outside categorization.

Usage

colored_dots(
  colors,
  dend,
  rowLabels = NULL,
  cex.rowLabels = 0.9,
  add = TRUE,
  y_scale,
  y_shift,
  text_shift = 1,
  sort_by_labels_order = TRUE,
  horiz = FALSE,
  dot_size = 1,
  ...
)

Arguments

Details

The reason you might choose colored_dots over colored_bars is when you have a lot of group types and/or a really large dendrogram. Hint: Make a group for each categorical factor and color it one color when true, and assign a fully transparent color when false.

You will often need to adjust the y_scale, y_shift and the text_shift parameters, in order to get the dots in the location you would want.

(This can probably be done automatically, but will require more work. since it has to do with the current mar settings, the number of groups, and each computer's specific graphic device. patches for smarter defaults will be appreciated)

Value

An invisible vector/matrix with the ordered colors.

Author(s)

Steve Horvath [email protected], Tal Galili [email protected], Peter Langfelder [email protected], Chase Clark [email protected]

Source

This function is based on the plotHclustColors from the moduleColor R package. It was modified so that it would work with dendrograms (and not just hclust objects), as well allow to add the colored dots on top of an existing plot (and not only as a seperate plot).

See: https://cran.r-project.org/package=moduleColor For more details.

See Also

branches_attr_by_clusters, plotDendroAndColors

Examples

rows_picking <- c(1:5, 25:30)
dend <- (iris[rows_picking, -5] * 10) %>%
  dist() %>%
  hclust() %>%
  as.dendrogram()
odd_numbers <- rows_picking %% 2
cols <- c("red", "white")[odd_numbers + 1]
plot(dend)
colored_dots(cols, dend)
# Example of adjusting postion of dots
plot(dend)
colored_dots(cols, dend,
  y_shift = -1,
  rowLabels = "Odd\n numbers"
)



rows_picking <- c(1:5, 25:30)
dend <- (iris[rows_picking, -5] * 10) %>%
  dist() %>%
  hclust() %>%
  as.dendrogram()
odd_numbers <- rows_picking %% 2
# For leaves that shouldn't have dots, make them the same color as the background,
# or set the alpha value to fully transparant
cols <- c("black", "white")[odd_numbers + 1]
# scale is off
plot(dend)
colored_dots(cols, dend)
# move and scale a bit
plot(dend)
colored_dots(cols, dend,
  y_shift = -1,
  rowLabels = "Odd\n numbers"
)
# Now let's cut the tree and add that info to the plot:
k2 <- cutree(dend, k = 2)
cols2 <- c("#1b9e77", "#d95f02")[k2]

par(mar = c(5, 6, 1, 1))
plot(dend)
colored_dots(cbind(cols2, cols), dend,
  rowLabels = c("2 clusters", "Even numbers")
)

# The same, but with an horizontal plot!
par(mar = c(6, 2, 2, 4))
plot(dend, horiz = TRUE)
colored_dots(cbind(cols2, cols), dend,
  rowLabels = c("2 clusters", "Even numbers"),
  horiz = TRUE
)

# ==============================
# ==============================

## mtcars example

# Create the dend:
dend <- as.dendrogram(hclust(dist(mtcars)))

# Get all company names
comp_names <- unlist(lapply(rownames(mtcars), function(x) strsplit(x, " ")[[1]][[1]]))
# Get the top three occurring companies
top_three <- sort(table(comp_names), decreasing = TRUE)[1:3]
# Match the top three companies to where they are found in the dendrogram labels
top_three <- sapply(names(top_three), function(x) grepl(x, labels(dend)))
top_three <- as.data.frame(top_three)
# "top_three" is now a data frame of the top three companies as columns.
# Each column represents a vector (rows) which is the length of labels(dend).
# The vector has values TRUE and FALSE, for whether the company name matched
# labels(dend)[i]

# Colorblind friendly vector of HEX colors
colorblind_friendly <- c("#1b9e77", "#d95f02", "#7570b3")

# If we run the for-loop on "top_three" we will turn the vectors into a character-type too early,
# so make a copy to "colored_dataframe" which we will work on
colored_dataframe <- top_three

for (i in 1:3) {
  # This replaces TRUE values with a color from our vector of colors
  colored_dataframe[top_three[, i], i] <- colorblind_friendly[[i]]
  # This replaces FALSE values with black HEX, but fully transparent (invisible on plot)
  colored_dataframe[!top_three[, i], i] <- "#00000000"
}

# Color branches and labels by "cutting" the dendrogram at an arbitrary height
dend <- color_branches(dend, h = 170)
dend <- color_labels(dend, h = 170)

### plots
par(mar = c(12, 4, 1, 1))
plot(dend)
colored_dots(colored_dataframe, dend,
  rowLabels = colnames(colored_dataframe), horiz = FALSE, sort_by_labels_order = FALSE
)
# Show a dotted line where tree was "cut"
abline(h = 170, lty = 3)

# horiz version:
par(mar = c(4, 1, 1, 12))
plot(dend, horiz = TRUE)
colored_dots(colored_dataframe, dend,
  rowLabels = colnames(colored_dataframe), horiz = TRUE, sort_by_labels_order = FALSE
)
# Show a dotted line where the tree was "cut"
abline(v = 170, lty = 3)

Description

Gets a dend and the output from "nodes_with_shared_labels" and returns a vector (length of labels), indicating the clusters forming shared subtrees

Usage

common_subtrees_clusters(dend1, dend2, leaves_get_0_cluster = TRUE, ...)

Arguments

Value

An integer vector, with values indicating which leaves in dend1 form a common subtree cluster, with ones available in dend2

See Also

color_branches, tanglegram

Examples

library(dendextend)
dend1 <- 1:6 %>%
  dist() %>%
  hclust() %>%
  as.dendrogram()
dend2 <- dend1 %>% set("labels", c(1:4, 6:5))
tanglegram(dend1, dend2)

clusters1 <- common_subtrees_clusters(dend1, dend2)
dend1_2 <- color_branches(dend1, clusters = clusters1)
plot(dend1_2)
plot(dend1_2, horiz = TRUE)
tanglegram(dend1_2, dend2, highlight_distinct_edges = FALSE)
tanglegram(dend1_2, dend2)

Description

Calculate Baker's Gamma correlation coefficient for two trees (also known as Goodman-Kruskal-gamma index).

Assumes the labels in the two trees fully match. If they do not please first use intersect_trees to have them matched.

WARNING: this can be quite slow for medium/large trees.

Usage

cor_bakers_gamma(dend1, ...)

## Default S3 method:
cor_bakers_gamma(dend1, dend2, ...)

## S3 method for class 'dendrogram'
cor_bakers_gamma(
  dend1,
  dend2,
  use_labels_not_values = TRUE,
  to_plot = FALSE,
  warn = dendextend_options("warn"),
  ...
)

## S3 method for class 'hclust'
cor_bakers_gamma(
  dend1,
  dend2,
  use_labels_not_values = TRUE,
  to_plot = FALSE,
  warn = dendextend_options("warn"),
  ...
)

## S3 method for class 'dendlist'
cor_bakers_gamma(dend1, which = c(1L, 2L), ...)

Arguments

Details

Baker's Gamma (see reference) is a measure of accosiation (similarity) between two trees of heirarchical clustering (dendrograms).

It is calculated by taking two items, and see what is the heighst possible level of k (number of cluster groups created when cutting the tree) for which the two item still belongs to the same tree. That k is returned, and the same is done for these two items for the second tree. There are n over 2 combinations of such pairs of items from the items in the tree, and all of these numbers are calculated for each of the two trees. Then, these two sets of numbers (a set for the items in each tree) are paired according to the pairs of items compared, and a spearman correlation is calculated.

The value can range between -1 to 1. With near 0 values meaning that the two trees are not statistically similar. For exact p-value one should result to a permutation test. One such option will be to permute over the labels of one tree many times, and calculating the distriubtion under the null hypothesis (keeping the trees topologies constant).

Notice that this measure is not affected by the height of a branch but only of its relative position compared with other branches.

Value

Baker's Gamma association Index between two trees (a number between -1 to 1)

References

Baker, F. B., Stability of Two Hierarchical Grouping Techniques Case 1: Sensitivity to Data Errors. Journal of the American Statistical Association, 69(346), 440 (1974).

See Also

cor_cophenetic

Examples

## Not run: 

set.seed(23235)
ss <- sample(1:150, 10)
hc1 <- hclust(dist(iris[ss, -5]), "com")
hc2 <- hclust(dist(iris[ss, -5]), "single")
dend1 <- as.dendrogram(hc1)
dend2 <- as.dendrogram(hc2)
#    cutree(dend1)

cor_bakers_gamma(hc1, hc2)
cor_bakers_gamma(dend1, dend2)

dend1 <- match_order_by_labels(dend1, dend2) # if you are not sure
cor_bakers_gamma(dend1, dend2, use_labels_not_values = FALSE)

library(microbenchmark)
microbenchmark(
  with_labels = cor_bakers_gamma(dend1, dend2, try_cutree_hclust = FALSE),
  with_values = cor_bakers_gamma(dend1, dend2,
    use_labels_not_values = FALSE, try_cutree_hclust = FALSE
  ),
  times = 10
)


cor_bakers_gamma(dend1, dend1, use_labels_not_values = FALSE)
cor_bakers_gamma(dend1, dend1, use_labels_not_values = TRUE)

## End(Not run)

Description

Calculates the number of nodes, in each tree, that are common (i.e.: that have the same exact list of labels). The correlation is between 0 (actually, 2*(nnodes-1)/(2*nnodes), for two trees with the same list of labels - since the top node will always be identical for them). Where 1 means that every node in the one tree, has a node in the other tree with the exact same list of labels. Notice this measure is non-parameteric (it ignores the heights and relative position of the nodes).

Usage

cor_common_nodes(dend1, dend2, ...)

Arguments

Value

A correlation value between 0 to 1 (almost identical trees)

See Also

distinct_edges, cor.dendlist

Examples

set.seed(23235)
ss <- sample(1:150, 10)
hc1 <- iris[ss, -5] %>%
  dist() %>%
  hclust("com")
hc2 <- iris[ss, -5] %>%
  dist() %>%
  hclust("single")
dend1 <- as.dendrogram(hc1)
dend2 <- as.dendrogram(hc2)

cor_cophenetic(dend1, dend2)
cor_common_nodes(dend1, dend2)
tanglegram(dend1, dend2)
# we can see we have only two nodes which are different...

Description

Cophenetic correlation coefficient for two trees.

Assumes the labels in the two trees fully match. If they do not please first use intersect_trees to have them matched.

Usage

cor_cophenetic(dend1, ...)

## Default S3 method:
cor_cophenetic(
  dend1,
  dend2,
  method_coef = c("pearson", "kendall", "spearman"),
  ...
)

## S3 method for class 'dendlist'
cor_cophenetic(
  dend1,
  which = c(1L, 2L),
  method_coef = c("pearson", "kendall", "spearman"),
  ...
)

Arguments

Details

From cophenetic: The cophenetic distance between two observations that have been clustered is defined to be the intergroup dissimilarity at which the two observations are first combined into a single cluster. Note that this distance has many ties and restrictions.

cor_cophenetic calculates the correlation between two cophenetic distance matrices of the two trees.

The value can range between -1 to 1. With near 0 values meaning that the two trees are not statistically similar. For exact p-value one should result to a permutation test. One such option will be to permute over the labels of one tree many times, and calculating the distriubtion under the null hypothesis (keeping the trees topologies constant).

Notice that this measure IS affected by the height of a branch.

Value

The correlation between cophenetic

References

Sokal, R. R. and F. J. Rohlf. 1962. The comparison of dendrograms by objective methods. Taxon, 11:33-40

Sneath, P.H.A. and Sokal, R.R. (1973) Numerical Taxonomy: The Principles and Practice of Numerical Classification, p. 278 ff; Freeman, San Francisco.

https://en.wikipedia.org/wiki/Cophenetic_correlation

See Also

cophenetic, cor_bakers_gamma

Examples

## Not run: 

set.seed(23235)
ss <- sample(1:150, 10)
hc1 <- iris[ss, -5] %>%
  dist() %>%
  hclust("com")
hc2 <- iris[ss, -5] %>%
  dist() %>%
  hclust("single")
dend1 <- as.dendrogram(hc1)
dend2 <- as.dendrogram(hc2)
#    cutree(dend1)

cophenetic(hc1)
cophenetic(hc2)
# notice how the dist matrix for the dendrograms have different orders:
cophenetic(dend1)
cophenetic(dend2)

cor(cophenetic(hc1), cophenetic(hc2)) # 0.874
cor(cophenetic(dend1), cophenetic(dend2)) # 0.16
# the difference is becasue the order of the distance table in the case of
# stats:::cophenetic.dendrogram will change between dendrograms!

# however, this is consistant (since I force-sort the rows/columns):
cor_cophenetic(hc1, hc2)
cor_cophenetic(dend1, dend2)

cor_cophenetic(dendlist(dend1, dend2))

# we can also use different cor methods (almost the same result though):
cor_cophenetic(hc1, hc2, method = "spearman") # 0.8456014
cor_cophenetic(dend1, dend2, method = "spearman") #


# cophenetic correlation is about 10 times (!) faster than bakers_gamma cor:
library(microbenchmark)
microbenchmark(
  cor_bakers_gamma = cor_bakers_gamma(dend1, dend2, try_cutree_hclust = FALSE),
  cor_cophenetic = cor_cophenetic(dend1, dend2),
  times = 10
)

# but only because of the cutree for dendrogram. When allowing hclust cutree
# it is only about twice as fast:
microbenchmark(
  cor_bakers_gamma = cor_bakers_gamma(dend1, dend2, try_cutree_hclust = TRUE),
  cor_cophenetic = cor_cophenetic(dend1, dend2),
  times = 10
)

## End(Not run)

Description

Calculates the FM_index Correlation for some k.

Usage

cor_FM_index(dend1, dend2, k, ...)

Arguments

Value

A correlation value between 0 to 1 (almost identical clusters for some k)

See Also

FM_index, cor.dendlist, Bk

Examples

set.seed(23235)
ss <- sample(1:150, 10)
hc1 <- iris[ss, -5] %>%
  dist() %>%
  hclust("com")
hc2 <- iris[ss, -5] %>%
  dist() %>%
  hclust("single")
dend1 <- as.dendrogram(hc1)
dend2 <- as.dendrogram(hc2)

cor_FM_index(dend1, dend2, k = 2)
cor_FM_index(dend1, dend2, k = 3)
cor_FM_index(dend1, dend2, k = 4)

Description

A correlation matrix between a list of trees.

Assumes the labels in the two trees fully match. If they do not please first use intersect_trees to have them matched.

Usage

cor.dendlist(
  dend,
  method = c("cophenetic", "baker", "common_nodes", "FM_index"),
  ...
)

Arguments

Value

A correlation matrix between the different trees

See Also

cophenetic, cor_cophenetic, cor_bakers_gamma, cor_common_nodes, cor_FM_index

Examples

## Not run: 

set.seed(23235)
ss <- sample(1:150, 10)
dend1 <- iris[ss, -5] %>%
  dist() %>%
  hclust("com") %>%
  as.dendrogram()
dend2 <- iris[ss, -5] %>%
  dist() %>%
  hclust("single") %>%
  as.dendrogram()
dend3 <- iris[ss, -5] %>%
  dist() %>%
  hclust("ave") %>%
  as.dendrogram()
dend4 <- iris[ss, -5] %>%
  dist() %>%
  hclust("centroid") %>%
  as.dendrogram()
#    cutree(dend1)
cors <- cor.dendlist(dendlist(d1 = dend1, d2 = dend2, d3 = dend3, d4 = dend4))

cors

# a nice plot for them:
library(corrplot)
corrplot(cor.dendlist(dend1234), "pie", "lower")

## End(Not run)

Description

This function counts the number of "practical" terminal nodes (nodes which are not leaves, but has 0 height to them are considered "terminal" nodes). If the tree is standard, that would simply be the number of leaves (only the leaves will have height 0). However, in cases where the tree has several nodes (before the leaves) with 0 height, the count_terminal_nodes counts such nodes as terminal nodes

The function is recursive in that it either returns 1 if it reached a terminal node (either a leaf or a 0 height node), else: it will count the number of terminal nodes in each of its sub-nodes, sum them up, and return them.

Usage

count_terminal_nodes(dend_node, ...)

Arguments

Value

The number of terminal nodes (excluding the leaves of nodes of height 0)

Examples

# define dendrogram object to play with:
hc <- hclust(dist(USArrests[1:3, ]), "ave")
dend <- as.dendrogram(hc)

###
# Trivial case
count_terminal_nodes(dend) # 3 terminal nodes
length(labels(dend)) # 3 - the same number
plot(dend,
  main = "This is considered a tree \n with THREE terminal nodes (leaves)"
)

###
# NON-Trivial case
str(dend)
attr(dend[[2]], "height") <- 0
count_terminal_nodes(dend) # 2 terminal nodes, why? see this plot:
plot(dend,
  main = "This is considered a tree \n with TWO terminal nodes only"
)
# while we have 3 leaves, in practice we have only 2 terminal nodes
# (this is a feature, not a bug.)

Description

Cuts the dend at height h and returns a list with the FUN function implemented on all the sub trees created by cut at height h. This is used for creating a cutree.dendrogram function, by using the labels function as FUN.

This is the Rcpp version of the function, offering a 10-60 times improvement in speed (depending on the tree size it is used on).

Usage

cut_lower_fun(dend, h, FUN = labels, warn = dendextend_options("warn"), ...)

Arguments

Value

A list with the output of running FUN on each of the sub dends derived from cutting "dend"

Author(s)

Tal Galili

See Also

labels, dendrogram, cutree.dendrogram

Examples

dend <- as.dendrogram(hclust(dist(iris[1:4, -5])))
# this is really cool!
cut_lower_fun(dend, .4, labels)
lapply(cut(dend, h = .4)$lower, labels)
cut_lower_fun(dend, .4, order.dendrogram)

Description

Cuts a dendrogram tree into several groups by specifying the desired number of clusters k(s), or cut height(s).

For hclust.dendrogram - In case there exists no such k for which exists a relevant split of the dendrogram, a warning is issued to the user, and NA is returned.

Usage

cutree(tree, k = NULL, h = NULL, ...)

## Default S3 method:
cutree(tree, k = NULL, h = NULL, ...)

## S3 method for class 'hclust'
cutree(
  tree,
  k = NULL,
  h = NULL,
  use_labels_not_values = TRUE,
  order_clusters_as_data = TRUE,
  warn = dendextend_options("warn"),
  NA_to_0L = TRUE,
  ...
)

## S3 method for class 'phylo'
cutree(tree, k = NULL, h = NULL, ...)

## S3 method for class 'phylo'
cutree(tree, k = NULL, h = NULL, ...)

## S3 method for class 'agnes'
cutree(tree, k = NULL, h = NULL, ...)

## S3 method for class 'diana'
cutree(tree, k = NULL, h = NULL, ...)

## S3 method for class 'dendrogram'
cutree(
  tree,
  k = NULL,
  h = NULL,
  dend_heights_per_k = NULL,
  use_labels_not_values = TRUE,
  order_clusters_as_data = TRUE,
  warn = dendextend_options("warn"),
  try_cutree_hclust = TRUE,
  NA_to_0L = TRUE,
  ...
)

Arguments

Details

At least one of k or h must be specified, k overrides h if both are given.

as opposed to cutree for hclust, cutree.dendrogram allows the cutting of trees at a given height also for non-ultrametric trees (ultrametric tree == a tree with monotone clustering heights).

Value

If k or h are scalar - cutree.dendrogram returns an integer vector with group memberships. Otherwise a matrix with group memberships is returned where each column corresponds to the elements of k or h, respectively (which are also used as column names).

In case there exists no such k for which exists a relevant split of the dendrogram, a warning is issued to the user, and NA is returned.

Author(s)

cutree.dendrogram was written by Tal Galili. cutree.hclust is redirecting the function to cutree from base R.

See Also

hclust, cutree, cutree_1h.dendrogram, cutree_1k.dendrogram,

Examples

## Not run: 
hc <- hclust(dist(USArrests[c(1, 6, 13, 20, 23), ]), "ave")
dend <- as.dendrogram(hc)
unbranch_dend <- unbranch(dend, 2)

cutree(hc, k = 2:4) # on hclust
cutree(dend, k = 2:4) # on dendrogram

cutree(hc, k = 2) # on hclust
cutree(dend, k = 2) # on dendrogram

cutree(dend, h = c(20, 25.5, 50, 170))
cutree(hc, h = c(20, 25.5, 50, 170))

# the default (ordered by original data's order)
cutree(dend, k = 2:3, order_clusters_as_data = FALSE)
labels(dend)

# as.hclust(unbranch_dend) # ERROR - can not do this...
cutree(unbranch_dend, k = 2) # all NA's
cutree(unbranch_dend, k = 1:4)
cutree(unbranch_dend, h = c(20, 25.5, 50, 170))
cutree(dend, h = c(20, 25.5, 50, 170))


library(microbenchmark)
## this shows how as.hclust is expensive - but still worth it if possible
microbenchmark(
  cutree(hc, k = 2:4),
  cutree(as.hclust(dend), k = 2:4),
  cutree(dend, k = 2:4),
  cutree(dend, k = 2:4, try_cutree_hclust = FALSE)
)
# the dendrogram is MUCH slower...

# Unit: microseconds
##                       expr      min       lq    median        uq       max neval
##        cutree(hc, k = 2:4)   91.270   96.589   99.3885  107.5075   338.758   100
##    tree(as.hclust(dend),
## 			  k = 2:4)           1701.629 1767.700 1854.4895 2029.1875  8736.591   100
##      cutree(dend, k = 2:4) 1807.456 1869.887 1963.3960 2125.2155  5579.705   100
##  cutree(dend, k = 2:4,
## 	try_cutree_hclust = FALSE) 8393.914 8570.852 8755.3490 9686.7930 14194.790   100

# and trying to "hclust" is not expensive (which is nice...)
microbenchmark(
  cutree_unbranch_dend = cutree(unbranch_dend, k = 2:4),
  cutree_unbranch_dend_not_trying_to_hclust =
    cutree(unbranch_dend, k = 2:4, try_cutree_hclust = FALSE)
)


## Unit: milliseconds
##                   expr      min       lq   median       uq      max neval
## cutree_unbranch_dend       7.309329 7.428314 7.494107 7.752234 17.59581   100
## cutree_unbranch_dend_not
## _trying_to_hclust        6.945375 7.079198 7.148629 7.577536 16.99780   100
## There were 50 or more warnings (use warnings() to see the first 50)

# notice that if cutree can't find clusters for the desired k/h, it will produce 0's instead!
# (It will produce a warning though...)
# This is a different behaviout than stats::cutree
# For example:
cutree(as.dendrogram(hclust(dist(c(1, 1, 1, 2, 2)))),
  k = 5
)

## End(Not run)

Description

Cuts a dendrogram tree into several groups by specifying the desired cut height (only a single height!).

Usage

cutree_1h.dendrogram(
  dend,
  h,
  order_clusters_as_data = TRUE,
  use_labels_not_values = TRUE,
  warn = dendextend_options("warn"),
  ...
)

Arguments

Value

cutree_1h.dendrogram returns an integer vector with group memberships

Author(s)

Tal Galili

See Also

hclust, cutree

Examples

hc <- hclust(dist(USArrests[c(1, 6, 13, 20, 23), ]), "ave")
dend <- as.dendrogram(hc)
cutree(hc, h = 50) # on hclust
cutree_1h.dendrogram(dend, h = 50) # on a dendrogram

labels(dend)

# the default (ordered by original data's order)
cutree_1h.dendrogram(dend, h = 50, order_clusters_as_data = TRUE)

# A different order of labels - order by their order in the tree
cutree_1h.dendrogram(dend, h = 50, order_clusters_as_data = FALSE)


# make it faster
## Not run: 
library(microbenchmark)
microbenchmark(
  cutree_1h.dendrogram(dend, h = 50),
  cutree_1h.dendrogram(dend, h = 50, use_labels_not_values = FALSE)
)
# 0.8 vs 0.6 sec - for 100 runs

## End(Not run)

Description

Cuts a dendrogram tree into several groups by specifying the desired number of clusters k (only a single k value!).

In case there exists no such k for which exists a relevant split of the dendrogram, a warning is issued to the user, and NA is returned.

Usage

cutree_1k.dendrogram(
  dend,
  k,
  dend_heights_per_k = NULL,
  use_labels_not_values = TRUE,
  order_clusters_as_data = TRUE,
  warn = dendextend_options("warn"),
  ...
)

Arguments

Value

cutree_1k.dendrogram returns an integer vector with group memberships.

In case there exists no such k for which exists a relevant split of the dendrogram, a warning is issued to the user, and NA is returned.

Author(s)

Tal Galili

See Also

hclust, cutree, cutree_1h.dendrogram

Examples

hc <- hclust(dist(USArrests[c(1, 6, 13, 20, 23), ]), "ave")
dend <- as.dendrogram(hc)
cutree(hc, k = 3) # on hclust
cutree_1k.dendrogram(dend, k = 3) # on a dendrogram

labels(dend)

# the default (ordered by original data's order)
cutree_1k.dendrogram(dend, k = 3, order_clusters_as_data = TRUE)

# A different order of labels - order by their order in the tree
cutree_1k.dendrogram(dend, k = 3, order_clusters_as_data = FALSE)


# make it faster
## Not run: 
library(microbenchmark)
dend_ks <- heights_per_k.dendrogram
microbenchmark(
  cutree_1k.dendrogram = cutree_1k.dendrogram(dend, k = 4),
  cutree_1k.dendrogram_no_labels = cutree_1k.dendrogram(dend,
    k = 4, use_labels_not_values = FALSE
  ),
  cutree_1k.dendrogram_no_labels_per_k = cutree_1k.dendrogram(dend,
    k = 4, use_labels_not_values = FALSE,
    dend_heights_per_k = dend_ks
  )
)
# the last one is the fastest...

## End(Not run)