Cognition of Clustering Analysis
For a better visualization and sensitivity analysis, on this project we used several R statistical packages:
Dedendextend-package {dendextend}is a function for extending dendrogram objects. For general tree structure there isDendrogram {stats}, class “dendrogram” provides general functions for handling tree-like structures. It is intended as a replacement for similar functions in hierarchical clustering and classification/regression trees, such that all of these can use the same engine for plotting or cutting trees. Hierarchical cluster analysishclust {stats}on a set of dissimilarities and methods for analyzing cluster. Furthermore,stats-package {stats}, this package contains functions for statistical calculations and random number generation.Knitr-package {knitr}takes an input file, extracts the R code in it according to a list of patterns, evaluates the code and writes the output in another file.Gplot-package {gplots}is a vignette summarises the various formats that grid drawing functions take.Corrplot-package (corrplot}is a graphical display of a correlation matrix, confidence interval. It also contains some algorithms to do matrix reordering. In addition, corrplot is good at details, including choosing color, text labels, color labels, layout, etc.
Iris for flower species, repub for presidential vote, animals for animal clustering, and microarrray gene for train and test expression, those 4 datasets will be used on this project.
library("DT")
iris <- datasets::iris
datatable(iris, options=list(pageLength = 4, scrollX=T))library("DT")
votes.repub <- cluster::votes.repub
datatable(votes.repub, options=list(pageLength = 4, scrollX=T))library("DT")
animals <- cluster::animals
datatable(animals, options=list(pageLength = 4, scrollX=T))
Study Cases
Flower Species
library(dendextend)
library(knitr)
knitr::opts_chunk$set(
cache = TRUE,
dpi = 75,
fig.width = 6, fig.height = 6,
tidy = FALSE)iris <- datasets::iris
iris2 <- iris[,-5]
species_labels <- iris[,5]
library(colorspace) # get nice colors
hcl_palettes(plot = TRUE)
(palette = "Dark 3")## [1] "Dark 3"species_col <- rev(rainbow_hcl(3))[as.numeric(species_labels)]# Plot a SPLOM:
pairs(iris2, col = species_col,
lower.panel = NULL,
cex.labels=1.1, pch=20, cex = 1.2)
# Add a legend
par(xpd = TRUE)
legend(x = 0.01, y = 0.2, cex = 1,
legend = as.character(levels(species_labels)),
fill = unique(species_col))
par(xpd = NA)par(las = 1, mar = c(4.5, 3, 3, 2) + 0.1, cex = .8)
MASS::parcoord(iris2, col = species_col, var.label = TRUE, lwd = 2)
# Add Title
title("Parallel coordinates plot of the Iris data")
# Add a legend
par(xpd = TRUE)
legend(x = 1.75, y = -.25, cex = 1,
legend = as.character(levels(species_labels)),
fill = unique(species_col), horiz = TRUE)
par(xpd = NA)d_iris <- dist(iris2) # method="man" # is a bit better
hc_iris <- hclust(d_iris, method = "complete")
iris_species <- rev(levels(iris[,5]))
library(dendextend)
dend <- as.dendrogram(hc_iris)
# order it the closest we can to the order of the observations:
dend <- rotate(dend, 1:150)
# Color the branches based on the clusters:
dend <- color_branches(dend, k=3) #, groupLabels=iris_species)
# Manually match the labels, as much as possible, to the real classification of the flowers:
labels_colors(dend) <-
rainbow_hcl(3)[sort_levels_values(
as.numeric(iris[,5])[order.dendrogram(dend)]
)]
# We shall add the flower type to the labels:
labels(dend) <- paste(as.character(iris[,5])[order.dendrogram(dend)],
"(",labels(dend),")",
sep = "")
# We hang the dendrogram a bit:
dend <- hang.dendrogram(dend,hang_height=0.1)
# reduce the size of the labels:
# dend <- assign_values_to_leaves_nodePar(dend, 0.5, "lab.cex")
dend <- set(dend, "labels_cex", 0.5)
# And plot:
par(mar = c(3,3,3,7))
plot(dend,
main = "Clustered Iris data set
(the labels give the true flower species)",
horiz = TRUE, nodePar = list(cex = .007))
legend("topleft", legend = iris_species, fill = rainbow_hcl(3))
par(mar = rep(0,4))
circlize_dendrogram(dend)
some_col_func <- function(n) rev(colorspace::heat_hcl(n, c = c(80, 30), l = c(30, 90), power = c(1/5, 1.5)))library(gplots)
gplots::heatmap.2(as.matrix(iris2),
main = "Heatmap for the Iris data set",
srtCol = 20,
dendrogram = "row",
Rowv = dend,
Colv = "NA", # this to make sure the columns are not ordered
trace="none",
margins =c(5,0.1),
key.xlab = "Cm",
denscol = "grey",
density.info = "density",
RowSideColors = rev(labels_colors(dend)), # to add nice colored strips
col = some_col_func
)
hclust_methods <- c("ward.D", "single", "complete", "average", "mcquitty",
"median", "centroid", "ward.D2")
iris_dendlist <- dendlist()
for(i in seq_along(hclust_methods)) {
hc_iris <- hclust(d_iris, method = hclust_methods[i])
iris_dendlist <- dendlist(iris_dendlist, as.dendrogram(hc_iris))
}
names(iris_dendlist) <- hclust_methods
iris_dendlist## $ward.D
## 'dendrogram' with 2 branches and 150 members total, at height 199.6205
##
## $single
## 'dendrogram' with 2 branches and 150 members total, at height 1.640122
##
## $complete
## 'dendrogram' with 2 branches and 150 members total, at height 7.085196
##
## $average
## 'dendrogram' with 2 branches and 150 members total, at height 4.062683
##
## $mcquitty
## 'dendrogram' with 2 branches and 150 members total, at height 4.497283
##
## $median
## 'dendrogram' with 2 branches and 150 members total, at height 2.82744
##
## $centroid
## 'dendrogram' with 2 branches and 150 members total, at height 2.994307
##
## $ward.D2
## 'dendrogram' with 2 branches and 150 members total, at height 32.44761
##
## attr(,"class")
## [1] "dendlist"iris_dendlist_cor <- cor.dendlist(iris_dendlist)
iris_dendlist_cor## ward.D single complete average mcquitty median
## ward.D 1.0000000 0.9836838 0.5774013 0.9841333 0.9641103 0.9451815
## single 0.9836838 1.0000000 0.5665529 0.9681156 0.9329029 0.9444723
## complete 0.5774013 0.5665529 1.0000000 0.6195121 0.6107473 0.6889092
## average 0.9841333 0.9681156 0.6195121 1.0000000 0.9828015 0.9449422
## mcquitty 0.9641103 0.9329029 0.6107473 0.9828015 1.0000000 0.9203374
## median 0.9451815 0.9444723 0.6889092 0.9449422 0.9203374 1.0000000
## centroid 0.9809088 0.9903934 0.5870062 0.9801444 0.9499123 0.9403569
## ward.D2 0.9911648 0.9682507 0.6096286 0.9895131 0.9829977 0.9445832
## centroid ward.D2
## ward.D 0.9809088 0.9911648
## single 0.9903934 0.9682507
## complete 0.5870062 0.6096286
## average 0.9801444 0.9895131
## mcquitty 0.9499123 0.9829977
## median 0.9403569 0.9445832
## centroid 1.0000000 0.9737886
## ward.D2 0.9737886 1.0000000corrplot::corrplot(iris_dendlist_cor, "pie", "lower")
iris_dendlist_cor_spearman <- cor.dendlist(iris_dendlist, method_coef = "spearman")
corrplot::corrplot(iris_dendlist_cor_spearman, "pie", "lower")
iris_dendlist %>% dendlist(which = c(1,8)) %>% ladderize %>%
set("branches_k_color", k=3) %>%
untangle(method = "step1side", k_seq = 3:20) %>%
set("clear_branches") %>% #otherwise the single lines are not black, since they retain the previous color from the branches_k_color.
tanglegram(faster = TRUE) # (common_subtrees_color_branches = TRUE)
iris_dendlist %>% dendlist(which = c(1,4)) %>% ladderize %>%
set("branches_k_color", k=2) %>%
untangle(method = "step1side", k_seq = 3:20) %>%
tanglegram(faster = TRUE) # (common_subtrees_color_branches = TRUE)
iris_dendlist %>% dendlist(which = c(1,4)) %>% ladderize %>%
untangle(method = "step1side", k_seq = 3:20) %>%
set("rank_branches") %>%
tanglegram(common_subtrees_color_branches = TRUE)
length(unique(common_subtrees_clusters(iris_dendlist[[1]], iris_dendlist[[4]]))[-1])## [1] 39# -1 at the end is because we are ignoring the "0" subtree, which indicates leaves that are singletons.iris_dendlist %>% dendlist(which = c(3,4)) %>% ladderize %>%
untangle(method = "step1side", k_seq = 2:6) %>%
set("branches_k_color", k=2) %>%
tanglegram(faster = TRUE) # (common_subtrees_color_branches = TRUE)
par(mfrow = c(4,2))
for(i in 1:8) {
iris_dendlist[[i]] %>% set("branches_k_color", k=2) %>% plot(axes = FALSE, horiz = TRUE)
title(names(iris_dendlist)[i])
}
iris_dendlist_cor2 <- cor.dendlist(iris_dendlist, method = "common")
iris_dendlist_cor2## ward.D single complete average mcquitty median
## ward.D 1.0000000 0.7324415 0.8595318 0.8461538 0.8361204 0.7458194
## single 0.7324415 1.0000000 0.7324415 0.7491639 0.7458194 0.7591973
## complete 0.8595318 0.7324415 1.0000000 0.8060201 0.7993311 0.7491639
## average 0.8461538 0.7491639 0.8060201 1.0000000 0.8494983 0.7892977
## mcquitty 0.8361204 0.7458194 0.7993311 0.8494983 1.0000000 0.7859532
## median 0.7458194 0.7591973 0.7491639 0.7892977 0.7859532 1.0000000
## centroid 0.7324415 0.7625418 0.7290970 0.7725753 0.7759197 0.8528428
## ward.D2 0.8795987 0.7324415 0.8294314 0.8294314 0.8294314 0.7558528
## centroid ward.D2
## ward.D 0.7324415 0.8795987
## single 0.7625418 0.7324415
## complete 0.7290970 0.8294314
## average 0.7725753 0.8294314
## mcquitty 0.7759197 0.8294314
## median 0.8528428 0.7558528
## centroid 1.0000000 0.7357860
## ward.D2 0.7357860 1.0000000corrplot::corrplot(iris_dendlist_cor2, "pie", "lower")
get_ordered_3_clusters <- function(dend) {
cutree(dend, k = 3)[order.dendrogram(dend)]
}
dend_3_clusters <- lapply(iris_dendlist, get_ordered_3_clusters)
compare_clusters_to_iris <- function(clus) {FM_index(clus, rep(1:3, each = 50), assume_sorted_vectors = TRUE)}
clusters_performance <- sapply(dend_3_clusters, compare_clusters_to_iris)
dotchart(sort(clusters_performance), xlim = c(0.7,1),
xlab = "Fowlkes-Mallows Index (from 0 to 1)",
main = "Perormance of clustering algorithms \n in detecting the 3 species",
pch = 19)
- We may conclude The Iris data set is only 4-dimensional, making it possible to explore using pairs plot (SPLOM) or parallel coordinates plot. It is clear from these that two main clusters are visible, while the separation of the third cluster is difficult. In the above analysis, we learned that the complete method fails to do the proper separation of the two main clusters when cut in k=2 (but succeeds in doing it, if moving to k=3 clusters). This is different from all the other 7 methods available in hclust, which do succeed in separating the 2 main clusters from the beginning (i.e.: for k=2). We also noticed that all clustering algorithms share a relatively high proportion of common nodes (between 75% to 90%). Lastly, when it came to trying to separating the flowers into 3 species, the median clustering method did the best, while the single method did the worst in this regard.
Gene
train <- dendextend::khan$train
test <- dendextend::khan$test
d_train <- train %>% dist %>% hclust %>% as.dendrogram
d_test <- test %>% dist %>% hclust %>% as.dendrogram
d_train_test <- dendlist(train = d_train, test = d_test)
d_train_test %>% cor.dendlist## train test
## train 1.0000000 0.5708019
## test 0.5708019 1.0000000d_train_test %>% cor.dendlist(method_coef = "spearman")## train test
## train 1.0000000 0.4971936
## test 0.4971936 1.0000000Bk_plot(d_train, d_test, k = 2:30, xlim = c(2,30))
pre_tang_d_train_test <- d_train_test %>% ladderize %>% # untangle %>%
set("branches_k_color", k = 7)
train_branches_colors <- get_leaves_branches_col(pre_tang_d_train_test$train)
pre_tang_d_train_test %>% tanglegram(fast = TRUE, color_lines = train_branches_colors)
# dput(d_train_test_common)
d_train_test_common <- structure(list(train = structure(list(structure(list(structure(171L, label = "491565", members = 1L, height = 0, leaf = TRUE), structure(178L, label = "505491", members = 1L, height = 0, leaf = TRUE)), members = 2L, midpoint = 0.5, height = 7.1369942952198), structure(list(structure(list(structure(8L, label = "283315", members = 1L, height = 0, leaf = TRUE), structure(9L, label = "897177", members = 1L, height = 0, leaf = TRUE)), members = 2L, midpoint = 0.5, height = 2.55936539399907), structure(list(structure(list(structure(106L, label = "345553", members = 1L, height = 0, leaf = TRUE), structure(112L, label = "307660", members = 1L, height = 0, leaf = TRUE)), members = 2L, midpoint = 0.5, height = 5.17910461856101), structure(list(structure(list(structure(268L, label = "504791", members = 1L, height = 0, leaf = TRUE), structure(306L, label = "782503", members = 1L, height = 0, leaf = TRUE)), members = 2L, midpoint = 0.5, height = 4.27052507661529), structure(list(structure(list(structure(246L, label = "81518", members = 1L, height = 0, leaf = TRUE), structure(290L, label = "280837", members = 1L, height = 0, leaf = TRUE)), members = 2L, midpoint = 0.5, height = 1.37572388944875), structure(list(structure(list(structure(266L, label = "866694", members = 1L, height = 0, leaf = TRUE), structure(277L, label = "811956", members = 1L, height = 0, leaf = TRUE)), members = 2L, midpoint = 0.5, height = 3.31301518861595), structure(list(structure(273L, label = "842918", members = 1L, height = 0, leaf = TRUE), structure(274L, label = "626555", members = 1L, height = 0, leaf = TRUE)), members = 2L, midpoint = 0.5, height = 2.71864544948399)), members = 4, midpoint = 1.5, height = 6.35097701381449)), members = 6, midpoint = 2, height = 8.7097033164167)), members = 8, midpoint = 2.25, height = 9.23807936424017)), members = 10, midpoint = 2.375, height = 11.6573350998416)), members = 12, midpoint = 2.4375, height = 17.5620766260713)), members = 14, midpoint = 2.46875, height = 30.2363452779928, class = "dendrogram"), test = structure(list(structure(list(structure(list(structure(171L, label = "491565", members = 1L, height = 0, leaf = TRUE), structure(178L, label = "505491", members = 1L, height = 0, leaf = TRUE)), members = 2L, midpoint = 0.5, height = 3.96666017450449), structure(list(structure(list(structure(list(structure(268L, label = "504791", members = 1L, height = 0, leaf = TRUE), structure(306L, label = "782503", members = 1L, height = 0, leaf = TRUE)), members = 2L, midpoint = 0.5, height = 2.31497882927685), structure(list(structure(list(structure(266L, label = "866694", members = 1L, height = 0, leaf = TRUE), structure(277L, label = "811956", members = 1L, height = 0, leaf = TRUE)), members = 2L, midpoint = 0.5, height = 1.75475236429532), structure(list(structure(273L, label = "842918", members = 1L, height = 0, leaf = TRUE), structure(274L, label = "626555", members = 1L, height = 0, leaf = TRUE)), members = 2L, midpoint = 0.5, height = 1.34617375921535)), members = 4, midpoint = 1.5, height = 2.76465021476497)), members = 6, midpoint = 2, height = 4.52927251774499), structure(list(structure(list(structure(246L, label = "81518", members = 1L, height = 0, leaf = TRUE), structure(290L, label = "280837", members = 1L, height = 0, leaf = TRUE)), members = 2L, midpoint = 0.5, height = 0.714433271901582), structure(list(structure(8L, label = "283315", members = 1L, height = 0, leaf = TRUE), structure(9L, label = "897177", members = 1L, height = 0, leaf = TRUE)), members = 2L, midpoint = 0.5, height = 1.71895552589356)), members = 4, midpoint = 1.5, height = 6.44143803354499)), members = 10, midpoint = 4.75, height = 7.736516720075)), members = 12, midpoint = 3.625, height = 11.0066972375913), structure(list(structure(106L, label = "345553", members = 1L, height = 0, leaf = TRUE), structure(112L, label = "307660", members = 1L, height = 0, leaf = TRUE)), members = 2L, midpoint = 0.5, height = 3.6486307417989)), members = 14, midpoint = 8.0625, height = 18.2331742971431, class = "dendrogram")), class = "dendlist", .Names = c("train",
"test"))# This was calculated before
# d_train_test_common <- d_train_test %>% prune_common_subtrees.dendlist
# d_train_test_common
d_train_test_common %>% untangle %>% tanglegram(common_subtrees_color_branches = TRUE)
d_train_test %>% nleaves## train test
## 306 306d_train_test_common %>% nleaves## train test
## 14 14- We may conclude the clustering algorithm resulted in trees which are significantly similar in both the training and the test data sets beyond chance, but that this similarity is restricted to only a very small proportion of genes.
Presidential Candidate
votes.repub <- cluster::votes.repubyears <- as.numeric(gsub("X", "", colnames(votes.repub)))
par(las = 2, mar = c(4.5, 3, 3, 2) + 0.1, cex = .8)
# MASS::parcoord(votes.repub, var.label = FALSE, lwd = 1)
matplot(1L:ncol(votes.repub), t(votes.repub), type = "l", col = 1, lty = 1,
axes = F, xlab = "", ylab = "")
axis(1, at = seq_along(years), labels = years)
axis(2)
# Add Title
title("Votes for Republican Candidate\n in Presidential Elections \n (each line is a country - over the years)")
arcsin_transformation <- function(x) asin(x/100)
dend_NA <- votes.repub %>% is.na %>%
dist %>% hclust %>% as.dendrogram %>% ladderize
dend <- votes.repub %>% arcsin_transformation %>%
dist %>% hclust(method = "com") %>% as.dendrogram %>%
rotate(labels(dend_NA)) %>%
color_branches(k=3)
# some_col_func <- function(n) rev(colorspace::heat_hcl(n, c = c(80, 30), l = c(30, 90), power = c(1/5, 1.5)))
some_col_func <- colorspace::diverge_hcl
# par(mar = c(3,3,3,3))
# library(gplots)
gplots::heatmap.2(as.matrix(votes.repub),
main = "Votes for\n Republican Presidential Candidate\n (clustered using complete)",
srtCol = 60,
dendrogram = "row",
Rowv = dend,
Colv = "NA", # this to make sure the columns are not ordered
trace="none",
margins =c(3,6),
key.xlab = "% Votes for Republican\n Presidential Candidate",
labCol = years,
denscol = "grey",
density.info = "density",
col = some_col_func
)
# RowSideColors = rev(labels_colors(dend)), # to add nice colored strips hclust_methods <- c("ward.D", "single", "complete", "average", "mcquitty",
"median", "centroid", "ward.D2")
votes.repub_dendlist <- dendlist()
for(i in seq_along(hclust_methods)) {
tmp_dend <- votes.repub %>% arcsin_transformation %>% dist %>% hclust(method = hclust_methods[i]) %>% as.dendrogram
votes.repub_dendlist <- dendlist(votes.repub_dendlist, tmp_dend)
}
names(votes.repub_dendlist) <- hclust_methods
# votes.repub_dendlistcorrplot::corrplot(cor.dendlist(votes.repub_dendlist), "pie", "lower")
arcsin_transformation <- function(x) asin(x/100)
dend_NA <- votes.repub %>% is.na %>%
dist %>% hclust %>% as.dendrogram %>% ladderize
dend <- votes.repub %>% arcsin_transformation %>%
dist %>% hclust(method = "ave") %>% as.dendrogram %>%
rotate(labels(dend_NA)) %>%
color_branches(k=3)
# some_col_func <- function(n) rev(colorspace::heat_hcl(n, c = c(80, 30), l = c(30, 90), power = c(1/5, 1.5)))
some_col_func <- colorspace::diverge_hcl
# par(mar = c(3,3,3,3))
# library(gplots)
gplots::heatmap.2(as.matrix(votes.repub),
main = "Votes for\n Republican Presidential Candidate\n (clustered using average)",
srtCol = 60,
dendrogram = "row",
Rowv = dend,
Colv = "NA", # this to make sure the columns are not ordered
trace="none",
margins =c(3,6),
key.xlab = "% Votes for Republican\n Presidential Candidate",
labCol = years,
denscol = "grey",
density.info = "density",
col = some_col_func
)
# RowSideColors = rev(labels_colors(dend)), # to add nice colored strips ord1 <- c("North Carolina", "Virginia", "Tennessee", "Kentucky", "Maryland",
"Delaware", "Oklahoma", "Missouri", "New Mexico", "Oregon", "Washington",
"California", "West Virginia", "Hawaii", "Nevada", "Arizona",
"Montana", "Idaho", "Wyoming", "Utah", "Colorado", "Alaska",
"Illinois", "New York", "Indiana", "Ohio", "Connecticut", "New Hampshire",
"New Jersey", "Pennsylvania", "Iowa", "South Dakota", "North Dakota",
"Wisconsin", "Minnesota", "Nebraska", "Kansas", "Maine", "Michigan",
"Massachusetts", "Rhode Island", "Vermont", "Alabama", "Georgia",
"Louisiana", "Arkansas", "Florida", "Texas", "South Carolina",
"Mississippi")
ord2 <- c("North Carolina", "Virginia", "Tennessee", "Oklahoma", "Kentucky",
"Maryland", "Delaware", "Missouri", "New Mexico", "West Virginia",
"Oregon", "Washington", "California", "Nevada", "Arizona", "Montana",
"Colorado", "Alaska", "Idaho", "Wyoming", "Utah", "Hawaii", "Maine",
"Illinois", "New York", "New Jersey", "Indiana", "Ohio", "Connecticut",
"New Hampshire", "Pennsylvania", "Michigan", "Iowa", "South Dakota",
"North Dakota", "Wisconsin", "Minnesota", "Massachusetts", "Rhode Island",
"Nebraska", "Kansas", "Vermont", "Alabama", "Georgia", "Louisiana",
"Arkansas", "Florida", "Texas", "South Carolina", "Mississippi"
)
# dput(lapply(dends, labels)[[2]])dend_com <- votes.repub %>% arcsin_transformation %>%
dist %>% hclust(method = "com") %>% as.dendrogram %>%
rotate(labels(dend_NA)) %>%
color_branches(k=3) # %>% ladderize
dend_ave <- votes.repub %>% arcsin_transformation %>%
dist %>% hclust(method = "ave") %>% as.dendrogram %>%
rotate(labels(dend_NA)) %>%
color_branches(k=3) # %>% ladderize
# The orders were predefined after using untangle("step2side")
# They are omitted here to save running time.
dend_com <- rotate(dend_com, ord1)
dend_ave <- rotate(dend_ave, ord2)
dends <- dendlist(complete = dend_com, average = dend_ave) # %>% untangle("step2side")
dends %>% tanglegram(margin_inner = 7)
- We may conclude two clusterings give similar results for: “Alabama”, “Georgia”, “Louisiana”, “Arkansas”, “Florida”, “Texas”, “South Carolina”, “Mississippi”. There are also several other sub-trees which are identical between the two methods. The biggest difference lies in several “rouge” states that are placed differently in the two clustering algorithms. They are: Vermont, Michigan, Maine, Hawaii, New Jersey, West Virginia, and Oklahoma.
Animal Clustering
animals <- cluster::animals
colnames(animals) <- c("warm-blooded",
"can fly",
"vertebrate",
"endangered",
"live in groups",
"have hair")dend_r <- animals %>% dist(method = "man") %>% hclust(method = "ward.D") %>% as.dendrogram %>% ladderize %>%
color_branches(k=4)
dend_c <- t(animals) %>% dist(method = "man") %>% hclust(method = "com") %>% as.dendrogram %>% ladderize%>%
color_branches(k=3)
# some_col_func <- function(n) rev(colorspace::heat_hcl(n, c = c(80, 30), l = c(30, 90), power = c(1/5, 1.5)))
# some_col_func <- colorspace::diverge_hcl
# some_col_func <- colorspace::sequential_hcl
some_col_func <- function(n) (colorspace::diverge_hcl(n, h = c(246, 40), c = 96, l = c(65, 90)))
# par(mar = c(3,3,3,3))
# library(gplots)
gplots::heatmap.2(as.matrix(animals-1),
main = "Attributes of Animals",
srtCol = 35,
Rowv = dend_r,
Colv = dend_c,
trace="row", hline = NA, tracecol = "darkgrey",
margins =c(6,3),
key.xlab = "no / yes",
denscol = "grey",
density.info = "density",
col = some_col_func
)
hclust_methods <- c("ward.D", "single", "complete", "average", "mcquitty",
"median", "centroid", "ward.D2")
animals_dendlist <- dendlist()
for(i in seq_along(hclust_methods)) {
tmp_dend <- animals %>% dist(method = "man") %>%
hclust(method = hclust_methods[i]) %>% as.dendrogram
animals_dendlist <- dendlist(animals_dendlist, tmp_dend)
}
names(animals_dendlist) <- hclust_methods
# votes.repub_dendlistcophenetic_cors <- cor.dendlist(animals_dendlist)
corrplot::corrplot(cophenetic_cors, "pie", "lower")
remove_median <- dendlist(animals_dendlist, which = c(1:8)[-6] )
FM_cors <- cor.dendlist(remove_median, method = "FM_index", k = 4)
corrplot::corrplot(FM_cors, "pie", "lower")
- We may conclude We removed the “median” method since it did not have k=4 possible. In general, the results seems sensitive to the algorithm used, and the different algorithm methods do not seem to agree with one another (with regards to k=4), so further analyses may be in place in order to decide on which algorithm and interpretation are most appropriate for these data.