This article describes how to compare cluster dendrograms in R using the dendextend R package.
The dendextend package provides several functions for comparing dendrograms. Here, we’ll focus on two functions:
- tanglegram() for visual comparison of two dendrograms
- and cor.dendlist() for computing a correlation matrix between dendrograms.
We’ll use the R base USArrests data sets and we start by standardizing the variables using the function scale() as follow:
df <- scale(USArrests)
To make readable the plots, generated in the next sections, we’ll work with a small random subset of the data set. Therefore, we’ll use the function sample() to randomly select 10 observations among the 50 observations contained in the data set:
# Subset containing 10 rows set.seed(123) ss <- sample(1:50, 10) df <- df[ss,]
We start by creating a list of two dendrograms by computing hierarchical clustering (HC) using two different linkage methods (“average” and “ward.D2”). Next, we transform the results as dendrograms and create a list to hold the two dendrograms.
library(dendextend) # Compute distance matrix res.dist <- dist(df, method = "euclidean") # Compute 2 hierarchical clusterings hc1 <- hclust(res.dist, method = "average") hc2 <- hclust(res.dist, method = "ward.D2") # Create two dendrograms dend1 <- as.dendrogram (hc1) dend2 <- as.dendrogram (hc2) # Create a list to hold dendrograms dend_list <- dendlist(dend1, dend2)
- Visual comparison of two dendrograms
To visually compare two dendrograms, we’ll use the following R functions [dendextend package]:
- untangle(): finds the best layout to align dendrogram lists, using heuristic methods
- tanglegram(): plots the two dendrograms, side by side, with their labels connected by lines.
- entanglement(): computes the quality of the alignment of the two trees. Entanglement is a measure between 1 (full entanglement) and 0 (no entanglement). A lower entanglement coefficient corresponds to a good alignment.
- Draw a tanglegram:
# Align and plot two dendrograms side by side dendlist(dend1, dend2) %>% untangle(method = "step1side") %>% # Find the best alignment layout tanglegram() # Draw the two dendrograms
# Compute alignment quality. Lower value = good alignment quality dendlist(dend1, dend2) %>% untangle(method = "step1side") %>% # Find the best alignment layout entanglement() # Alignment quality
##  0.0384
- Customized the tanglegram using many other options as follow:
dendlist(dend1, dend2) %>% untangle(method = "step1side") %>% tanglegram( highlight_distinct_edges = FALSE, # Turn-off dashed lines common_subtrees_color_lines = FALSE, # Turn-off line colors common_subtrees_color_branches = TRUE # Color common branches )
Note that, “unique” nodes, with a combination of labels/items not present in the other tree, are highlighted with dashed lines.
Note that, just because we can get two trees to have horizontal connecting lines, it doesn’t mean these trees are identical (or even very similar topologically).
In the following section, we’ll perform correlation analysis to measure the similarity between dendrograms.
- Correlation matrix between a list of dendrogams
The function cor.dendlist() is used to compute “Baker” or “Cophenetic” correlation matrix between a list of trees. The value can range between -1 to 1. With near 0 values meaning that the two trees are not statistically similar.
# Cophenetic correlation matrix cor.dendlist(dend_list, method = "cophenetic")
## [,1] [,2] ## [1,] 1.000 0.965 ## [2,] 0.965 1.000
# Baker correlation matrix cor.dendlist(dend_list, method = "baker")
## [,1] [,2] ## [1,] 1.000 0.962 ## [2,] 0.962 1.000
The correlation between two trees can be also computed as follow:
# Cophenetic correlation coefficient cor_cophenetic(dend1, dend2)
##  0.965
# Baker correlation coefficient cor_bakers_gamma(dend1, dend2)
##  0.962
It’s also possible to compare simultaneously multiple dendrograms. A chaining operator %>% is used to run multiple function at the same time. It’s useful for simplifying the code:
# Create multiple dendrograms by chaining dend1 <- df %>% dist %>% hclust("complete") %>% as.dendrogram dend2 <- df %>% dist %>% hclust("single") %>% as.dendrogram dend3 <- df %>% dist %>% hclust("average") %>% as.dendrogram dend4 <- df %>% dist %>% hclust("centroid") %>% as.dendrogram # Compute correlation matrix dend_list <- dendlist("Complete" = dend1, "Single" = dend2, "Average" = dend3, "Centroid" = dend4) cors <- cor.dendlist(dend_list) # Print correlation matrix round(cors, 2)
## Complete Single Average Centroid ## Complete 1.00 0.76 0.99 0.75 ## Single 0.76 1.00 0.80 0.84 ## Average 0.99 0.80 1.00 0.74 ## Centroid 0.75 0.84 0.74 1.00
# Visualize the correlation matrix using corrplot package library(corrplot) corrplot(cors, "pie", "lower")
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