applying the pvclust R function to a precomputed dist object

Question

I'm using R to perform an hierarchical clustering. As a first approach I used hclust and performed the following steps:

1. I imported the distance matrix
2. I used the as.dist function to transform it in a dist object
3. I run hclust on the dist object

Here's the R code:

distm <- read.csv("distMatrix.csv")
d <- as.dist(distm)
hclust(d, "ward")

At this point I would like to do something similar with the function pvclust; however, I cannot because it's not possible to pass a precomputed dist object. How can I proceed considering that I'm using a distance not available among those provided by the dist function of R?

Answer 1

It's not clear to me whether you only have a distance matrix, or you computed it beforehand. In the former case, as already suggested by @Vincent, it would not be too difficult to tweak the R code of pvclust itself (using fix() or whatever; I provided some hints on another question on CrossValidated). In the latter case, the authors of pvclust provide an example on how to use a custom distance function, although that means you will have to install their "unofficial version".

Answer 2

I've tested the suggestion of Vincent, you can do the following (my data set is a dissimilarity matrix):

# Import you data
distm <- read.csv("distMatrix.csv")
d <- as.dist(distm)

# Compute the eigenvalues
x <- cmdscale(d,1,eig=T)

# Plot the eigenvalues and choose the correct number of dimensions (eigenvalues close to 0)
plot(x$eig, 
   type="h", lwd=5, las=1, 
   xlab="Number of dimensions", 
   ylab="Eigenvalues")

# Recover the coordinates that give the same distance matrix with the correct number of dimensions    
x <- cmdscale(d,nb_dimensions)

# As mentioned by Stéphane, pvclust() clusters columns
pvclust(t(x))

Answer 3

If the dataset is not too large, you can embed your n points in a space of dimension n-1, with the same distance matrix.

# Sample distance matrix
n <- 100
k <- 1000
d <- dist( matrix( rnorm(k*n), nc=k ), method="manhattan" )

# Recover some coordinates that give the same distance matrix
x <- cmdscale(d, n-1)
stopifnot( sum(abs(dist(x) - d)) < 1e-6 )

# You can then indifferently use x or d
r1 <- hclust(d)
r2 <- hclust(dist(x)) # identical to r1
library(pvclust)
r3 <- pvclust(x)

If the dataset is large, you may have to check how pvclust is implemented.