psm {salso}

Compute an Adjacency or Pairwise Similarity Matrix

Description

If only one sample is provided, this function computes an adjacency matrix, i.e., a binary matrix whose (i,j) element is one if and only if elements i and j in the partition have the same cluster label. If multiple samples are provided (as rows of the x matrix), this function computes the n-by-n matrix whose (i,j) element gives the relative frequency (i.e., estimated probability) that items i and j are in the same subset (i.e., cluster). This is the mean of the adjacency matrices of the provided samples.

Usage

psm(x, nCores = 0)

Arguments

x

A B-by-n matrix, where each of the B rows represents a clustering of n items using cluster labels. For the bth clustering, items i and j are in the same cluster if x[b,i] == x[b,j].

nCores

The number of CPU cores to use, i.e., the number of simultaneous runs at any given time. A value of zero indicates to use all cores on the system.

Value

A n-by-n symmetric matrix whose (i,j) element gives the relative frequency that that items i and j are in the same subset (i.e., cluster).

Examples

# For examples, use 'nCores=1' per CRAN rules, but in practice omit this.
data(iris.clusterings)
partition <- iris.clusterings[1,]
# R_CARGO \dontrun{
# R_CARGO # Example disabled since Cargo was not found when installing from source package.
# R_CARGO # You can still run the example if you install Cargo. Hint: cargo::install().
psm(partition, nCores=1)
# R_CARGO }

dim(iris.clusterings)
# R_CARGO \dontrun{
# R_CARGO # Example disabled since Cargo was not found when installing from source package.
# R_CARGO # You can still run the example if you install Cargo. Hint: cargo::install().
probs <- psm(iris.clusterings, nCores=1)
dim(probs)
probs[1:6, 1:6]
# R_CARGO }