psm | R Documentation |
Compute the Posterior Pairwise Similarity for All Pairs of Items
psm(partitions)
partitions |
A matrix, with each row a numeric vector cluster labels |
A symmetric matrix of pairwise similarities based on the partitions given.
# Neal (2000) model and data nealData <- c(-1.48, -1.40, -1.16, -1.08, -1.02, 0.14, 0.51, 0.53, 0.78) mkLogPosteriorPredictiveDensity <- function(data = nealData, sigma2 = 0.1^2, mu0 = 0, sigma02 = 1) { function(i, subset) { posteriorVariance <- 1 / ( 1/sigma02 + length(subset)/sigma2 ) posteriorMean <- posteriorVariance * ( mu0/sigma02 + sum(data[subset])/sigma2 ) posteriorPredictiveSD <- sqrt(posteriorVariance + sigma2) dnorm(data[i], posteriorMean, posteriorPredictiveSD, log=TRUE) } } logPostPredict <- mkLogPosteriorPredictiveDensity() nSamples <- 500L partitions <- matrix(0, nrow=nSamples, ncol=length(nealData)) for ( i in 2:nSamples ) { partitions[i,] <- nealAlgorithm3(partitions[i-1,], logPostPredict, mass = 1.0, nUpdates = 2) } psm(partitions)
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