clusterTrace | R Documentation |
Plot Traces of Cluster Sizes
clusterTrace( partitions, plot.cols = rep("black", ncol(partitions)), plot.title = "" )
partitions |
A matrix, with each row a numeric vector cluster labels |
plot.cols |
A character vector of valid color names, whose length represents the maximum number of stacked traces to be plotted |
plot.title |
A character string to be used as the main title on the trace plot |
# 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) } clusterTrace(partitions, plot.title = "Neal (2000) Data")
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