heatmapClass: Heatmap of the similarities between classes about clustering

In RMixtCompUtilities: Utility Functions for 'MixtComp' Outputs

Heatmap of the similarities between classes about clustering

Description

Heatmap of the similarities between classes about clustering

Usage

heatmapClass(output, pkg = c("ggplot2", "plotly"), ...)

Arguments

output	object returned by mixtCompLearn function from RMixtComp or rmcMultiRun function from RMixtCompIO
pkg	"ggplot2" or "plotly". Package used to plot
...	arguments to be passed to plot_ly. For pkg = "ggplot2", addValues = TRUE prints similarity values on the heatmap

Details

The similarities between classes k and g is defined by 1 - Sigma(k,g)

Sigma(k,g)^2 = (1/n) * \sum_{i=1}^n (P(Z_i=k|x_i) - P(Z_i=g|x_i))^2

Author(s)

Matthieu MARBAC

See Also

computeSimilarityClass

Other plot: heatmapTikSorted(), heatmapVar(), histMisclassif(), plot.MixtComp(), plotConvergence(), plotDataBoxplot(), plotDataCI(), plotDiscrimClass(), plotDiscrimVar(), plotParamConvergence(), plotProportion()

Examples

if (requireNamespace("RMixtCompIO", quietly = TRUE)) {
  dataLearn <- list(
    var1 = as.character(c(rnorm(50, -2, 0.8), rnorm(50, 2, 0.8))),
    var2 = as.character(c(rnorm(50, 2), rpois(50, 8)))
  )

  model <- list(
    var1 = list(type = "Gaussian", paramStr = ""),
    var2 = list(type = "Poisson", paramStr = "")
  )

  algo <- list(
    nClass = 2,
    nInd = 100,
    nbBurnInIter = 100,
    nbIter = 100,
    nbGibbsBurnInIter = 100,
    nbGibbsIter = 100,
    nInitPerClass = 3,
    nSemTry = 20,
    confidenceLevel = 0.95,
    ratioStableCriterion = 0.95,
    nStableCriterion = 10,
    mode = "learn"
  )

  resLearn <-RMixtCompIO::rmcMultiRun(algo, dataLearn, model, nRun = 3)

  # plot
  heatmapClass(resLearn)
}

RMixtCompUtilities documentation built on Sept. 22, 2023, 5:10 p.m.