heatmapVar {RMixtCompUtilities}

Heatmap of the similarities between variables about clustering

Description

Heatmap of the similarities between variables about clustering

Usage

heatmapVar(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 variables j and h is defined by Delta(j,h)

Delta(j,h) = 1 - \sqrt{(1/n) * \sum_{i=1}^n \sum_{k=1}^K (P(Z_i=k|x_{ij}) - P(Z_i=k|x_{ih}))^2}

Author(s)

Matthieu MARBAC

See Also

computeSimilarityVar

Other plot: heatmapClass(), heatmapTikSorted(), 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
  heatmapVar(resLearn)
}