plotDiscrimVar {RMixtCompUtilities}

Barplot of the discriminative power of the variables

Description

Barplot of the discriminative power of the variables

Usage

plotDiscrimVar(
  output,
  class = NULL,
  ylim = c(0, 1),
  pkg = c("ggplot2", "plotly"),
  ...
)

Arguments

output

object returned by mixtCompLearn function from RMixtComp or rmcMultiRun function from RMixtCompIO

class

NULL or a number of classes. If NULL, return the discrimative power of variables globally otherwise return the discrimative power of variables in the given class

ylim

vector of length 2 defining the range of y-axis

pkg

"ggplot2" or "plotly". Package used to plot

...

arguments to be passed to plot_ly

Details

The discriminative power of variable j is defined by 1 - C(j)

C(j)= -\sum_{k=1}^K \sum_{i=1}^n P(Z_i=k|x_{ij}) ln(P(Z_i=k|x_{ij})) / (n*\log(K))

Author(s)

Matthieu MARBAC

See Also

computeDiscrimPowerVar

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

  plotDiscrimVar(resLearn, class = 1)
}