plotDataCI: Mean and 95%-level confidence intervals per class

In RMixtCompUtilities: Utility Functions for 'MixtComp' Outputs

Mean and 95%-level confidence intervals per class

Description

Mean and 95%-level confidence intervals per class

Usage

plotDataCI(
  output,
  var,
  class = seq_len(output$algo$nClass),
  grl = FALSE,
  pkg = c("ggplot2", "plotly"),
  ...
)

Arguments

output	object returned by mixtCompLearn function from RMixtComp or rmcMultiRun function from RMixtCompIO
var	name of the variable
class	class to plot
grl	if TRUE plot the CI for the dataset and not only classes
pkg	"ggplot2" or "plotly". Package used to plot
...	other parameters (see Details)

Details

For functional data, three other parameters are available:

add.obs

if TRUE, observations are added to the plot. Default = FALSE.

add.CI

if FALSE, confidence intervals are removed from the plot. Default = TRUE.

xlim

xlim of the plot.

ylim

ylim of the plot.

Author(s)

Matthieu MARBAC

See Also

Other plot: heatmapClass(), heatmapTikSorted(), heatmapVar(), histMisclassif(), plot.MixtComp(), plotConvergence(), plotDataBoxplot(), 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
  plotDataCI(resLearn, "var1")
}

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