RMixtCompUtilities (version 4.1.3)

plotDataBoxplot: Boxplot per class

Description

Display a boxplot (5

Usage

plotDataBoxplot(
  output,
  var,
  class = 1:output$algo$nClass,
  grl = TRUE,
  pkg = c("ggplot2", "plotly"),
  ...
)

Arguments

output

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

var

name of the variable

class

classes to plot

grl

if TRUE plot the general distribution of the data

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.

ylim

ylim of the plot.

xlim

xlim of the plot.

See Also

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

Examples

# NOT RUN {
require(RMixtCompIO) # for learning a mixture model
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 <- rmcMultiRun(algo, dataLearn, model, nRun = 3)

# plot
plotDataBoxplot(resLearn, "var1")


# }