computeDiscrimPowerVar {RMixtCompUtilities}

Discriminative power

Description

Compute the discriminative power of each variable or class

Usage

computeDiscrimPowerVar(outMixtComp, class = NULL)

computeDiscrimPowerClass(outMixtComp)

Arguments

outMixtComp

object of class MixtCompLearn or MixtComp obtained using mixtCompLearn or mixtCompPredict functions from RMixtComp package or rmcMultiRun from RMixtCompIO package.

class

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

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}) \log(P(Z_i=k|x_{ij})) / (n*\log(K))

A high value (close to one) means that the variable is highly discriminating. A low value (close to zero) means that the variable is poorly discriminating.

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

C(j)= - sum_{i=1}^n (P(Z_i!=k|x_{ij}) \log(P(Z_i!=k|x_{ij})) + P(Z_i=k|x_{ij}) \log(P(Z_i=k|x_{ij}))) / (n*\log(2))

The discriminative power of class k is defined by 1 - D(k)

D(k) = -\sum_{i=1}^n P(Z_i=k|x_i) \log(P(Z_i=k|x_i)) / (n*\exp(-1))

Value

the discriminative power

Author(s)

Matthieu Marbac

See Also

plotDiscrimClass plotDiscrimVar

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)


  discVar <- computeDiscrimPowerVar(resLearn)
  discVarInClass1 <- computeDiscrimPowerVar(resLearn, class = 1)
  discClass <- computeDiscrimPowerClass(resLearn)

  # graphic representation of discriminant variables
  plotDiscrimVar(resLearn)
  # graphic representation of discriminant classes
  plotDiscrimClass(resLearn)
}