computeDiscrimPowerVar: Discriminative power

Description

Compute the discriminative power of each variable or classe

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 discrimative power of variables globally otherwise return the discrimative power of variables in the given class

Value

the discriminative power

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

The discriminative power of class k is defined by 1 - D(k) $$D(k) = -\sum_{i=1}^n P(Z_i=k|x_i) \log(P(Z_i=k|x_i)) / (n*\exp(-1))$$

Examples

Run this code

# 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)


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)

# }