RMixtCompUtilities (version 4.1.6)

computeSimilarityVar: Similarity

Description

Compute the similarity between variables (or classes)

Usage

computeSimilarityVar(outMixtComp)
computeSimilarityClass(outMixtComp)

Value

a similarity matrix

Arguments

outMixtComp

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

Author

Quentin Grimonprez

Details

The similarities between variables j and h is defined by Delta(j,h) $$Delta(j,h)^2 = 1 - \sqrt{(1/n) * \sum_{i=1}^n \sum_{k=1}^K (P(Z_i=k|x_{ij}) - P(Z_i=k|x_{ih}))^2}$$

The similarities between classes k and g is defined by 1 - Sigma(k,g) $$Sigma(k,g)^2 = (1/n) * \sum_{i=1}^n (P(Z_i=k|x_i) - P(Z_i=g|x_i))^2$$

See Also

heatmapVar heatmapClass

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)

  simVar <- computeSimilarityVar(resLearn)
  simClass <- computeSimilarityClass(resLearn)
}