TuneClassifiers: Tune the parameters of the different classifiers for a given...
In elqumsan/RNAseqMVA: Application of Multivariate Analysis methods to RNA-seq Data

Description Usage Arguments Value Author(s)

Tune the parameters of the different classifiers for a given study case

TuneClassifiers(
  studyCase,
  dataType = "TMM_log2_PC",
  tuneSVM = TRUE,
  svmCost = 1,
  svmDegree = 1:4,
  svmGamma = 4^(-2:4),
  svmCoef0 = 4^(-2:4),
  tuneRandomForest = TRUE,
  rfNtree = c(100, 200, 300, 500),
  rfMtry = c(50, 100, 200, 300, 500),
  tuneKNN = TRUE,
  knnK = c(1, 2, 3, 4, 5, 7, 10, 15),
  knnL = 0,
  plotResults = FALSE
)

`studyCase`	a studyCase object
`dataType="TMM_log2_PC"`	data type to use for the tuning. Must be one of the data types included in the 'datasetsForTest' attribute of the studyCase object
`tuneSVM=TRUE`	if TRUE, tune parameters for SVM
`svmCost=1`	cost parameter values for SVM
`svmDegree=1:4`	degree values for SVM polynomial kernel
`svmGamma=4^(-2:4)`	gamma values for SVM kernels (ignored for linear kernel)
`tuneRandomForest=TRUE`	if TRUE, tune parameters for Random Forest
`rfNtree=c(100, 200, 300, 500)`	values to test for RF numbers of trees
`rfMtry=c(50, 100, 200, 300, 500)`	values to test for RF mtry parameter
`tuneKNN=TRUE`	if TRUE, tune parameters for KNN
`knnK=c(1, 2, 3, 4, 5, 7, 10, 15)`	number of neighbours for KNN
`knnL=0`	minimum vote for definite decision for KNN
`plotResults=FALSE`	if TRUE, run plot() on the tuned objects