discreteControl: Define controlling parameters for discrete classifiers (NBLDA...
In dncR/MLSeq: Machine Learning Interface for RNA-Seq Data

Description Usage Arguments Author(s) See Also Examples

This function sets the control parameters for discrete classifiers (PLDA and NBLDA) while training the model.

discreteControl(method = "repeatedcv", number = 5, repeats = 10,
  rho = NULL, rhos = NULL, beta = 1, prior = NULL, alpha = NULL,
  truephi = NULL, foldIdx = NULL, tuneLength = 30,
  parallel = FALSE, ...)

`method`	validation method. Support repeated cross validation only ("repeatedcv").
`number`	a positive integer. Number of folds.
`repeats`	a positive integer. Number of repeats.
`rho`	a single numeric value. This parameter is used as tuning parameter in PLDA classifier. It does not effect NBLDA classifier.
`rhos`	a numeric vector. If optimum parameter is searched among given values, this option shpould be used.
`beta`	parameter of Gamma distribution. See PLDA for details.
`prior`	prior probabilities of each class
`alpha`	a numeric value in the interval 0 and 1. It is used to apply power transformation through PLDA method.
`truephi`	a numeric value. If true value of genewise dispersion is known and constant for all genes, this parameter should be used.
`foldIdx`	a list including the fold indexes. Each element of this list is the vector indices of samples which are used as test set in this fold.
`tuneLength`	a positive integer. If there is a tuning parameter in the classifier, this value is used to define total number of tuning parameter to be searched.
`parallel`	if TRUE, parallel computing is performed.
`...`	further arguments. Deprecated.