discreteControl {MLSeq} | R Documentation

## Define controlling parameters for discrete classifiers (NBLDA and PLDA)

### Description

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

### Usage

    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,
      ...)

### Arguments

`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.

### Author(s)

Gokmen Zararsiz, Dincer Goksuluk, Selcuk Korkmaz, Vahap Eldem, Bernd Klaus, Ahmet Ozturk and Ahmet Ergun Karaagaoglu

### See Also

`classify`, `trainControl`, `discreteControl`

### Examples

    1L

[Package MLSeq version 1.20.3 Index]