mcTune {LedPred} | R Documentation

## Tuning the SVM parameters

### Description

The `mcTune` function is a modified version of the function `tune` from package e1071 [6]. It tests the different combinations of C and gamma parameters given as vectors in a list and will return the prediction error computed during the cross-validation step.

### Usage

    mcTune(data, cl = 1, ranges = list(gamma = c(1, 10), cost = c(1, 10)),
      kernel = "linear", valid.times = 10, file.prefix = NULL,
      numcores = ifelse(.Platform$OS.type == "windows", 1, parallel::detectCores()
      - 1))

### Arguments

`data` | data.frame containing the training set
`cl` | integer indicating the column number corresponding to the response vector that classify positive and negative regions (default = 1)
`ranges` | list object containing one (linear kernel) or two (radial kernel) vectors of integers corresponding to SVM cost and SVM gamma parameters to test.
`kernel` | SVM kernel, a character string: "linear" or "radial". (default = "radial")
`valid.times` | Integer indicating how many times the training set will be split for the cross validation step (default = 10). This number must be smaller than positive and negative sets sizes.
`file.prefix` | A character string that will be used as a prefix followed by "_c_g_eval.png" for result plot files, if it is NULL (default), no plot is returned
`numcores` | Number of cores to use for parallel computing (default: the number of available cores in the machine - 1)

### Value

A list of class `tune`

`best.parameters` | A list of the parameters giving the lowest misclassification error
`best.performance` | The lowest misclassification error
`method` | The method used
`nparcomb` | the number of tested parameter combinations
`train.ind` | The indexes used to produce subsets during the cross validation step
`sampling` | The cross-validation fold number
`performances` | A matrix summarizing the cross-validation step with the error for each tested parameter at each round and the dispersion of these errors (regarding to the average error)
`best.model` | The model produced by the best parameters

### Examples

    data(crm.features)
    cost.vector <- c(1,3,10,30)
    gamma.vector <- c(1,3,10,30)
    #c.g.obj <- mcTune(data.granges= crm.features, ranges = list(cost=cost.vector,
    #    gamma=gamma.vector), kernel='linear', file.prefix = "test")
    #names(c.g.obj)
    # cost <- c.g.obj$best.parameters$cost
    # gamma <- c.g.obj$best.parameters$gamma

[Package LedPred version 1.21.0 Index]