LedPred (version 1.6.0)

mcTune: 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