LedPred (version 1.6.0)

evaluateModelPerformance: Evaluate model performances

Description

evaluateModelPerformance function computes the precision and recall measures to evaluate the model through cross validation steps using ROCR package.

Usage

evaluateModelPerformance(data, cl = 1, valid.times = 10, feature.ranking = NULL, feature.nb = NULL, numcores = ifelse(.Platform$OS.type == "windows", 1, parallel::detectCores() - 1), file.prefix = NULL, kernel = "linear", cost = NULL, gamma = NULL)

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)

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.

feature.ranking

List of ordered features.

feature.nb

the optimal number of feature to use from the list of ordered features.

numcores

Number of cores to use for parallel computing (default: the number of available cores in the machine - 1)

file.prefix

A character string that will be used as a prefix followed by "_ROCR_perf.png" for the result plot file, if it is NULL (default), no plot is returned

kernel

SVM kernel, a character string: "linear" or "radial". (default = "radial")

cost

The SVM cost parameter for both linear and radial kernels. If NULL (default), the function mcTune is run.

gamma

The SVM gamma parameter for radial kernel. If radial kernel and NULL (default), the function mcTune is run.

Value

A list with two objects.

probs

The predictions computed by the model for each subset during the cross-validation

labels

The actual class for each subset

Examples

data(crm.features)
data(feature.ranking)
#probs.labels.list <- evaluateModelPerformance(data.granges=crm.features,
#    feature.ranking=feature.ranking, feature.nb=50,
#    file.prefix = "test")
#names(probs.labels.list[[1]])