Curve.generator: A function to generate accuracy curve by passing different...

In stepwiseCM: Stepwise Classification of Cancer Samples using High-dimensional Data Sets

Description

Accuracy curve may be used as a reference for choosing the re-classification score (RS) threshold threshold for incoming samples.

Usage

Curve.generator(RS, pred1.test, pred2.test, 
    test.label, plot.it = TRUE)

Arguments

RS	A numeric vector contains the re-classification score of the test set.
pred1.test	A numeric vector of contains the predicted class labels of the test set from the data set used at the first stage. Should be numeric not factor.
pred2.test	A numeric vector of contains the predicted class labels of the test set from the data set used at the second stage. Should be numeric not factor.
test.label	A vector of actual class labels (0 or 1) of the test set. Should be numeric not factor.
plot.it	If set to “TRUE”, this function produces a plot in which Y axis denotes the accuracy and X denotes the percentage of samples passed to the second stage. In order to make this plot, class labels and molecular data for the test set must be given. Default is “TRUE”.

Value

A data frame of two columns. The first column denotes the percentage of samples passed to the data used at the second stage, and the second denotes the corresponding accuracy.

Author(s)

Askar Obulkasim

Maintainer: Askar Obulkasim <[e-mail]>

See Also

Classifier, Classifier.par, Proximity, RS.generator

Examples

data(CNS)
train.cli <- t(CNS$cli[1:40,])
test.cli <- t(CNS$cli[41:60,])
train.gen <- CNS$mrna[,1:40]
test.gen <- CNS$mrna[,41:60]
train.label <- CNS$class[1:40]
test.label <- CNS$class[41:60]
pred.cli <- Classifier(train = train.cli, train.label = train.label, test = test.cli,
            type = "GLM_L1", CVtype = "k-fold", outerkfold = 2, innerkfold = 2)
pred.gen <- Classifier(train = train.gen, train.label = train.label, test = test.gen,
            type = "GLM_L1", CVtype = "k-fold", outerkfold = 2, innerkfold = 2)
prox1 <- Proximity(train.cli, train.label, test.cli, N = 2)$prox.test
prox2 <- Proximity(train.gen, train.label, NULL, N = 2)$prox.train
RS <- RS.generator(pred.cli$P.train, pred.gen$P.train, train.label, prox1, 
             prox2, type = "rank")
res <- Curve.generator(RS, pred.cli$P.test, pred.gen$P.test, test.label)
             

stepwiseCM documentation built on May 31, 2017, 11:47 a.m.