stepwiseCM (version 1.18.0)

Curve.generator: A function to generate accuracy curve by passing different portion of samples to the data set used at the second stage.

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

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)