Step.pred: A function to generate RS cutoff point based the given...

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

Description

Based on the specified percentage, this function finds the RS threshold and recommend which test samples may benefit by classifying with the data set at the second stage.

Usage

Step.pred(RS, percent)

Arguments

RS	A vector of RS.
percent	Percentage of samples allow to pass to the second stage data set.

Value

A list object with following components:

RS.cut	RS threshold corresponding to the specified re-classification percentage.
ind	a vector of binary values. 1 denotes sample is recommend to classify with the data set at the second stage and vice versa.

Author(s)

Askar Obulkasim

Maintainer: Askar Obulkasim <[e-mail]>

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 <- Step.pred(RS, 30)

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