sigFeature.enfold: Significant feature selection with k-fold data.

In sigFeature: sigFeature: Significant feature selection using SVM-RFE & t-statistic

Description

After converting the dataset into k-folds the function named "sigFeature.enfold()" is used to select significant features from the classes. The randomization process is used to sub-sample the dataset.

Usage

sigFeature.enfold(x, y, CV, CVnumber=0)

Arguments

x	n-by-d data matrix to train (n chips/patients, d clones/genes)
y	vector of class labels -1 or 1 s (for n chips/patiens )
CV	the number of folds in case of k-fold cross validation.
CVnumber	the number of folds in case of n fold cross validation.

Details

The "sigFeature()" function is further enhanced by incorporating one cross validation methods such as k-fold external cross validation. In this k-fold cross validation procedure k-1 fold are used for selecting the feature and one fold remain untouched which will latter used as test sample set.

Value

feature.ids	selected significant features.
train.data.ids	training chips/patients ids.
test.data.ids	testng chips/patients ids.
train.data.level	vector of class labels -1 or 1s (for n chips/patiens ) for train da.
test.data.level	vector of class labels -1 or 1s (for n chips/patiens ) for test da.

Note

This function will compute the feature with cross checking.

Author(s)

Pijush Das <[e-mail]>, et al.

References

Zhang, H. H., Ahn, J., Lin, X. and Park, C. (2006). Gene selection using support vector machines with nonconvex penalty. Bioinformatics, 22, pp. 88-95.

See Also

findgacv.scad, predict.penSVM, sim.data

Examples

#Example for sigFeature.enfold()
#Data set taken from GSE2280
#library(SummarizedExperiment)
#data(ExampleRawData, package="sigFeature")

#x  <- t(assays(ExampleRawData)$counts)
#y  <- colData(ExampleRawData)$sampleLabels

#For ten fold external cross validation.
#results = sigFeature.enfold(x,y,"kfold",10)


#Compactly display the internal structure of an R object named "results"
data(results)
str(results)

sigFeature documentation built on Nov. 8, 2020, 8:28 p.m.