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

Description Usage Arguments Details Value Note Author(s) References See Also Examples

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

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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 <[email protected]>, 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

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#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 May 6, 2019, 3:54 a.m.