classify.aep: Training and predicting using aepSVM (aepSVM) classification...

In netClass: netClass: An R Package for Network-Based Biomarker Discovery

Description

Training and predicting using aepSVM (aepSVM) classification methods

Usage

classify.aep(fold, cuts, Cs, x, y, cv.repeat, int, DEBUG = DEBUG, Gsub)

Arguments

fold	number of -folds cross validation (CV)
cuts	list for randomly divide the training set in to x-x-folds CV
Cs	soft-margin tuning parameter of the SVM. Defaults to 10^c(-3:3).
x	gene expression data
y	class labels
cv.repeat	model for one CV training and predicting
int	Intersect of genes in network and gene expression profile.
DEBUG	show debugging information in screen more or less.
Gsub	an adjacency matrix that represents the underlying biological network.

Value

fold	the recored for test fold
auc	The AUC values of test fold
train	The tranined models for traning folds
feat	The feature selected by each by the train

Author(s)

Yupeng Cun [e-mail]

References

Guo et al., Towards precise classification of cancers based on robust gene functional expression profiles. BMC Bioinformatics 2005, 6:58.

See Also

See Also as cv.aep

Examples

#See cv.aep

netClass documentation built on May 29, 2017, 7:18 p.m.