classify.pac: Training and predicting using PAC classification methods

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

Description

Training and predicting using PAC classification methods

Usage

classify.pac(fold, cuts, x, y, cv.repeat, Gsub, int, DEBUG = FALSE)

Arguments

fold	number of -folds cross validation (CV)
cuts	list for randomly divide the training set in to x-x-folds CV
Gsub	an adjacency matrix that represents the underlying biological network.
x	gene expression data
y	a factor of length p comprising the 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 or not.

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

Lee E, Chuang H-Y, Kim J-W, Ideker T, Lee D (2008) Inferring Pathway Activity toward Precise Disease Classification. PLoS Comput Biol 4(11): e1000217. doi:10.1371/journal.pcbi.1000217

See Also

See Also as cv.pac

Examples

#see cv.pac

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