cv.hubc: Cross validation for hub nodes classification

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

Description

Cross validation for hub nodes classification, which described in Taylor et al.(2009).

Usage

cv.hubc(x, y, folds = 10, repeats = 5, parallel = TRUE, cores  = NULL, 
		DEBUG = TRUE, nperm = 500, node.ct = 0.98, Gsub = matrix(1, 100, 100), 
		Gs = Gs, seed = 1234, Cs = 10^c(-3:3))

Arguments

x	a p x n matrix of expression measurements with p samples and n genes.
y	a factor of length p comprising the class labels.
folds	number of -folds cross validation (CV)
repeats	number of CV repeat times
parallel	paralle computing or not
cores	cores used in parallel computing
DEBUG	show more results or not
nperm	number of permutation test steps
node.ct	cut off value for select highly quantile nodes in a nwtwork. Defaults to 0.98).
Gsub	an adjacency matrix that represents the underlying biological network.
Gs	Undirected of graph with adjacency matrix Gsub.
seed	Seed for random sampling.
Cs	Soft-margin tuning parameter of the SVM. Defaults to 10^c(-3:3).

Value

auc	The AUC values of each test fold
fits	The tranined models for traning folds
feat	The selected features of each training folds
labels	the original lables for training

Author(s)

Yupeng Cun [e-mail]

References

Taylor et al.(2009)Dynamic modularity in protein interaction networks predicts breast cancer outcome, Nat. Biotech.: doi: 10.1038/nbt.1522

Examples

 data(ad.matrix)
 #data(Gs2)
 library(netClass)
 data(expr)
 x <- expr$genes
 y <- expr$y


# r.hubC <- cv.hubc(x=x, y=y, folds=3, repeats=1, parallel=FALSE, cores=2, DEBUG=TRUE,
#				nperm=2, Gsub=ad.matrix,Gs=Gs2,node.ct=0.5,Cs=10^(-3:3))

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