netClass (version 1.2.1)

classify.hubc: Training and predicting using hub nodes classification methods

Description

Training and predicting using hub nodes classification methods

Usage

classify.hubc(fold, r, cuts, x, y, cv.repeat, Gsub = Gsub, DEBUG = DEBUG, gHub = gHub, hubs = hubs, nperm = nperm, node.ct = node.ct, Cs = Cs)

Arguments

fold

number of -fold cross validation (CV)

cuts

list for randomly divide the training set in to x-x-fold 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

DEBUG

show debugging information in screen more or less.

r

repeat order for CV

gHub

Subgraph of hubs of graph Gs

hubs

Hubs in graph Gs

nperm

number of permutation test steps

node.ct

cut off value for select highly quantile nodes in a nwtwork. Defaults to 0.98).

Cs

Soft-margin tuning parameter of the SVM. Defaults to 10^c(-3:3).

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

References

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

See Also

See cv.hubc

Examples

#See cv.hubc