netClass (version 1.0)

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, 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

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