classify.hubc: Training and predicting using hub nodes classification...
In netClass: netClass: An R Package for Network-Based Biomarker Discovery

Description Usage Arguments Value Author(s) References See Also Examples

Training and predicting using hub nodes classification methods

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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)

`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)`.

`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