classify.stsvm: Training and predicting using stSVM classification methods
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 stSVM classification methods

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classify.stsvm(fold, cuts, ex.sum, x, p, a, y, cv.repeat, DEBUG = DEBUG, 
				Gsub=Gsub,  op.method=op.method, op = op, aa = aa, 
				dk = dk, dk.tf = dk.tf, seed = seed, Cs = Cs)

`fold`	number of folds to perform
`cuts`	list for randomly divide the training set in to x-x-folds CV
`ex.sum`	expression data
`x`	expression data
`a`	constant value of random walk kernel
`p`	random walk step(s) of random walk kernel
`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.
`Gsub`	an adjacency matrix that represents the underlying biological network.
`op.method`	Method for selecet optimal feature subgoups: pt is permutation test, sp is span bound.
`op`	optimal on top op
`aa`	permutation test steps
`dk`	Random Walk Kernel matrix of network
`dk.tf`	cut off p-value of permutation test
`seed`	seed for random sampling.
`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

Yupeng Cun yupeng.cun@gmail.com

Yupeng Cun, Holger Frohlich (2013) Network and Data Integration for Biomarker Signature Discovery via Network Smoothed T-Statistics. PLoS ONE 8(9): e73074. doi:10.1371/journal.pone.0073074

see cv.stsvm