train.stsvm: Training the data using stsvm methods

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

Description

Training the data using stsvm methods

Usage

train.stsvm(x=x, y=y, DEBUG=FALSE,Gsub=Gsub, op.method="sp", op=10,aa=100,
			dk=dk, dk.tf=0.05,seed = 1234,Cs=10^(-3:3),EN2SY=NULL)

Arguments

x	expression data for training
y	Class labels
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).
EN2SY	A list for mapping gene sybol ids or entez ids.

Value

The list returned

trained	The tranined models for traning folds
feat	The feature selected by each by the train

Author(s)

Yupeng Cun [e-mail]

See Also

See cv.stsvm

Examples

#see cv.stsvm

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