classify.frsvm: Training and predicting using FrSVM
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 FrSVM

1 2	classify.frsvm(fold, cuts, x, y, cv.repeat, DEBUG = DEBUG, Gsub = Gsub, d = d, op = op, aa = aa, Cs = Cs)

`fold`	number of folds to perform
`cuts`	list for randomly divide the training set in to x-x-CV
`x`	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.
`Gsub`	an adjacency matrix that represents the underlying biological network.
`d`	damping factor for GeneRank, defaults value is 0.5
`op`	the uper bound of top ranked genes
`aa`	the lower bound of top ranked genes
`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 (2012) Integrating Prior Knowledge Into Prognostic Biomarker Discovery Based on Network Structure.arXiv:1212.3214
Winter C, Kristiansen G, Kersting S, Roy J, Aust D, et al. (2012) Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes. PLoS Comput Biol 8(5): e1002511. doi:10.1371/journal.pcbi.1002511

See Also as cv.frsvm