netClass (version 1.2.1)

classify.frsvm: Training and predicting using FrSVM

Description

Training and predicting using FrSVM

Usage

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

Arguments

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).

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

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

See Also as cv.frsvm

Examples

#see cv.frsvm