cv.frsvm: Cross validation for FrSVM

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

Description

Cross validation for FrSVM, an R algorithm, which integrates protein-protein interaction network information into gene selection for microarry classification

Usage

cv.frsvm(x, y, folds = 10, Gsub = matrix(1, 100, 100), repeats
                 = 5, parallel = FALSE, cores = 2, DEBUG = FALSE, d =
                 0.85, top.uper = 10, top.lower = 50, seed = 1234, Cs =
                 10^c(-3:3))

Arguments

x	gene expression data
y	class labels
folds	number of -folds cross validation (CV)
Gsub	Adjacency matrix of Protein-protein intersction network
repeats	number of CV repeat times
parallel	paralle computing or not
cores	cores used in parallel computing
DEBUG	show more results or not
d	damping factor for GeneRank, defaults value is 0.5
top.uper	the uper bound of top ranked genes
top.lower	the lower bound of top ranked genes
seed	Seed for random sampling.
Cs	soft-margin tuning parameter of the SVM. Defaults to 10^c(-3:3).

Value

a LIST for Cross-Validation results

auc	The AUC values of each test fold
fits	The tranined models for traning folds
feat	The feature selected by each by the fits
labels	the original lables for training

Author(s)

Yupeng Cun [e-mail]

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.

Examples

library(netClass)
data(expr)
data(ad.matrix)
x <- expr$genes
y <- expr$y
### 
r.frsvm <-cv.frsvm(x[,1:200], y, folds=3,Gsub=ad.matrix, repeats=1, parallel=FALSE,
	cores=2, DEBUG=TRUE,d=.85,top.uper=5,top.lower=15,seed=1234,Cs=10^c(-3:3))

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