cv.aep: Cross validation for aepSVM (aepSVM)

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

Description

Cross validation for aepSVM (aepSVM) using SAM to select significant differential expressed genes

Usage

cv.aep(x, y, folds = 10, repeats = 5, parallel = FALSE, cores
                 = 2, DEBUG = TRUE, Gsub = matrix(1, 100, 100), 
                 Cs = 10^(-3:3), seed = 1234)

Arguments

x	a p x n matrix of expression measurements with p samples and n genes.
y	a factor of length p comprising the class labels.
folds	number of -folds cross validation (CV)
repeats	number of CV repeat times
parallel	paralle computing or not
cores	cores used in parallel computing
DEBUG	show more results or not
Gsub	Adjacency matrix of Protein-protein interaction network
Cs	soft-margin tuning parameter of the SVM. Defaults to 10^c(-3:3).
seed	seed for random sampling.

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

Guo et al., Towards precise classification of cancers based on robust gene functional expression profiles. BMC Bioinformatics 2005, 6:58.

Examples

library(netClass)
data(expr)
data(ad.matrix)
x <- expr$genes
y <- expr$y

 library(KEGG.db)
#r.aep <- cv.aep(x[,1:500], y, folds=3, repeats=1, parallel=FALSE,cores=2,
#			Gsub=ad.matrix,	Cs=10^(-3:3),seed=1234,DEBUG=TRUE)

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