netClass (version 1.2.1)

cv.pac: Cross validation for Pathway Activities Classification(PAC)

Description

Cross validation for Pathway Activities Classification(PAC) using Logistic regression model for classification. Implementation of the Pathway Activities Classification by CROG algorithm.

Usage

cv.pac(x=x, y=y, folds=10, repeats=5, parallel = TRUE, cores = NULL, 
		DEBUG=TRUE, Gsub=matrix(1,100,100), 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 debugging information in screen or not.

Gsub

Adjacency matrix of Protein-protein intersction network

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

References

Lee E, Chuang H-Y, Kim J-W, Ideker T, Lee D (2008) Inferring Pathway Activity toward Precise Disease Classification. PLoS Comput Biol 4(11): e1000217.

Examples

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

library(KEGG.db)
r.pac <- cv.pac(x=x, y=y, folds=3, repeats=1, parallel=FALSE, cores=2, DEBUG=TRUE,
				Gsub=ad.matrix,seed=1234)