netClass (version 1.0)

classify.pac: Training and predicting using PAC classification methods

Description

Training and predicting using PAC classification methods

Usage

classify.pac(fold, cuts, x, y, cv.repeat, int, DEBUG = FALSE)

Arguments

fold

number of -folds cross validation (CV)

cuts

list for randomly divide the training set in to x-x-folds CV

x

gene expression data

y

a factor of length p comprising the class labels.

cv.repeat

model for one CV training and predicting

int

Intersect of genes in network and gene expression profile.

DEBUG

show debugging information in screen or not.

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

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. doi:10.1371/journal.pcbi.1000217

See Also

See Also as {pac.cv}

Examples

#see \name{FrSVM.cv}