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, Gsub, 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
Gsub
an adjacency matrix that represents the underlying biological network.
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