getSlices: Duplicate Covariates According to Regulatory Network and...

In kouroshz/creNet: Fit a GLM with a combination of lasso and overlapping group lasso regularization

Usage

getSlices(ents, rels, x, y, cre = c("filter", "weight", "none"), cre.sig = 0.01, verbose = TRUE)

Arguments

ents	Data frame of entries typically obtained with processMicroArray
rels	Data frame of regulator/genes relations typically obtained with processMicroArray
x	Matrix of predictors (gene expression levels)
y	vector or responses
cre	CRE operations: filter significant regulators, only compute weights with no filtering, or none (no CRE)
cre.sig	significance level for CRE filtering. Defaults to 0.01
verbose	logical flag for verbosity level

kouroshz/creNet documentation built on May 20, 2019, 1:11 p.m.