independent.contributions.formula.mul

a matrix or data.frame containing binary observations (columns are variables)

data

target.vars

vector of P(class labels = 1|varX=1) for different X

prob1

vector of P(class labels = 1|varX=0) for different X

prob0

logical.formula

Version of <code>independent.contributions.formula</code> that works with any number of variables. See the help page
for <code>independent.contributions.formula</code> for description of functionality.

Distinguish direct from indirect interactions in gene
regulation and infer combinatorial code from highly correlated
variables such as transcription factor binding profiles. The
package implements the Neighbourhood Consistent PC algorithm
(NCPC) and draws Direct Dependence Graphs to represent
dependence structure around a target variable. The package also
provides a unified interface to other Graphical Modelling
(Bayesian Network) packages for distinguishing direct and
indirect interactions.

independent.contributions.formula.mul: Generate class labels by independent contributions of two variables

Description

Usage

Arguments

Value

Examples