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

In ddgraph: Distinguish direct and indirect interactions with Graphical Modelling

Description

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

Usage

independent.contributions.formula.mul(data, target.vars, prob1, prob0, logical.formula)

Arguments

data	a matrix or data.frame containing binary observations (columns are variables)
target.vars	indexes of target variables
prob1	vector of P(class labels = 1|varX=1) for different X
prob0	vector of P(class labels = 1|varX=0) for different X
logical.formula	a character string for the formula

Value

a vector of binary class labels

Examples

# noisy OR function with three variables and with noise level of 0.1 for a, b, and 0.2 for c
data <- cbind("a"=c(0,0,0,0,1,1,1,1), "b"=c(0,0,1,1,0,0,1,1), "c"=c(0,1,0,1,0,1,0,1))
independent.contributions.formula.mul(data, c("a", "b", "c"), c(0.9, 0.9, 0.8), c(0.1, 0.1, 0.2), "a | b | c")

ddgraph documentation built on Nov. 17, 2017, 10:50 a.m.