pcalg (version 1.1-4)

disCItest: Test for (conditional) independence for discrete data

Description

This function tests for (condtitional) independence between discrete random variables. The function is written in a way, so that it can be easily used in skeleton, pc and fci.

Usage

disCItest(x, y, S, suffStat)

Arguments

x

Position of variable X in the adjacency matrix

y

Position of variable Y in the adjacency matrix

S

Position of conditioning variables in the adjacency matrix

suffStat

A list with three elements: (1) Element "dm" containing the data matrix (columns are variables, rows are samples), (2) element "nlev" containing a vector with the numbers of levels for each variable and (3) element "adaptDF" as a boolean varia

Value

• The p-value of the test.

Details

This function is based on gSquareDis; see its help file for details.

See Also

dsepTest, gaussCItest and binCItest for similar functions for a d-separation oracle, a conditional independence test for gaussian variables and a conditional independence test for binary variables, respectively.

Examples

## Simulate data
set.seed(123)
x <- sample(1:3,100,TRUE)
y <- sample(1:4,100,TRUE)
z <- sample(1:2,100,TRUE)
dat <- cbind(x,y,z)

suffStat <- list(dm = dat, nlev = c(3,4,2), adaptDF = FALSE)
disCItest(1,3,2,suffStat)