SCCI {SCCI}R Documentation

Stochastic Complexity-based Conditional Independence Criterium

Description

Calculates whether two random variables XX and YY are independent given a set of variables ZZ using SCCISCCI. A score of 00 denotes that independence holds and values greater than 00 mean that XX is not independent of YY given ZZ. For details on SCCISCCI, we refer to Marx and Vreeken (AISTATS, 2019). If you use SCCISCCI in your work, please cite Marx and Vreeken (AISTATS, 19).

The output of SCCI(..)SCCI(..) is the difference in number of bits between condtioning XX only on ZZ and conditioning on ZZ and YY. For the variant of SCCISCCI that gives outputs that can be intpreted as p-values, please refer to pSCCI.

Usage

SCCI(x, y, Z, score="fNML", sym=FALSE)	

Arguments

x

A discrete vector.

y

A discrete vector.

Z

A data frame consisting of zero or more columns of discrete vectors.

score

Default: fNML, optionally qNML can be passed.

sym

sym can be true or false

References

Alexander Marx and Jilles Vreeken; Testing Conditional Independence on Discrete Data using Stochastic Complexity, Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR, 2019

Examples

set.seed(1)
x = round((runif(1000, min=0, max=5)))
y = round((runif(1000, min=0, max=5)))
Z = data.frame(round((runif(1000, min=0, max=5))), round((runif(1000, min=0, max=5))))
SCCI(x=x,y=y,Z=Z,score="fNML",sym=FALSE)	## 0

[Package SCCI version 1.2 Index]