SCCI {SCCI} | R Documentation |
Stochastic Complexity-based Conditional Independence Criterium
Description
Calculates whether two random variables X
and Y
are independent given a set of variables Z
using SCCI
. A score of 0
denotes that independence holds and values greater than 0
mean that X
is not independent of Y
given Z
. For details on SCCI
, we refer to Marx and Vreeken (AISTATS, 2019). If you use SCCI
in your work, please cite Marx and Vreeken (AISTATS, 19).
The output of SCCI(..)
is the difference in number of bits between condtioning X
only on Z
and conditioning on Z
and Y
. For the variant of SCCI
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