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