CBCI: The Conditional Independence Bertin Classification Index

In extracat: Categorical Data Analysis and Visualization

Description

Computes the Conditional Independence Bertin Classification Index which uses conditional independence as a reference for normalization. High values indicate that the BCC is not far from the expectation if we know the two marginal 2D BBC values.

Usage

CBCI(x, r = 1,  joint.order = FALSE)

Arguments

x	The 3D table with non-negative entries.
r	The index of the conditioning variable, e.g. r = 1 uses the table with variables 2 and 3 conditionally independent given 1 for normalization.
joint.order	Whether or not to use a joint ordering for all variables. Otherwise the pairwise values are computed using separate reorderings.

Details

The BCI of a 3D table but instead of the total independence case the conditional independence case is used for normalization.

Value

Numeric value in [0,1].

Author(s)

Alexander Pilhoefer

See Also

BCI, JBCI, WBCI

Examples

## Not run: 
A <- optile(arsim(10000, c(11,12,13), 4,0.1))

BCI(A)

CBCI(A,1,TRUE)
CBCI(A,1,FALSE)


## End(Not run)

extracat documentation built on July 17, 2018, 5:05 p.m.