# msu: Estimating Multivariate Symmetrical Uncertainty. In msu: Multivariate Symmetric Uncertainty and Other Measurements

## Description

MSU is a generalization of symmetrical uncertainty (`SU`) where it is considered the interaction between two or more variables, whereas SU can only consider the interaction between two variables. For instance, consider a table with two variables X1 and X2 and a third variable, Y (the class of the case), that results from the logical XOR operator applied to X1 and X2

 X1 X2 Y 0 0 0 0 1 1 1 0 1 1 1 0

For this case

MSU(X1, X2, Y) = 0.5.

This, in contrast to the measurements obtained by SU of the variables X1 and X2 against Y,

SU(X1, Y) = 0

and

SU(X2, Y) = 0.

## Usage

 `1` ```msu(table_variables, table_class) ```

## Arguments

 `table_variables` A list of factors as categorical variables. `table_class` A factor representing the class of the case.

## Value

Multivariate symmetrical uncertainty estimation for the variable set {`table_variables`, `table_class`}. The result is `round`ed to 7 decimal places.

`symmetrical_uncertainty`
 ```1 2 3 4 5 6 7 8 9``` ```# completely predictable msu(list(factor(c(0,0,1,1))), factor(c(0,0,1,1))) # XOR msu(list(factor(c(0,0,1,1)), factor(c(0,1,0,1))), factor(c(0,1,1,0))) ## Not run: msu(c(factor(c(0,0,1,1)), factor(c(0,1,0,1))), factor(c(0,1,1,0))) msu(list(factor(c(0,0,1,1)), factor(c(0,1,0,1))), c(0,1,1,0)) ## End(Not run) ```