Description Usage Arguments Value See Also Examples

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.*

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

`table_variables` |
A list of factors as categorical variables. |

`table_class` |
A factor representing the class of the case. |

Multivariate symmetrical uncertainty estimation for the
variable set {`table_variables`

,
`table_class`

}. The result is `round`

ed to 7 decimal
places.

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)
``` |

msu documentation built on Sept. 30, 2017, 5:05 p.m.

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