NormalizedMI: Maximal Normalized Mutual Information (MaxNMI) function for 2...
In linkspotter: Bivariate Correlations Calculation and Visualization
Description
Usage
Arguments
Value
Examples
Description
Calculate the MaxNMI relationship measurement for 2 categorical variables
Usage
1
NormalizedMI(x, y, includeNA = T)
Arguments
x
a vector of factor.
y
a vector of factor.
includeNA
a boolean. TRUE to include NA value as a factor level.
Value
a double between 0 and 1 corresponding to the MaxNMI.
Examples
1
2
3
4
# calculate a correlation dataframe
data(iris)
discreteSepalLength=BeEFdiscretization.numfact(continuousY=iris$Sepal.Length,factorX=iris$Species)
NormalizedMI(iris$Species,discreteSepalLength)
linkspotter documentation built on July 23, 2020, 5:08 p.m.
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Description Usage Arguments Value Examples
Description
Calculate the MaxNMI relationship measurement for 2 categorical variables
Usage
1 | NormalizedMI(x, y, includeNA = T)
|
Arguments
x |
a vector of factor. |
y |
a vector of factor. |
includeNA |
a boolean. TRUE to include NA value as a factor level. |
Value
a double between 0 and 1 corresponding to the MaxNMI.
Examples
1 2 3 4 | # calculate a correlation dataframe
data(iris)
discreteSepalLength=BeEFdiscretization.numfact(continuousY=iris$Sepal.Length,factorX=iris$Species)
NormalizedMI(iris$Species,discreteSepalLength)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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