calculateDiscreteBinaryMI: calculate self information when an observation has a discrete...

In terminological/tidy-info-stats: Functions for manipulating information statistics

calculate self information when an observation has a discrete value (X).

Description

calculate self information when an observation has a discrete value (X).

Usage

calculateDiscreteBinaryMI(
  df,
  discreteVars,
  countVar = NULL,
  method = "Grassberger",
  ...
)

Arguments

df	- may be grouped, in which case the value is interpreted as different types of continuous variable - the grouping may be w.g. a test or concept.
discreteVars	- the column(s) of the categorical value (X) quoted by vars(...)
countVar	- optional the column of the count variable - how often does the event happen? If missing then this will be the assumed to be individual observations. In this case the df is a contingency table
method	- the method employed - valid options are "MontgomerySmith", "Histogram", "Grassberger", "InfoTheo", "Compression"
...	- the other parameters are passed onto the implementations

Value

a dataframe containing the disctinct values of the groups of df, and for each group a mutual information column (I). If df was not grouped this will be a single entry

terminological/tidy-info-stats documentation built on Nov. 19, 2022, 11:23 p.m.