MIk: Calculates mutual information

In nvihrs14/tcherry: Learning the structure of tcherry trees

Description

Calculates mutual information for k categorical variables.

Usage

MIk(variables, data, smooth = 0, log_base = 2)

Arguments

variables	Vector of variable names.
data	Data frame or matrix with the variables.
smooth	Additional cell counts for bayesian estimation of probabilities.
log_base	The base of the logarithmic function to be used.

Details

The mutual information for variables V1,…,Vk is calculated by the formula

MI(V1,…,Vk) = ∑ P(V1,…,Vk) log(P (V1,…,Vk) / (P(V1)… P(Vk)))

where the sum is over all possible values of V1,…,Vk.

If the function is used for two variables it corresponds to using MI2 and for three variables it corresponds to MI3.

Value

The mutual information given by a single numeric value.

Author(s)

Katrine Kirkeby, [e-mail]

Maria Knudsen, [e-mail]

Ninna Vihrs, [e-mail]

References

\insertRef

EKTShyptcherry

See Also

MI2 and MI3 for mutual infomation for two or three variables respectively.

Examples

set.seed(43)
var1 <- c(sample(c(1, 2), 100, replace = TRUE))
var2 <- var1 + c(sample(c(1, 2), 100, replace = TRUE))
var3 <- var1 + c(sample(c(0, 1), 100, replace = TRUE,
                        prob = c(0.9, 0.1)))
var4 <- c(sample(c(1, 2), 100, replace = TRUE))
var5 <- var2 + var3
var6 <- var1 - var4 + c(sample(c(1, 2), 100, replace = TRUE))
var7 <- c(sample(c(1, 2), 100, replace = TRUE))

data <- data.frame("var1" = as.character(var1),
                   "var2" = as.character(var2),
                   "var3" = as.character(var3),
                   "var4" = as.character(var4),
                   "var5" = as.character(var5),
                   "var6" = as.character(var6),
                   "var7" = as.character(var7))
                   
MIk(c("var1", "var2", "var7"), data, smooth = 0.001)

nvihrs14/tcherry documentation built on Aug. 1, 2020, 6:25 p.m.