ddhellinger: Distance between probability distributions of discrete...

In dad: Three-Way / Multigroup Data Analysis Through Densities

Distance between probability distributions of discrete variables given samples

Description

Hellinger (or Matusita) distance between two multivariate (q > 1) or univariate (q = 1) discrete probability distributions, estimated from samples.

Usage

ddhellinger(x1, x2)

Arguments

x1, x2

data frames of q columns or vectors (can also be tibbles). If they are data frames and have not the same column names, there is a warning.

Details

Let p_1 and p_2 denote the estimated probability distributions of the discrete samples x_1 and x_2. The Matusita distance between the discrete probability distributions of the samples are computed using the ddhellingerpar function.

Value

The distance between the two probability distributions.

Author(s)

Rachid Boumaza, Pierre Santagostini, Smail Yousfi, Sabine Demotes-Mainard

References

Deza, M.M. and Deza E. (2013). Encyclopedia of distances. Springer.

See Also

ddhellingerpar: Hellinger metric (Matusita distance) between two discrete distributions, given the on their common support probabilities.

Other distances: ddchisqsym, ddjeffreys, ddjensen, ddlp.

Examples

# Example 1
x1 <- c("A", "A", "B", "B")
x2 <- c("A", "A", "A", "B", "B")
ddhellinger(x1, x2)

# Example 2
x1 <- data.frame(x = factor(c("A", "A", "A", "B", "B", "B")),
                 y = factor(c("a", "a", "a", "b", "b", "b")))                 
x2 <- data.frame(x = factor(c("A", "A", "A", "B", "B")),
                 y = factor(c("a", "a", "b", "a", "b")))
ddhellinger(x1, x2)

dad documentation built on Aug. 30, 2023, 5:06 p.m.