ddhellinger: Distance between probability distributions of discrete...

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ddhellingerR Documentation

Distance between probability distributions of discrete variables given samples


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


ddhellinger(x1, x2)


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.


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.


The distance between the two probability distributions.


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


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.


# 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.