ddjeffreys | R Documentation |

jeffreys's divergence (symmetrized Kullback-Leibler divergence) between two multivariate (`q > 1`

) or univariate (`q = 1`

) discrete probability distributions, estimated from samples.

```
ddjeffreys(x1, x2)
```

`x1, x2` |
vectors or data frames of 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 jeffreys's divergence between the discrete probability distributions of the samples are computed using the `ddjeffreyspar`

function.

The divergence 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.

`ddjeffreyspar`

: Jeffrey's distances between two discrete distributions, given the probabilities on their common support.

Other distances: `ddchisqsym`

, `ddhellinger`

, `ddjensen`

, `ddlp`

.

```
# Example 1
x1 <- c("A", "A", "B", "B")
x2 <- c("A", "A", "A", "B", "B")
ddjeffreys(x1, x2)
# Example 2 (Its value can be infinity -Inf-)
x1 <- c("A", "A", "B", "C")
x2 <- c("A", "A", "A", "B", "B")
ddjeffreys(x1, x2)
# Example 3
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")))
ddjeffreys(x1, x2)
```

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