jeffreys {dad}

Jeffreys measure between Gaussian densities

Description

Jeffreys measure (or symmetrised Kullback-Leibler divergence) between two multivariate (p > 1) or univariate (p = 1) Gaussian densities given samples (see Details).

Usage

jeffreys(x1, x2, check = FALSE)

Arguments

x1

a matrix or data frame of n_1 rows (observations) and p columns (variables) (can also be a tibble) or a vector of length n_1.

x2

matrix or data frame (or tibble) of n_2 rows and p columns or vector of length n_2.

check

logical. When TRUE (the default is FALSE) the function checks if the covariance matrices are not degenerate (multivariate case) or if the variances are not zero (univariate case).

Details

The Jeffreys measure between the two Gaussian densities is computed by using the jeffreyspar function and the density parameters estimated from samples.

Value

Returns the Jeffrey's measure between the two probability densities.

Be careful! If check = FALSE and one smoothing bandwidth matrix is degenerate, the result returned must not be considered.

Author(s)

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

References

Thabane, L., Safiul Haq, M. (1999). On Bayesian selection of the best population using the Kullback-Leibler divergence measure. Statistica Neerlandica, 53(3): 342-360.

See Also

jeffreyspar: Jeffreys measure between Gaussian densities, given their parameters.

Examples

require(MASS)
m1 <- c(0,0)
v1 <- matrix(c(1,0,0,1),ncol = 2) 
m2 <- c(0,1)
v2 <- matrix(c(4,1,1,9),ncol = 2)
x1 <- mvrnorm(n = 3,mu = m1,Sigma = v1)
x2 <- mvrnorm(n = 5, mu = m2, Sigma = v2)
jeffreys(x1, x2)