contourmggd {mggd}

Contour Plot of the Bivariate Generalised Gaussian Density

Description

Draws the contour plot of the probability density of the generalised Gaussian distribution with 2 variables with mean vector mu, dispersion matrix Sigma and shape parameter beta.

Usage

contourmggd(mu, Sigma, beta,
                    xlim = c(mu[1] + c(-10, 10)*Sigma[1, 1]),
                    ylim = c(mu[2] + c(-10, 10)*Sigma[2, 2]),
                    zlim = NULL, npt = 30, nx = npt, ny = npt,
                    main = "Multivariate generalised Gaussian density",
                    sub = NULL, nlevels = 10,
                    levels = pretty(zlim, nlevels), tol = 1e-6, ...)

Arguments

mu

length 2 numeric vector.

Sigma

symmetric, positive-definite square matrix of order 2. The dispersion matrix.

beta

positive real number. The shape of the first distribution.

xlim, ylim

x-and y- limits.

zlim

z- limits. If NULL, it is the range of the values of the density on the x and y values within xlim and ylim.

npt

number of points for the discretisation.

nx, ny

number of points for the discretisation among the x- and y- axes.

main, sub

main and sub title, as for title.

nlevels, levels

arguments to be passed to the contour function.

tol

tolerance (relative to largest variance) for numerical lack of positive-definiteness in Sigma, for the estimation of the density. see dmggd.

...

additional arguments to plot.window, title, Axis and box, typically graphical parameters such as cex.axis.

Value

Returns invisibly the probability density function.

Author(s)

Pierre Santagostini, Nizar Bouhlel

References

E. Gomez, M. Gomez-Villegas, H. Marin. A Multivariate Generalization of the Power Exponential Family of Distribution. Commun. Statist. 1998, Theory Methods, col. 27, no. 23, p 589-600. doi:10.1080/03610929808832115

See Also

plotmggd: plot of a bivariate generalised Gaussian density.

dmggd: Probability density of a multivariate generalised Gaussian distribution.

Examples

mu <- c(1, 4)
Sigma <- matrix(c(0.8, 0.2, 0.2, 0.2), nrow = 2)
beta <- 0.74
contourmggd(mu, Sigma, beta)