contourmvggd: Contour Plot of the Bivariate Generalised Gaussian Density

In mggd: Multivariate Generalised Gaussian Distribution; Kullback-Leibler Divergence

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

contourmvggd(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 mvdggd.
...	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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/03610929808832115")}

See Also

plotmvggd: plot of a bivariate generalised Gaussian density.

mvdggd: 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
contourmvggd(mu, Sigma, beta)

mggd documentation built on March 31, 2023, 9:56 p.m.