plotmggd {mggd}

Plot of the Bivariate Generalised Gaussian Density

Description

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

Usage

plotmggd(mu, Sigma, beta, xlim = c(mu[1] + c(-10, 10)*Sigma[1, 1]),
                 ylim = c(mu[2] + c(-10, 10)*Sigma[2, 2]), n = 101,
                 xvals = NULL, yvals = NULL, xlab = "x", ylab = "y",
                 zlab = "f(x,y)", col = "gray", 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 distribution.

xlim, ylim

x-and y- limits.

n

A one or two element vector giving the number of steps in the x and y grid, passed to plot3d.function.

xvals, yvals

The values at which to evaluate x and y. If used, xlim and/or ylim are ignored.

xlab, ylab, zlab

The axis labels.

col

The color to use for the plot. See plot3d.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 pass to plot3d.function.

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

contourmggd: contour 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
plotmggd(mu, Sigma, beta)