persp-methods {copula}R Documentation

Methods for Function ‘persp’ in Package ‘copula’

Description

Methods for function persp to draw perspective plots (of two dimensional distributions from package copula).

Usage

## S4 method for signature 'Copula'
persp(x, FUN, n.grid = 26, delta = 0,
      xlab = "u1", ylab = "u2",
      zlab = deparse(substitute(FUN))[1],
      theta = -30, phi = 30, expand = 0.618,
      ticktype = "detail", ...)
## S4 method for signature 'mvdc'
persp(x, FUN, xlim, ylim, n.grid = 26,
      xlab = "x1", ylab = "x2", zlab = deparse(substitute(FUN))[1],
      theta = -30, phi = 30, expand = 0.618,
      ticktype = "detail", ...)

Arguments

x

a "Copula" or a "mvdc" object.

FUN

the function to be plotted; typically dCopula or pCopula.

n.grid

the number of grid points used in each dimension. This can be a vector of length two, giving the number of grid points used in x- and y-direction, respectively; the function FUN will be evaluated on the corresponding (x,y)-grid.

delta

A small number in [0, 1/2) influencing the evaluation boundaries. The x- and y- vectors will have the range [0+delta, 1-delta], the default being [0,1].

xlim, ylim

The range of the x and y variables, respectively.

xlab, ylab, zlab, theta, phi, expand, ticktype, ...

Arguments for (the default method of) persp(), the ones enumerated here all with different defaults than there.

Value

invisible; a list with the following components:

x, y

The numeric vectors, as passed to persp.default.

z

The matrix of evaluated FUN values on the grid as passed to persp.default.

persp

the 4 x 4 transformation matrix returned by persp.default.

Methods

Perspective plots for both "copula" or "mvdc" objects, see x in the Arguments section.

See Also

The contour-methods for drawing contour lines of the same functions.

Examples

persp(frankCopula(-0.8), dCopula)
persp(claytonCopula(2),  pCopula, main = "CDF of claytonCopula(2)")
## An example with negative tau:
(th1 <- iTau(amhCopula(), -0.1))
persp(amhCopula(th1), dCopula)
persp(amhCopula(th1), pCopula, ticktype = "simple") # no axis ticks

mvNN <- mvdc(gumbelCopula(3), c("norm", "norm"),
          list(list(mean = 0, sd = 1), list(mean = 1)))
persp(mvNN, dMvdc, xlim=c(-2, 2), ylim=c(-1, 3), main = "Density")
persp(mvNN, pMvdc, xlim=c(-2, 2), ylim=c(-1, 3), main = "Cumulative Distr.")

[Package copula version 0.999-15 Index]