persp-methods: Methods for Function 'persp' in Package 'copula'

In copula: Multivariate Dependence with Copulas

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], zlim = NULL,
      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, \frac 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, zlim, 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 \times 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(claytonCopula(2),   pCopula, main = "CDF of claytonCopula(2)")
persp(  frankCopula(1.5), dCopula, main = "Density of frankCopula(1.5)")
persp(  frankCopula(1.5), dCopula, main = "c_[frank(1.5)](.)", zlim = c(0,2))

## Examples with negative tau:
(th1 <- iTau(amhCopula(), -0.1))
persp(amhCopula(th1), dCopula)
persp(amhCopula(th1), pCopula, ticktype = "simple") # no axis ticks
persp(  frankCopula(iTau(  frankCopula(), -0.1)), dCopula)
persp(claytonCopula(iTau(claytonCopula(), -0.1)), dCopula)
##
cCop.2 <- function(u, copula, ...) cCopula(u, copula, ...)[,2]
persp(    amhCopula(iTau(    amhCopula(), -0.1)), cCop.2, main="cCop(AMH...)[,2]")
persp(  frankCopula(iTau(  frankCopula(), -0.1)), cCop.2, main="cCop(frankC)[,2]")
## and  Clayton  also looks "the same" ...

## MVDC Examples ------------------------------------
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.")

copula documentation built on Sept. 11, 2024, 7:48 p.m.