Methods for Function ‘persp’ in Package ‘copula’
Methods for function persp to draw perspective plots (of
two dimensional distributions from package copula).
## 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", ...)x |
|
FUN |
|
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 |
delta |
A small number in [0, 1/2)
influencing the evaluation boundaries. The x- and y- vectors will
have the range |
xlim, ylim |
The |
xlab, ylab, zlab, zlim, theta, phi, expand, ticktype, ... |
Arguments
for (the default method of) |
invisible; a list with the following components:
x, y |
The numeric vectors, as passed to |
z |
The matrix of evaluated |
persp |
the 4 x 4 transformation matrix
returned by |
The contour-methods for drawing contour lines of the
same functions.
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.")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.