splom2-methods | R Documentation |
Methods splom2()
to draw scatter-plot matrices of
(random samples of) distributions from package copula.
## S4 method for signature 'matrix'
splom2(x, varnames = NULL, varnames.null.lab = "U",
xlab = "", col.mat = NULL, bg.col.mat = NULL, ...)
## ditto an identical 'data.frame' method
## S4 method for signature 'Copula'
splom2(x, n, ...)
## S4 method for signature 'mvdc'
splom2(x, n, varnames.null.lab = "X", ...)
x |
a |
n |
when |
varnames |
the variable names, typically unspecified. |
varnames.null.lab |
the |
xlab |
the x-axis label. |
col.mat |
a matrix of colors (or one color) for the plot symbols;
if |
bg.col.mat |
a matrix of colors for the background (the default is the
setting as obtained from
|
... |
additional arguments passed to the underlying
|
From splom()
, an R object of class "trellis"
.
pairs2()
for a similar function (for matrices and data
frames) based on pairs()
.
The lattice-based cloud2-methods
for 3D data, and
wireframe2-methods
and
contourplot2-methods
for functions.
## For 'matrix' objects
## Create a 100 x 7 matrix of random variates from a t distribution
## with four degrees of freedom and plot the generated data
n <- 1000 # sample size
d <- 3 # dimension
nu <- 4 # degrees of freedom
tau <- 0.5 # Kendall's tau
th <- iTau(tCopula(df = nu), tau) # corresponding parameter
cop <- tCopula(th, dim = d, df = nu) # define copula object
set.seed(271)
U <- rCopula(n, copula = cop)
splom2(U)
## For 'copula' objects
set.seed(271)
splom2(cop, n = n) # same as above
## For 'rotCopula' objects: ---> Examples in rotCopula
## For 'mvdc' objects
mvNN <- mvdc(cop, c("norm", "norm", "exp"),
list(list(mean = 0, sd = 1), list(mean = 1), list(rate = 2)))
splom2(mvNN, n = n)
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