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# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Library General Public
# License as published by the Free Software Foundation; either
# version 2 of the License, or (at your option) any later version.
#
# This library is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Library General Public License for more details.
#
# You should have received a copy of the GNU Library General
# Public License along with this library; if not, write to the
# Free Foundation, Inc., 59 Temple Place, Suite 330, Boston,
# MA 02111-1307 USA
################################################################################
# FUNCTION: DESCRIPTION:
# grid2d Returns from two vectors x-y grid coordinates
# density2d Returns 2D Kernel Density Estimates
# hist2d Returns 2D Histogram Counts
# FUNCTION: BIVARIATE DISTRIBUTIONS:
# pnorm2d Computes bivariate Normal probability function
# dnorm2d Computes bivariate Normal density function
# rnorm2d Generates bivariate normal random deviates
# pcauchy2d Computes bivariate Cauchy probability function
# dcauchy2d Computes bivariate Cauchy density function
# rcauchy2d Generates bivariate Cauchy random deviates
# pt2d Computes bivariate Student-t probability function
# dt2d Computes bivariate Student-t density function
# rt2d Generates bivariate Student-t random deviates
# FUNCTION: ELLIPTICAL DISTRIBUTIONS:
# delliptical2d Computes density for elliptical distributions
# REQUIREMENTS:
.perspPlot <- fBasics::.perspPlot
.contourPlot <- fBasics::.contourPlot
################################################################################
test.grid2d =
function()
{
# Grid Data:
grid2d(x = (0:10)/10)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.density2d =
function()
{
# Data:
z <- rnorm2d(1000)
# Density:
D = density2d(x = z[, 1], y = z[, 2])
.perspPlot(D)
.contourPlot(D)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.hist2d =
function()
{
# Data:
z <- rnorm2d(1000)
# Histogram:
H <- hist2d(x = z[, 1], y = z[, 2])
.perspPlot(H)
.contourPlot(H)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.norm2d =
function()
{
# pnorm2d - Computes bivariate Normal probability function
# dnorm2d - Computes bivariate Normal density function
# rnorm2d - Generates bivariate normal random deviates
# Normal Density:
x = (-40:40)/10
X = grid2d(x)
z = dnorm2d(X$x, X$y)
Z = list(x = x, y = x, z = matrix(z, ncol = length(x)))
.perspPlot(Z)
.contourPlot(Z)
# Normal Density, rho = 0.5:
x = (-40:40)/10
X = grid2d(x)
z = dnorm2d(X$x, X$y, rho = 0.5)
Z = list(x = x, y = x, z = matrix(z, ncol = length(x)))
.perspPlot(Z)
.contourPlot(Z)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.cauchy2d =
function()
{
# pcauchy2d - Computes bivariate Cauchy probability function
# dcauchy2d - Computes bivariate Cauchy density function
# rcauchy2d - Generates bivariate Cauchy random deviates
# Cauchy Density:
x = (-40:40)/10
X = grid2d(x)
z = dcauchy2d(X$x, X$y)
Z = list(x = x, y = x, z = matrix(z, ncol = length(x)))
.perspPlot(Z)
.contourPlot(Z)
# Cauchy Density, rho = 0.5:
x = (-40:40)/10
X = grid2d(x)
z = dcauchy2d(X$x, X$y, rho = 0.5)
Z = list(x = x, y = x, z = matrix(z, ncol = length(x)))
.perspPlot(Z)
.contourPlot(Z)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.t2d =
function()
{
# pt2d - Computes bivariate Student-t probability function
# dt2d - Computes bivariate Student-t density function
# rt2d - Generates bivariate Student-t random deviates
# Student Density:
x = (-40:40)/10
X = grid2d(x)
z = dt2d(X$x, X$y, nu = 4)
Z = list(x = x, y = x, z = matrix(z, ncol = length(x)))
.perspPlot(Z)
.contourPlot(Z)
# Student Density, rho = 0.5:
x = (-40:40)/10
X = grid2d(x)
z = dt2d(X$x, X$y, rho = 0.5, nu = 4)
Z = list(x = x, y = x, z = matrix(z, ncol = length(x)))
.perspPlot(Z)
.contourPlot(Z)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.delliptical2d =
function()
{
# Settings:
xy = grid2d((-50:50)/10)
# Contour Plots:
par(mfrow = c(1, 1))
contour(delliptical2d(xy, rho = 0.75, param = NULL,
type = "norm", output = "list"), main = "norm")
contour(delliptical2d(xy, rho = 0.75, param = NULL,
type = "cauchy", output = "list"), main = "cauchy")
contour(delliptical2d(xy, rho = 0.75, param = 4,
type = "t", output = "list"), main = "t")
contour(delliptical2d(xy, rho = 0.75, param = NULL,
type = "laplace", output = "list"), main = "laplace")
contour(delliptical2d(xy, rho = 0.75, param = NULL,
type = "kotz", output = "list"), main = "kotz")
contour(delliptical2d(xy, rho = 0.75, param = NULL,
type = "epower", output = "list"), main = "epower")
# Return Value:
return()
}
################################################################################
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