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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
# Copyrights (C)
# for this R-port:
# 1999 - 2007, Diethelm Wuertz, GPL
# Diethelm Wuertz <wuertz@itp.phys.ethz.ch>
# info@rmetrics.org
# www.rmetrics.org
# for the code accessed (or partly included) from other R-ports:
# see R's copyright and license files
# for the code accessed (or partly included) from contributed R-ports
# and other sources
# see Rmetrics's copyright file
################################################################################
# FUNCTION: DESCRIPTION:
# runif.pseudo Uniform Pseudo Random number sequence
# rnorm.pseudo Normal Pseudo Random number sequence
# runif.halton Uniform Halton low discrepancy sequence
# rnorm.halton Normal Halton low discrepancy sequence
# runif.sobol Uniform Sobol low discrepancy sequence
# rnorm.sobol Normal Sobol low discrepancy sequence
################################################################################
test.pseudo =
function()
{
# Pseudo Random Numbers:
# Uniform and Normal pseudo random number sequences
# RVs:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
# Graphics Frame:
par(mfrow = c(2, 2), cex = 0.75)
# Histogram Uniform:
runif.pseudo(n = 10, dimension = 5)
r = runif.pseudo(n = 1000, dimension = 1)
hist(r, probability = TRUE, main = "Uniform Pseudo", xlab = "x",
col = "steelblue", border = "white")
abline (h = 1, col = "orange", lwd = 2)
# Scatterplot Uniform:
r = runif.pseudo(n = 1000, dimension = 2)
plot(r, cex = 0.5, main = "Scatterplot Uniform Pseudo")
# Histogram Normal:
rnorm.pseudo(n = 10, dimension = 5)
r = rnorm.pseudo(n = 1000, dimension = 1)
hist(r, probability = TRUE, xlim = c(-3, 3), main = "Normal Pseudo",
xlab = "x", col = "steelblue", border = "white")
x = seq(-3, 3, length = 301)
lines(x, dnorm(x), col = "orange", lwd = 2)
# Scatterplot Normal:
r = rnorm.pseudo(n = 1000, dimension = 2)
plot(r, cex = 0.5, main = "Scatterplot Normal Pseudo")
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.halton =
function()
{
# Halton Sequence:
# Uniform and Normal Halton low discrepancy sequences
# RVs:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
# Graphics Frame:
par(mfrow = c(2, 2), cex = 0.75)
# Histogram Uniform:
runif.halton(n = 10, dimension = 5)
r = runif.halton(n = 5000, dimension = 1)
hist(r, probability = TRUE, main = "Uniform Halton", xlab = "x",
col = "steelblue", border = "white")
abline (h = 1, col = "orange", lwd = 2)
# Scatterplot Uniform:
r = runif.halton(n = 1000, dimension = 2)
plot(r, cex = 0.5, main = "Scatterplot Uniform Halton")
# Histogram Normal:
rnorm.halton(n = 10, dimension = 5)
r = rnorm.halton(n = 5000, dimension = 1)
hist(r, probability = TRUE, xlim = c(-3, 3), main = "Normal Halton",
xlab = "x", col = "steelblue", border = "white")
x = seq(-3, 3, length = 301)
lines(x, dnorm(x), col = "orange", lwd = 2)
# Scatterplot Normal:
r = rnorm.halton(n = 1000, dimension = 2)
plot(r, cex = 0.5, main = "Scatterplot Normal Halton")
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.sobol =
function()
{
# Sobol Sequence:
# Uniform and Normal Sobol low discrepancy sequences
# RVs:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
# Graphics Frame:
par(mfrow = c(2, 2), cex = 0.75)
# Histogram Uniform:
runif.sobol(n = 10, dimension = 5)
r = runif.sobol(5000, 1)
hist(r, probability = TRUE, main = "Uniform Sobol",
xlab = "x", col = "steelblue", border = "white")
abline (h = 1, col = "orange", lwd = 2)
# Scatterplot Uniform:
# Histogram Normal:
rnorm.sobol(n = 10, dimension = 5)
r = rnorm.sobol(1000, 1)
hist(r, probability = TRUE, main = "Normal Sobol",
xlab = "x", col = "steelblue", border = "white")
x = seq(-3, 3, length = 301)
lines(x, dnorm(x), col = "orange", lwd = 2)
# Scatterplot Normal:
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.scrambling =
function()
{
# Sobol Scrambling:
# RVs:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
# runif.sobol(n, dimension, init = TRUE, scrambling = 0, seed = 4711)
# Unscrambled:
runif.sobol(10, 5)
# Owen Type Scrambling:
runif.sobol(10, 5, scrambling = 1)
# Faure-Tezuka Type Scrambling:
runif.sobol(10, 5, scrambling = 2)
# Combined Owen and Faure-Tezuka Type Scrambling:
runif.sobol(10, 5, scrambling = 3)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.restart =
function()
{
# Sobol Restart:
# RVs:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
# runif.sobol(n, dimension, init = TRUE, scrambling = 0, seed = 4711)
runif.sobol(10, 5, init = TRUE)
runif.sobol(10, 5, init = FALSE)
# Seed:
print(.runif.sobol.seed)
# Return Value:
return()
}
################################################################################
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