QUnif | R Documentation |
These functions provide quasi random numbers or space filling or
low discrepancy sequences in the p
-dimensional unit cube.
sHalton(n.max, n.min = 1, base = 2, leap = 1)
QUnif (n, min = 0, max = 1, n.min = 1, p, leap = 1, silent = FALSE)
n.max |
maximal (sequence) number. |
n.min |
minimal sequence number. |
n |
number of |
base |
integer |
min , max |
lower and upper limits of the univariate intervals.
Must be of length 1 or |
p |
dimensionality of space (the unit cube) in which points are generated. |
leap |
integer indicating (if |
silent |
logical asking to suppress the message about enlarging
the prime table for large |
sHalton(n,m)
returns a numeric vector of length n-m+1
of
values in [0,1]
.
QUnif(n, min, max, n.min, p=p)
generates n-n.min+1
p-dimensional points in [min,max]^p
returning a numeric matrix
with p columns.
For leap
Kocis and Whiten recommend values of
L=31,61,149,409
, and particularly the L=409
for dimensions
up to 400.
Martin Maechler
James Gentle (1998) Random Number Generation and Monte Carlo Simulation; sec.\ 6.3. Springer.
Kocis, L. and Whiten, W.J. (1997) Computational Investigations of Low-Discrepancy Sequences. ACM Transactions of Mathematical Software 23, 2, 266–294.
32*sHalton(20, base=2)
stopifnot(sHalton(20, base=3, leap=2) ==
sHalton(20, base=3)[1+2*(0:9)])
## ------- a 2D Visualization -------
Uplot <- function(xy, axes=FALSE, xlab="", ylab="", ...) {
plot(xy, xaxs="i", yaxs="i", xlim=0:1, ylim=0:1, xpd = FALSE,
axes=axes, xlab=xlab, ylab=ylab, ...)
box(lty=2, col="gray40")
}
do4 <- function(n, ...) {
op <- mult.fig(4, main=paste("n =", n,": Quasi vs. (Pseudo) Random"),
marP=c(-2,-2,-1,0))$old.par
on.exit(par(op))
for(i in 1:2) {
Uplot(QUnif(n, p=2), main="QUnif", ...)
Uplot(cbind(runif(n), runif(n)), main="runif", ...)
}
}
do4(100)
do4(500)
do4(1000, cex = 0.8, col="slateblue")
do4(10000, pch= ".", col="slateblue")
do4(40000, pch= ".", col="slateblue")
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