rand | R Documentation |
Create random matrices or random points in a unit circle (Matlab style).
rand(n = 1, m = n)
randn(n = 1, m = n)
randi(imax, n = 1, m = n)
randsample(n, k, w = NULL, replacement = FALSE)
rands(n = 1, N = 1, r = 1)
randp(n = 1, r = 1)
n, m |
integers specifying the size of the matrix |
imax |
integer or pair of integers |
k |
number of elements to return. |
w |
weight vector, used for discrete probabilities. |
replacement |
logical; sampling with or without replacement. |
N |
dimension of a shere, N=1 for the unit circle |
r |
radius of circle, default 1. |
rand()
, randn()
, randi()
create random matrices of
size n x m
, where the default is square matrices if m
is
missing.
rand()
uses the uniform distribution on ]0, 1[
, while
randn()
uses the normal distribution with mean 0 and standard
deviation 1.
randi()
generates integers between imax[1]
and imax[2]
resp. 1 and imax
, if imax
is a scalar.
randsample()
samples k
elements from 1:n
, with or
without replacement, or returns a weighted sample (with replacement),
using the weight vector w
for probabilities.
rands()
generates uniformly random points on an N
-sphere in
the N+1
-dimensional space. To generate uniformly random points in the
N
-dim. unit cube, take points in S^{N-1}
und multiply with
unif(n)^(1/(N-1))
.
randp()
generates uniformly random points in the unit circle (or in
a circle of radius r).
Matrices of size nxm
resp. a vector of length n
.
randp()
returns a pair of values representing a point in the circle,
or a matrix of size (n,2)
. rands()
returns a matrix of size
(n, N+1)
with all rows being vectors of length 1
.
The Matlab style of setting a seed is not available; use R style
set.seed(...)
.
Knuth, D. (1981). The Art of Computer programming; Vol. 2: Seminumerical Algorithms; Chapt. 3: Random Numbers. Addison-Wesley, Reading.
set.seed
rand(3)
randn(1, 5)
randi(c(1,6), 1, 10)
randsample(10, 5, replacement = TRUE, w = c(0,0,0, 1, 1, 1, 1, 0,0,0))
P <- rands(1000, N = 1, r = 2)
U <- randp(1000, 2)
## Not run:
plot(U[, 1], U[, 2], pch = "+", asp = 1)
points(P, pch = ".")
## End(Not run)
#-- v is 2 independent normally distributed elements
# u <- randp(1); r <- t(u) %*% u
# v <- sqrt(-2 * log(r)/r) * u
n <- 5000; U <- randp(n)
R <- apply(U*U, 1, sum)
P <- sqrt(-2 * log(R)/R) * U # rnorm(2*n)
## Not run:
hist(c(P))
## End(Not run)
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