| Uniform | R Documentation |
These functions provide information about the uniform distribution
on the interval from min to max. dunif gives the
density, punif gives the distribution function qunif
gives the quantile function and runif generates random
deviates.
dunif(x, min = 0, max = 1, log = FALSE) punif(q, min = 0, max = 1, lower.tail = TRUE, log.p = FALSE) qunif(p, min = 0, max = 1, lower.tail = TRUE, log.p = FALSE) runif(n, min = 0, max = 1)
x, q |
vector of quantiles. |
p |
vector of probabilities. |
n |
number of observations. If |
min, max |
lower and upper limits of the distribution. Must be finite. |
log, log.p |
logical; if TRUE, probabilities p are given as log(p). |
lower.tail |
logical; if TRUE (default), probabilities are P[X ≤ x], otherwise, P[X > x]. |
If min or max are not specified they assume the default
values of 0 and 1 respectively.
The uniform distribution has density
f(x) = 1/(max-min)
for min ≤ x ≤ max.
For the case of u := min == max, the limit case of
X == u is assumed, although there is no density in
that case and dunif will return NaN (the error condition).
runif will not generate either of the extreme values unless
max = min or max-min is small compared to min,
and in particular not for the default arguments.
dunif gives the density,
punif gives the distribution function,
qunif gives the quantile function, and
runif generates random deviates.
The length of the result is determined by n for
runif, and is the maximum of the lengths of the
numerical arguments for the other functions.
The numerical arguments other than n are recycled to the
length of the result. Only the first elements of the logical
arguments are used.
The characteristics of output from pseudo-random number generators
(such as precision and periodicity) vary widely. See
.Random.seed for more information on R's random number
generation algorithms.
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.
RNG about random number generation in R.
Distributions for other standard distributions.
u <- runif(20) ## The following relations always hold : punif(u) == u dunif(u) == 1 var(runif(10000)) #- ~ = 1/12 = .08333
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