# Uniform

0th

Percentile

##### The Uniform Distribution

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.

Keywords
distribution
##### Usage
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)
##### Arguments
x, q

vector of quantiles.

p

vector of probabilities.

n

number of observations. If length(n) > 1, the length is taken to be the number required.

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 $P[X \le x]$, otherwise, $P[X > x]$.

##### Details

If min or max are not specified they assume the default values of 0 and 1 respectively.

The uniform distribution has density $$f(x) = \frac{1}{max-min}$$ for $min \le x \le max$.

For the case of $u := min == max$, the limit case of $X \equiv u$ 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.

##### Value

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.

##### Note

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.

##### References

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.

• Uniform
• dunif
• punif
• qunif
• runif
##### Examples
library(stats) # NOT RUN { u <- runif(20) ## The following relations always hold : punif(u) == u dunif(u) == 1 var(runif(10000)) #- ~ = 1/12 = .08333 # } 
Documentation reproduced from package stats, version 3.6.2, License: Part of R 3.6.2

### Community examples

richie@datacamp.com at Jan 17, 2017 stats v3.3.1

Generate 20 random numbers from a uniform distribution between 0 and 1. {r} runif(20)  As above, but with a uniform distribution from -5 to 5. {r} runif(20, -5, 5)  The probability density function (PDF), cumulative density function (CDF), and inverse CDF. {r} lo <- -5 hi <- 5 x <- seq(lo - 1, hi + 1, 0.01) pdf_of_x <- dunif(x, lo, hi) cdf_of_x <- punif(x, lo, hi) probabilities <- seq(0, 1, 0.01) inverse_cdf_of_x <- qunif(probabilities, lo, hi) layout(matrix(1:3)) plot(x, pdf_of_x, type = "l", main = "PDF of x") plot(x, cdf_of_x, type = "l", main = "CDF of x") plot(probabilities, inverse_cdf_of_x, type = "l", , main = "Inverse CDF of x")  If you want log probabilities, the log/log.p argument is faster than calling the log function afterwards {r} lo <- -5 hi <- 5 x <- seq(lo - 1, hi + 1, 0.01) microbenchmark::microbenchmark( log(punif(x, lo, hi)), punif(x, lo, hi, log.p = TRUE) # same but faster )