# Exponential

0th

Percentile

##### The Exponential Distribution

Density, distribution function, quantile function and random generation for the exponential distribution with rate rate (i.e., mean 1/rate).

Keywords
distribution
##### Usage
dexp(x, rate = 1, log = FALSE)
pexp(q, rate = 1, lower.tail = TRUE, log.p = FALSE)
qexp(p, rate = 1, lower.tail = TRUE, log.p = FALSE)
rexp(n, rate = 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.

rate

vector of rates.

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 rate is not specified, it assumes the default value of 1.

The exponential distribution with rate $\lambda$ has density $$f(x) = \lambda {e}^{- \lambda x}$$ for $x \ge 0$.

##### Value

dexp gives the density, pexp gives the distribution function, qexp gives the quantile function, and rexp generates random deviates.

The length of the result is determined by n for rexp, 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 cumulative hazard $H(t) = - \log(1 - F(t))$ is -pexp(t, r, lower = FALSE, log = TRUE).

##### References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995) Continuous Univariate Distributions, volume 1, chapter 19. Wiley, New York.

exp for the exponential function.

Distributions for other standard distributions, including dgamma for the gamma distribution and dweibull for the Weibull distribution, both of which generalize the exponential.

• Exponential
• dexp
• pexp
• qexp
• rexp
##### Examples
library(stats) # NOT RUN { dexp(1) - exp(-1) #-> 0 ## a fast way to generate *sorted* U[0,1] random numbers: rsunif <- function(n) { n1 <- n+1 cE <- cumsum(rexp(n1)); cE[seq_len(n)]/cE[n1] } plot(rsunif(1000), ylim=0:1, pch=".") abline(0,1/(1000+1), col=adjustcolor(1, 0.5)) # } 
Documentation reproduced from package stats, version 3.5.0, License: Part of R 3.5.0

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