Exponential {distributions3} | R Documentation |
Create an Exponential distribution
Description
Exponential distributions are frequently used for modeling the amount of time that passes until a specific event occurs. For example, exponential distributions could be used to model the time between two earthquakes, the amount of delay between internet packets, or the amount of time a piece of machinery can run before needing repair.
Usage
Exponential(rate = 1)
Arguments
rate |
The rate parameter, written |
Details
We recommend reading this documentation on https://alexpghayes.github.io/distributions3/, where the math will render with additional detail and much greater clarity.
In the following, let be an Exponential random variable with
rate parameter
rate
= .
Support: x in [0, )
Mean: 1 /
Variance: 1 /
Probability density function (p.d.f):
Cumulative distribution function (c.d.f):
Moment generating function (m.g.f):
Value
An Exponential
object.
See Also
Other continuous distributions:
Beta()
,
Cauchy()
,
ChiSquare()
,
Erlang()
,
FisherF()
,
Frechet()
,
GEV()
,
GP()
,
Gamma()
,
Gumbel()
,
LogNormal()
,
Logistic()
,
Normal()
,
RevWeibull()
,
StudentsT()
,
Tukey()
,
Uniform()
,
Weibull()
Examples
set.seed(27)
X <- Exponential(5)
X
mean(X)
variance(X)
skewness(X)
kurtosis(X)
random(X, 10)
pdf(X, 2)
log_pdf(X, 2)
cdf(X, 4)
quantile(X, 0.7)
cdf(X, quantile(X, 0.7))
quantile(X, cdf(X, 7))