srexp | R Documentation |
The srexp()
function generates random samples from a Exponential Distribution using the STORS algorithm.
It employs an optimized proposal distribution around the mode and Inverse Transform (IT) method for the tails.
srexp(n = 1, rate = 1, x = NULL)
srexp_custom(n = 1, x = NULL)
n |
Integer, length 1. Number of samples to draw. |
rate |
Numeric. is the rate parameter of the Exponential Distribution. |
x |
(optional) Numeric vector of length |
The Exponential distribution has the probability density function (PDF):
f(x | \lambda) = \lambda \exp(-\lambda x), \quad x \geq 0,
where:
\lambda
is the rate parameter (\lambda > 0
), which determines the rate of decay of the distribution.
The Exponential distribution is commonly used to model the time between independent events that occur at a constant average rate.
These two functions are for sampling using the STORS algorithm based on the proposal that has been constructed using srexp_optimize
.
By default, srexp()
samples from a standard Exponential Distribution rate = 1
.
The proposal distribution is pre-optimized at package load time using srexp_optimize()
with
steps = 4091
, creating a scalable proposal centred around the mode.
If srexp()
is called with custom rate
parameter, the samples are generated
from the standard Exponential Distribution, then scaled accordingly.
A numeric vector of length n
containing samples from the Exponential Distribution with the specified
rate
.
NOTE: When the x
parameter is specified, it is updated in-place with the simulation for performance reasons.
srexp_optimize
to optimize the custom or the scaled proposal.
# Generate 10 samples from the standard Exponential Distribution
samples <- srexp(10)
print(samples)
# Generate 10 samples using a pre-allocated vector
x <- numeric(10)
srexp(10, x = x)
print(x)
# Generate 10 samples from a Exponential Distribution with rate = 4
samples <- srexp(10, rate = 4)
print(samples)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.