simEd (version 2.0.0)

vlogis: Variate Generation for Logistic Distribution

Description

Variate Generation for Logistic Distribution

Usage

vlogis(
  n,
  location = 0,
  scale = 1,
  stream = NULL,
  antithetic = FALSE,
  asList = FALSE
)

Arguments

n

number of observations

location

Location parameter

scale

Scale parameter (default 1)

stream

if NULL (default), uses stats::runif to generate uniform variates to invert via stats::qlogis; otherwise, an integer in 1:25 indicates the rstream stream from which to generate uniform variates to invert via stats::qlogis;

antithetic

if FALSE (default), inverts \(u\) = uniform(0,1) variate(s) generated via either stats::runif or rstream::rstream.sample; otherwise, uses \(1 - u\)

asList

if FALSE (default), output only the generated random variates; otherwise, return a list with components suitable for visualizing inversion. See return for details

Value

If asList is FALSE (default), return a vector of random variates.

Otherwise, return a list with components suitable for visualizing inversion, specifically:

u

A vector of generated U(0,1) variates

x

A vector of logistic random variates

quantile

Parameterized quantile function

text

Parameterized title of distribution

Details

Generates random variates from the logistic distribution.

Logistic variates are generated by inverting uniform(0,1) variates produced either by stats::runif (if stream is NULL) or by rstream::rstream.sample (if stream is not NULL). In either case, stats::qlogis is used to invert the uniform(0,1) variate(s). In this way, using vlogis provides a monotone and synchronized binomial variate generator, although not particularly fast.

The stream indicated must be an integer between 1 and 25 inclusive.

The logistic distribution with location \(= \mu\) and scale \(= \sigma\) has distribution function

$$F(x) = \frac{1}{1 + e^{-(x - \mu) / \sigma}}$$

and density

$$f(x) = \frac{1}{\sigma} \frac{e^{(x-\mu)/\sigma}} {(1 + e^{(x-\mu)/\sigma})^2}$$

It is a long-tailed distribution with mean \(\mu\) and variance \(\pi^2 / 3 \sigma^2\).

See Also

rstream, set.seed, stats::runif

stats::rlogis

Examples

# NOT RUN {
 set.seed(8675309)
 # NOTE: following inverts rstream::rstream.sample using stats::qlogis
 vlogis(3, location = 5, scale = 0.5)

 set.seed(8675309)
 # NOTE: following inverts rstream::rstream.sample using stats::qlogis
 vlogis(3, 5, 1.5, stream = 1)
 vlogis(3, 5, 1.5, stream = 2)

 set.seed(8675309)
 # NOTE: following inverts rstream::rstream.sample using stats::qlogis
 vlogis(1, 5, 1.5, stream = 1)
 vlogis(1, 5, 1.5, stream = 2)
 vlogis(1, 5, 1.5, stream = 1)
 vlogis(1, 5, 1.5, stream = 2)
 vlogis(1, 5, 1.5, stream = 1)
 vlogis(1, 5, 1.5, stream = 2)

 set.seed(8675309)
 variates <- vlogis(1000, 5, 1.5, stream = 1)
 set.seed(8675309)
 variates <- vlogis(1000, 5, 1.5, stream = 1, antithetic = TRUE)

# }