halton_dists: Generate pseudo-random draws from specified distributions...

In flexCountReg: Estimation of a Variety of Count Regression Models

Generate pseudo-random draws from specified distributions using Halton draws

Description

Generate pseudo-random draws from specified distributions using Halton draws

Usage

halton_dists(dist, mean, sdev, hdraw = NULL, ndraws = 500)

Arguments

dist	The distribution type to use. The distribution options include normal ("n"), lognormal ("ln"), triangular ("t"), uniform ("u"), and gamma ("g").
mean	The mean value for the random draws.
sdev	The standard deviation value for the random draws.
hdraw	An optional vector of Halton draws to convert to the specified distribution. If not provided, the function will generate Halton draws.
ndraws	The number of random draws to generate. This is only used if 'hdraw' is not provided.

Details

This function is used to convert Halton draws to the specified distribution. The function can be used to generate random draws for use in random parameter models, generating Halton-based pseudo-random draws for specified distributions, etc.

The distributions generated all use the 'mean' (

\mu

) and 'sdev' (

\sigma

) parameters to generate the random draws. The density functions for the distributions are as follows: The Normal distribution is: f(x) = \frac{1}{\sqrt{2\pi\sigma^2}} \exp\left(-\frac{(x - \mu)^2}{2\sigma^2}\right)

The Lognormal distribution is: f(x) = \frac{1}{x\sigma\sqrt{2\pi}} \exp\left(-\frac{(\log(x) - \mu)^2}{2\sigma^2}\right)

The Triangular distribution is (note that this is a symmetrical triangular distribution where

\mu

is the median and

\sigma

is the half-width): f(x) = \begin{cases} \frac{(x - \mu + \sigma)}{\sigma^2}, & \text{for } \mu - \sigma \leq x \leq \mu \\ \frac{(\mu + \sigma - x)}{\sigma^2}, & \text{for } \mu < x \leq \mu + \sigma \\0, & \text{otherwise}\end{cases}

The Uniform distribution is (note that \mu is the midpoint and \sigma is the half-width): f(x) = \frac{1}{(\beta_{\mu}+\beta_{\sigma}) - (\beta_{\mu}-\beta_{\sigma})}=\frac{1}{2\beta_{\sigma}}

The Gamma distribution is based on

\mu = \frac{\alpha}{\beta}

and

\sigma^2 = \frac{\alpha}{\beta^2}

: f(x) = \frac{\left(\frac{\mu}{\sigma^2}\right)^ {\frac{\mu^2}{\sigma^2}}}{\Gamma\left(\frac{\mu^2}{\sigma^2}\right)} x^{\frac{\mu^2}{\sigma^2} - 1} e^{-\frac{\mu}{\sigma^2} x}

Value

A vector of psudo-random draws from the specified distribution, based on Halton draws.

Examples

# Generate 500 random draws from a normal distribution 
halton_dists(dist="n", mean=3, sdev=2, ndraws=500)

# Generate 500 random draws from a lognormal distribution 
halton_dists(dist="ln", mean=2, sdev=1.5, ndraws=500)

# Generate 500 random draws from a triangular distribution
halton_dists(dist="t", mean=1, sdev=0.5, ndraws=500)

# Generate 500 random draws from a uniform distribution
halton_dists(dist="u", mean=8, sdev=3, ndraws=500)

# Generate 500 random draws from a gamma distribution
halton_dists(dist="g", mean=0.5, sdev=1.5, ndraws=500)

flexCountReg documentation built on Jan. 20, 2026, 1:06 a.m.