copula (version 0.999-15)

rlog: Sampling Logarithmic Distributions

Description

Generating random variates from a Log(p) distribution with probability mass function $$p_k=\frac{p^k}{-\log(1-p)k},\ k\in\mathbf{N}, $$ where $p in (0,1)$. The implemented algorithm is the one named “LK” in Kemp (1981).

Usage

rlog(n, p, Ip = 1 - p)

Arguments

n

sample size, that is, length of the resulting vector of random variates.

p

parameter in $(0,1)$.

Ip

$= 1 - p$, possibly more accurate, e.g, when $p ~= 1$.

Value

A vector of positive integers of length n containing the generated random variates.

Details

For documentation and didactical purposes, rlogR is a pure-R implementation of rlog. However, rlogR is not as fast as rlog (the latter being implemented in C).

References

Kemp, A. W. (1981), Efficient Generation of Logarithmically Distributed Pseudo-Random Variables, Journal of the Royal Statistical Society: Series C (Applied Statistics) 30, 3, 249--253.

Examples

## Sample n random variates from a Log(p) distribution and plot a
## histogram
n <- 1000
p <- .5
X <- rlog(n, p)
hist(X, prob = TRUE)