rlseries: Log-Series Distribution

In divent: Entropy Partitioning to Measure Diversity

Log-Series Distribution

Description

Random generation for the log-series distribution.

Usage

rlseries(n, size, fisher_alpha, show_progress = TRUE, check_arguments = TRUE)

Arguments

n	the number of observations.
size	The size of the distribution.
fisher_alpha	Fisher's \alpha in the log-series distribution.
show_progress	if TRUE, a progress bar is shown during long computations.
check_arguments	if TRUE, the function arguments are verified. Should be set to FALSE to save time when the arguments have been checked elsewhere.

Details

Fast implementation of the random generation of a log-series distribution \insertCiteFisher1943divent.

The complete set of functions (including density, distribution function and quantiles) can be found in package sads but this implementation of the random generation is much faster.

If size is too large, i.e. size + 1 can't be distinguished from size due to rounding, then an error is raised.

Value

A numeric vector with the random values drawn from the log-series distribution.

References

\insertAllCited

Examples

# Generate a community made of 10000 individuals with alpha=40
size <- 1E4
fisher_alpha <- 40
species_number <- fisher_alpha * log(1 + size / fisher_alpha)
abundances <- rlseries(species_number, size = 1E5, fisher_alpha = 40)
# rcommunity() may be a better choice here
autoplot(rcommunity(1, size = 1E4, fisher_alpha = 40, distribution = "lseries"))

divent documentation built on April 3, 2025, 7:40 p.m.