logseries: The Logarithmic Series Distribution

In vcdExtra: 'vcd' Extensions and Additions

The Logarithmic Series Distribution

Description

The logarithmic series distribution is a long-tailed distribution introduced by Fisher etal. (1943) in connection with data on the abundance of individuals classified by species.

Usage

dlogseries(x, prob = 0.5, log = FALSE)

plogseries(q, prob = 0.5, lower.tail = TRUE, log.p = FALSE)

qlogseries(p, prob = 0.5, lower.tail = TRUE, log.p = FALSE, max.value = 10000)

rlogseries(n, prob = 0.5)

Arguments

x, q	vector of quantiles representing the number of events.
prob	parameter for the distribution, ⁠0 < prob < 1⁠
log, log.p	logical; if TRUE, probabilities p are given as log(p)
lower.tail	logical; if TRUE (default), probabilities are P[X \le x], otherwise, P[X > x].
p	vector of probabilities
max.value	maximum value returned by qlogseries
n	number of observations for rlogseries

Details

These functions provide the density, distribution function, quantile function and random generation for the logarithmic series distribution with parameter prob.

The logarithmic series distribution with prob = p has density

p ( x ) = \alpha p^x / x

for x = 1, 2, \dots, where \alpha= -1 / \log(1 - p) and 0 < p < 1. % Note that counts x==2 cannot occur.

Value

dlogseries gives the density, plogseries gives the cumulative distribution function, qlogseries gives the quantile function, and rlogseries generates random deviates.

Author(s)

Michael Friendly, using original code modified from the gmlss.dist package by Mikis Stasinopoulos.

References

https://en.wikipedia.org/wiki/Logarithmic_distribution

Fisher, R. A. and Corbet, A. S. and Williams, C. B. (1943). The relation between the number of species and the number of individuals Journal of Animal Ecology, 12, 42-58.

See Also

Distributions

Examples

XL <-expand.grid(x=1:5, p=c(0.33, 0.66, 0.99))
lgs.df <- data.frame(XL, prob=dlogseries(XL[,"x"], XL[,"p"]))
lgs.df$p = factor(lgs.df$p)
str(lgs.df)

require(lattice)
mycol <- palette()[2:4]
xyplot( prob ~ x, data=lgs.df, groups=p,
	xlab=list('Number of events (k)', cex=1.25),
	ylab=list('Probability',  cex=1.25),
	type='b', pch=15:17, lwd=2, cex=1.25, col=mycol,
	key = list(
					title = 'p',
					points = list(pch=15:17, col=mycol, cex=1.25),
					lines = list(lwd=2, col=mycol),
					text = list(levels(lgs.df$p)),
					x=0.9, y=0.98, corner=c(x=1, y=1)
					)
	)


# random numbers
hist(rlogseries(200, prob=.4), xlab='x')
hist(rlogseries(200, prob=.8), xlab='x')

vcdExtra documentation built on Dec. 11, 2025, 9:06 a.m.