tsal-cens: The Tsallis Distribution with a censoring parameter...

In tsallisqexp: Tsallis q-Exp Distribution

Description

Density function, distribution function, quantile function, random generation.

Usage

dtsal.tail(x, shape=1,scale=1, q=tsal.q.from.shape(shape),
kappa=tsal.kappa.from.ss(shape,scale), xmin=0,
log=FALSE)

ptsal.tail(x, shape=1, scale=1, q=tsal.q.from.shape(shape),
kappa=tsal.kappa.from.ss(shape,scale), xmin=0,
lower.tail=TRUE, log.p=FALSE)

qtsal.tail(p,  shape=1, scale=1, q=tsal.q.from.shape(shape),
kappa=tsal.kappa.from.ss(shape,scale), xmin=0,
lower.tail=TRUE, log.p=FALSE)

rtsal.tail(n, shape=1, scale=1, q=tsal.q.from.shape(shape),
kappa=tsal.kappa.from.ss(shape,scale), xmin=0)

Arguments

x	vector of quantiles.
q	vector of quantiles or a shape parameter.
p	vector of probabilities.
n	number of observations. If length(n) > 1, the length is taken to be the number required.
shape	shape parameter.
scale, kappa	scale parameters.
xmin	minimum x-value.
log, log.p	logical; if TRUE, probabilities p are given as log(p).
lower.tail	logical; if TRUE (default), probabilities are P[X ≤ x], otherwise, P[X > x].

Details

The Tsallis distribution with a censoring parameter is the distribution of a Tsallis distributed random variable conditionnaly on x>xmin. The density is defined as

f(x) = C/κ(1-(1-q)x/κ)^{1/(1-q)}

for all x>xmin where C is the appropriate constant so that the integral of the density equals 1. That is C is the survival probability of the classic Tsallis distribution at x=xmin. It is convenient to introduce a re-parameterization shape = -1/(1-q), scale = shape*κ which makes the relationship to the Pareto clearer, and eases estimation. If we have both shape/scale and q/kappa parameters, the latter over-ride.

Value

dtsal.tail gives the density, ptsal.tail gives the distribution function, qtsal.tail gives the quantile function, and rtsal.tail generates random deviates.

The length of the result is determined by n for rtsal.tail, and is the maximum of the lengths of the numerical parameters for the other functions.

Author(s)

Cosma Shalizi (original R code), Christophe Dutang (R packaging)

References

Maximum Likelihood Estimation for q-Exponential (Tsallis) Distributions, http://bactra.org/research/tsallis-MLE/ and https://arxiv.org/abs/math/0701854.

Examples

#####
# (1) density function
x <- seq(0, 5, length=24)

cbind(x, dtsal(x, 1/2, 1/4))

#####
# (2) distribution function

cbind(x, ptsal(x, 1/2, 1/4))




		

tsallisqexp documentation built on Feb. 10, 2021, 9:06 a.m.