GenPareto: The Generalized Pareto Distribution

In hurdlr: Zero-Inflated and Hurdle Modelling Using Bayesian Inference

Description

Density, distribution function, quantile function and random generation for the Generalized Pareto distribution with parameters mu, sigma, and xi.

Usage

dgpd(x, mu = 0, sigma = 1, xi = 1, log = F)

mgpd(x, mu = 0, sigma = 1, xi = 1, log = F)

pgpd(q, mu = 0, sigma = 1, xi = 1, lower.tail = T)

qgpd(p, mu = 0, sigma = 1, xi = 1, lower.tail = T)

rgpd(n, mu = 0, sigma = 1, xi = 1)

Arguments

x, q	vector of quantiles.
mu	location parameter.
sigma	(non-negative) scale parameter.
xi	shape parameter.
log	logical; if TRUE, probabilities p are given as log(p).
lower.tail	logical; if TRUE, probabilities are P[X ≤ x], otherwise, P[X > x].
p	numeric predictor matrix.
n	number of random values to return.

Details

The generalized pareto distribution has density

f(x) = sigma^(1/xi) / (sigma + xi*(x-mu))^(1/xi + 1)

Value

dgpd gives the continuous density, pgpd gives the distribution function, qgpd gives the quantile function, and rgpd generates random deviates.

mgpd gives a probability mass function for a discretized version of GPD.

Author(s)

Taylor Trippe <[e-mail]>
Earvin Balderama <[e-mail]>

Examples

dexp(1,rate=.5) #Exp(rate) equivalent to gpd with mu=0 AND xi=0, and sigma=1/rate.
dgpd(1,mu=0,sigma=2,xi=0) #cannot take xi=0.
dgpd(1,mu=0,sigma=2,xi=0.0000001) #but can get close.

##"mass" function of GPD
mgpd(8) == pgpd(8.5) - pgpd(7.5)

Example output

[1] 0.3032653
[1] NaN
[1] 0.3032653
[1] TRUE

hurdlr documentation built on May 2, 2019, 3:19 p.m.