gpdUC: The Generalized Pareto Distribution In VGAM: Vector Generalized Linear and Additive Models

 gpdUC R Documentation

The Generalized Pareto Distribution

Description

Density, distribution function, quantile function and random generation for the generalized Pareto distribution (GPD) with location parameter location, scale parameter scale and shape parameter shape.

Usage

dgpd(x, location = 0, scale = 1, shape = 0, log = FALSE,
tolshape0 = sqrt(.Machine\$double.eps))
pgpd(q, location = 0, scale = 1, shape = 0,
lower.tail = TRUE, log.p = FALSE)
qgpd(p, location = 0, scale = 1, shape = 0,
lower.tail = TRUE, log.p = FALSE)
rgpd(n, location = 0, scale = 1, shape = 0)


Arguments

 x, q vector of quantiles. p vector of probabilities. n number of observations. If length(n) > 1 then the length is taken to be the number required. location the location parameter \mu. scale the (positive) scale parameter \sigma. shape the shape parameter \xi. log Logical. If log = TRUE then the logarithm of the density is returned. lower.tail, log.p Same meaning as in punif or qunif. tolshape0 Positive numeric. Threshold/tolerance value for resting whether \xi is zero. If the absolute value of the estimate of \xi is less than this value then it will be assumed zero and an exponential distribution will be used.

Details

See gpd, the VGAM family function for estimating the two parameters by maximum likelihood estimation, for formulae and other details. Apart from n, all the above arguments may be vectors and are recyled to the appropriate length if necessary.

Value

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

Note

The default values of all three parameters, especially \xi = 0, means the default distribution is the exponential.

Currently, these functions have different argument names compared with those in the evd package.

Author(s)

T. W. Yee and Kai Huang

References

Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. London: Springer-Verlag.

gpd, Exponential.

Examples

## Not run:  loc <- 2; sigma <- 1; xi <- -0.4
x <- seq(loc - 0.2, loc + 3, by = 0.01)
plot(x, dgpd(x, loc, sigma, xi), type = "l", col = "blue",
main = "Blue is density, red is the CDF", ylim = c(0, 1),
sub = "Purple are 5,10,...,95 percentiles", ylab = "", las = 1)
abline(h = 0, col = "blue", lty = 2)
lines(qgpd(seq(0.05, 0.95, by = 0.05), loc, sigma, xi),
dgpd(qgpd(seq(0.05, 0.95, by = 0.05), loc, sigma, xi), loc, sigma, xi),
col = "purple", lty = 3, type = "h")
lines(x, pgpd(x, loc, sigma, xi), type = "l", col = "red")
abline(h = 0, lty = 2)

pgpd(qgpd(seq(0.05, 0.95, by = 0.05), loc, sigma, xi), loc, sigma, xi)

## End(Not run)


VGAM documentation built on Sept. 19, 2023, 9:06 a.m.