GEV: Generalized Extreme Value Distribution

Description

Density, quantiles, cumulative probability, and fitting of the Generalized Extreme Value distribution.

Usage

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pGEV(q, xi, mu = 0, sigma = 1) 
qGEV(p, xi, mu = 0, sigma = 1) 
dGEV(x, xi, mu = 0, sigma = 1, log = FALSE) 
rGEV(n, xi, mu = 0, sigma = 1)
fit.GEV(maxima, ...)

Arguments

log

logical, whether log values of density should be returned, default is FALSE.

maxima

vector, block maxima data

mu

numeric, location parameter.

n

integer, count of random variates.

p

vector, probabilities.

q

vector, quantiles.

sigma

numeric, scale parameter.

x

vector, values to evaluate density.

xi

numeric, shape parameter.

...

ellipsis, arguments are passed down to optim().

Value

numeric, probability (pGEV), quantile (qGEV), density (dGEV) or random variates (rGEV) for the GEV distribution with shape parameter xi, location parameter mu and scale parameter sigma. A list object in case of fit.GEV().

See Also

GPD

Examples

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quantValue <- 4.5
pGEV(q = quantValue, xi = 0, mu = 1.0, sigma = 2.5) 
pGumbel(q = quantValue, mu = 1.0, sigma = 2.5)
## Fitting to monthly block-maxima
data(nasdaq)
l <- -returns(nasdaq)
em <- timeLastDayInMonth(time(l))
monmax <- aggregate(l, by = em, FUN = max) 
mod1 <- fit.GEV(monmax) 

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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