QRM (version 0.4-31)

GEV: Generalized Extreme Value Distribution

Description

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

Usage

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

# NOT RUN {
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) 
# }