uvmle: Maximum-likelihood Fitting of Univariate Extreme Value Distributions

Description

Maximum-likelihood estimates for the Frechet, Gumbel, reversed Weibull and generalized extreme value distributions, allowing parameters to be held fixed if desired.

Usage

ffrechet(x, start, ...)
fgumbel(x, start, ...) 
frweibull(x, start, ...) 
fgev(x, start, ...)

Arguments

a numeric vector.

start

a named list giving the parameters to be optimized with initial values.

...

additional parameters, either for the distribution or for optim. If parameters of the distribution are included they will be held fixed. If parameters of the distribution are not included either here or as a named component in

Value

A list with with components
estimatea vector containing the maximum likelihood estimates.
std.erra vector containing the standard errors.
deviancethe deviance at the maximum likelihood estimates.
countsa two-element integer vector giving the number of calls to the log-likelihood function and the number gradient approximations respectively.

Details

Maximization of the log-likelihood is performed. The estimated standard errors are taken from the observed information, calculated by a numerical approximation.

Examples

Run this code

data <- rgev(1000, loc = 0.13, scale = 1.1, shape = 0.2)
fgev(data, start = list(loc=0,scale=1,shape=0), method = "BFGS", 
control = list(trace = 1))
fgev(data, start = list(loc=0,scale=1), shape = 0, method = "BFGS")
fgev(data, start = list(loc=0), scale = 1, shape = 0)