R: Fits univariate extreme value distributions to data

margin fits {SpatialExtremes} | R Documentation

## Fits univariate extreme value distributions to data

### Description

These functions fit the generalised extreme value and generalised Pareto distribution to data using maximum likelihood.

### Usage

    gevmle(x, ..., method = "Nelder")
    gpdmle(x, threshold, ..., method = "Nelder")

### Arguments

`x` | Numeric vector of observations
`...` | Optional arguments to be passed to the `optim` function.
`threshold` | Numeric. The threshold value.
`method` | The numerical optimisation method to be used.

### Details

These two functions are “extremely light” functions to fit the GEV/GPD. These functions are mainly useful to compute starting values for the Schlather and Smith model - see `fitmaxstab`.

If more refined (univariate) analysis have to be performed, users should use more specialised packages - e.g. POT, evd, ismev, ....

### Value

A vector for the estimated parameters of the GEV/GPD.

### Author(s)

Mathieu Ribatet

### Examples

    ## 1 - GEV fit
    x <- rep(NA, 100)
    for (i in 1:100)
      x[i] <- max(rnorm(365))

    gevmle(x)

    ## 2- GPD fit
    x <- rnorm(10000)
    ##we need to fix a threshold
    u <- quantile(x, 0.99)
    gpdmle(x, u)

[Package SpatialExtremes version 2.0-5 Index]