# rmstable: Sampling of a multivariate max-stable distribution In graphicalExtremes: Statistical Methodology for Graphical Extreme Value Models

## Description

Simulates exact samples of a multivariate max-stable distribution.

## Usage

 ```1 2``` ```rmstable(n, model = c("HR", "logistic", "neglogistic", "dirichlet")[1], d, par) ```

## Arguments

 `n` Number of simulations. `model` The parametric model type; one of: `HR` (default), `logistic`, `neglogistic`, `dirichlet`. `d` Dimension of the multivariate Pareto distribution. `par` Respective parameter for the given `model`, that is, Γ, numeric d x d variogram matrix, if `model = HR`. 0 < θ < 1, if `model = logistic`. θ > 0, if `model = neglogistic`. α, numeric vector of size `d` with positive entries, if `model = dirichlet`.

## Details

The simulation follows the extremal function algorithm in \insertCitedom2016;textualgraphicalExtremes. For details on the parameters of the Huesler–Reiss, logistic and negative logistic distributions see \insertCitedom2016;textualgraphicalExtremes, and for the Dirichlet distribution see \insertCitecoles1991modelling;textualgraphicalExtremes.

## Value

Numeric matrix of size n x d of simulations of the multivariate max-stable distribution.

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## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27``` ```## A 4-dimensional HR distribution n <- 10 d <- 4 G <- cbind(c(0, 1.5, 1.5, 2), c(1.5, 0, 2, 1.5), c(1.5, 2, 0, 1.5), c(2, 1.5, 1.5, 0)) rmstable(n, "HR", d = d, par = G) ## A 3-dimensional logistic distribution n <- 10 d <- 3 theta <- .6 rmstable(n, "logistic", d, par = theta) ## A 5-dimensional negative logistic distribution n <- 10 d <- 5 theta <- 1.5 rmstable(n, "neglogistic", d, par = theta) ## A 4-dimensional Dirichlet distribution n <- 10 d <- 4 alpha <- c(.8, 1, .5, 2) rmstable(n, "dirichlet", d, par = alpha) ```

graphicalExtremes documentation built on Nov. 8, 2019, 5:07 p.m.