rmstable: Sampling of a multivariate max-stable distribution

Description Usage Arguments Details Value References Examples

View source: R/simulation_functions.R

Description

Simulates exact samples of a multivariate max-stable distribution.

Usage

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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.

References

\insertAllCited

Examples

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## 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.