mev (version 1.11)

rparp: Simulation from R-Pareto processes

Description

Simulation from R-Pareto processes

Usage

rparp(n, shape = 1, riskf = c("sum", "site", "max"), siteindex = NULL, d,
  param, sigma, model = c("log", "neglog", "bilog", "negbilog", "hr", "br",
  "xstud", "smith", "schlather", "ct", "sdir", "dirmix"), weights, vario, loc,
  ...)

Arguments

n

number of observations

shape

shape index of Pareto variable

riskf

string indicating risk functional.

siteindex

integer between 1 and d specifying the index of the site or variable

d

dimension of sample

param

parameter vector for the logistic, bilogistic, negative bilogistic and extremal Dirichlet (Coles and Tawn) model. Parameter matrix for the Dirichlet mixture. Degree of freedoms for extremal student model. See Details.

sigma

covariance matrix for Brown-Resnick and extremal Student-t distributions. Symmetric matrix of squared coefficients \(\lambda^2\) for the Husler-Reiss model, with zero diagonal elements.

model

for multivariate extreme value distributions, users can choose between 1-parameter logistic and negative logistic, asymmetric logistic and negative logistic, bilogistic, Husler-Reiss, extremal Dirichlet model (Coles and Tawn) or the Dirichlet mixture. Spatial models include the Brown-Resnick, Smith, Schlather and extremal Student max-stable processes.

weights

vector of length m for the m mixture components. Must sum to one

vario

variogram function whose first argument must be distance. Used only if provided in conjonction with loc and if sigma is missing

loc

d by k matrix of location, used as input in the variogram vario or as parameter for the Smith model. If grid is TRUE, unique entries should be supplied.

...

additional arguments for the vario function

Details

For riskf=max and riskf=min, the procedure uses rejection sampling based on Pareto variates sampled from sum and may be slow if d is large.

Examples

# NOT RUN {
rparp(n=10, riskf = "site", siteindex=2, d=3, param=2.5, model="log")
rparp(n=10, riskf = "min", d=3, param=2.5, model="neglog")
rparp(n=10, riskf = "max", d=4, param=c(0.2,0.1,0.9,0.5), model="bilog")
rparp(n=10, riskf = "sum", d=3, param=c(0.8,1.2,0.6, -0.5), model="sdir")
vario <- function(x, scale=0.5, alpha=0.8){ scale*x^alpha }
grid.loc <- as.matrix(expand.grid(runif(4), runif(4)))
rparp(n=10, riskf = "max", vario=vario,loc=grid.loc, model="br")
# }