hkevp.rand {hkevp} | R Documentation |
Simulation of the HKEVP
Description
Simulation procedure of the HKEVP with given sites and knots positions and marginal and spatial dependence parameters.
Usage
hkevp.rand(nrep, sites, knots, loc, scale, shape, alpha, tau)
Arguments
nrep |
A positive integer. Number of realisations of the block mashapema process. |
sites |
The coordinates of the sites where the data are observed. Each row corresponds to a site position. |
knots |
The coordinates of the knots in the HKEVP. By default, the positions of the knots coincide with the positions of the sites. |
loc |
A numerical value or a vector of real values for the GEV location parameter. If a vector, its length must coincide with the number of sites. The value by default is 1. |
scale |
A numerical value or a vector of real values for the GEV scale parameter. If a vector, its length must coincide with the number of sites. The value by default is 1. |
shape |
A numerical value or a vector of real values for the GEV shape parameter. If a vector, its length must coincide with the number of sites. The value by default is 1. |
alpha |
The dependence parameter |
tau |
The bandwidth parameter |
Details
Simulating one realisation of the block mashapema process Y(\cdot)
from the HKEVP involves three steps:
The nugget process
U(\cdot)
is generated independently at each position, by simulating a random variable withGEV(1,\alpha,\alpha)
distribution.The residual dependence process
\theta(\cdot)
is computed by using the kernel functions centered at the set of knots, the bandwidth parameter\tau
and the simulations of the positive stablePS(\alpha)
random effectA
.The process
Z = U\theta
is computed and its margins are transformed to the general GEV distribution with\mu(s),\sigma(s)
and\xi(s)
parameters.
Value
A numerical matrix of real values. Each column corresponds to a position and each row to a realisation of the process.
Author(s)
Quentin Sebille
Examples
# Simulation of HKEVP:
sites <- as.matrix(expand.grid(1:3,1:3))
loc <- sites[,1]*10
scale <- 3
shape <- 0
alpha <- .4
tau <- 1
ysim <- hkevp.rand(10, sites, sites, loc, scale, shape, alpha, tau)