The algorithm performs forward sampling by simulating first from a
mixture, then sample angles conditional on them being less than (max) or greater than (min) one.
The resulting sample from the angular distribution is then multiplied by
Pareto variates with tail index shape
.
Arguments
- n
sample size.
- model
string indicating the model family.
- risk
string indicating the risk functional. Only
max
andmin
are currently supported.- param
parameter value for the logistic or negative logistic model
- d
dimension of the multivariate model, only needed for logistic or negative logistic models
- Lambda
parameter matrix for the Brown–Resnick model. See Details.
- Sigma
correlation matrix if
model = 'xstud'
, otherwise the covariance matrix formed from the stationary Brown-Resnick process.- df
degrees of freedom for extremal Student process.
- shape
tail index of the Pareto variates (reciprocal shape parameter). Must be strictly positive.
- ...
additional parameters, currently ignored
Details
For the moment, only exchangeable models and models based n elliptical processes are handled.
The parametrization of the Brown–Resnick is in terms of the matrix Lambda
, which
is formed by evaluating the semivariogram \(\gamma\) at sites \(s_i, s_j\), meaning that
\(\Lambda_{i,j} = \gamma(s_i, s_j)/2\).
The argument Sigma
is ignored for the Brown-Resnick model
if Lambda
is provided by the user.
See also
rparp
for general simulation of Pareto processes based on an accept-reject algorithm.
Examples
if (FALSE) { # \dontrun{
#Brown-Resnick, Wadsworth and Tawn (2014) parametrization
D <- 20L
coord <- cbind(runif(D), runif(D))
semivario <- function(d, alpha = 1.5, lambda = 1){0.5 * (d/lambda)^alpha}
Lambda <- semivario(as.matrix(dist(coord))) / 2
rparpcs(n = 10, Lambda = Lambda, model = 'br', shape = 0.1)
#Extremal Student
Sigma <- stats::rWishart(n = 1, df = 20, Sigma = diag(10))[,,1]
rparpcs(n = 10, Sigma = cov2cor(Sigma), df = 3, model = 'xstud')
} # }