simultoccur.l: Likelihood for vectors of componentwise maxima with...

In HiDimMaxStable: Inference on High Dimensional Max-Stable Distributions

Description

Computes the likelihood for observations of vectors of componentwise maxima with additional information on maxima occurences. The data that are used to compute componentwise maxima must belong to the maximum domain of attraction of a multivariate max-stable distribution whose spectral random vector is Gaussian, Log-normal or has a clustered copula distribution.

Usage

simultoccur.l(data,occur,ln=FALSE,...)

Arguments

data	a matrix representing the data. Each column corresponds to one observation of a vector of componentwise maxima.
occur	a matrix representing the data. Each column corresponds to one observation of a vector that gives which componentwise maxima occur simultaneously.
ln	logical. If TRUE log-density is computed.
...	further arguments to be passed to mubz.* function (where * stands for the category of the model). In particular, category is a character string indicating the model to be used: "normal", "lnormal" or "copula", and params gives the values of the parameters for which the likelihood is computed.

See Also

mubz.normal,mubz.lnormal, mubz.copula.

Examples

raw.data<-rCMS(copulas=c(copClayton,copGumbel),
               margins=c(marginLnorm,marginFrechet),
               classes=c(rep(1,3),rep(2,3)),
               params=c(0.5,1,1.5,1.7),n=20)

data<-maxblocks(raw.data,n.blocks=3)

d<-simultoccur.l(data$normalized.max,occur=data$classes.max,
            params=c(0.5,1,1.5,1.7),
            category="copula",
            copulas=c(copClayton,copGumbel),
            margins=c(marginLnorm,marginFrechet),
            classes=c(rep(1,3),rep(2,3)))

HiDimMaxStable documentation built on May 29, 2017, 6:20 p.m.