maxstable: Partition-composite likelihood for multivariate max-stable...
In HiDimMaxStable: Inference on High Dimensional Max-Stable Distributions

Description Usage Arguments See Also Examples

Computes the partition-composite likelihood for observations sampled from a multivariate max-stable distribution whose spectral random vector is Gaussian, Log-normal or has a clustered copula distribution.

1	maxstable.l.clusters(data,clusters=rep(1,dim(data)[1]),ln=FALSE,spatial=NULL,...)

`data`	a matrix representing the data. Each column corresponds to one observation.
`clusters`	a vector of integers that gives the partition that is used to compute the partition-composite likelihood. Blocks of the partition should be of size smaller or equal to 7 to avoid a too long computing time. `clusters=rep(1,dim(data)[1])` must be used to get the full likelihood.
`ln`	logical. If `TRUE` log-density is computed.
`spatial`	argument passed to the `mubz.` function (where `` stands for the `category` of the model).
`...`	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.

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

n.site<-5
xy<-matrix(runif(2 * n.site, 0, 0.5), ncol = 2)

param<-c(0.5,1.5)
n.obs<-2
library(SpatialExtremes)
data<-t(rmaxstab(n.obs, xy, "whitmat",
                 nugget = 0, range = param[1], smooth = param[2]))

d<-maxstable.l.clusters(data,clusters=c(1,1,1,2,2),
                        params=param,
                        category="normal",
                        spatial=list(sites=xy,family=spatialWhittleMatern))