HiDimMaxStable (version 0.1.1)

mubz.lnormal: $mu(B,z)$ for the Log-normal model

Description

Computes $mu(B,z)$ for the Log-normal model.

Usage

mubz.lnormal(b,z,params=NULL,spatial=NULL, cov.matrix=spatial.cor.matrix(params,spatial), details=FALSE)

Arguments

b

a vector of TRUE or FALSE, of length d where d=length(z), TRUE indicating the coordinates of $B$

z

a vector of positive constants

params

a vector of length 2 if spatial=spatialPower (Brown-Resnick max-stable process): the first component is for the range parameter, the second component is for the smoothness parameter.

a vector of length 3 for the other spatial models (Geometric Gaussian max-stable process): the first component is for the range parameter, the second component is for the smoothness parameter and the third component is for the scale parameter

spatial

the correlation model given as a list:

spatial$sites is a matrix that gives the coordinates of each location. Each row corresponds to one location.

spatial$family is a object from the spatial class that gives the spatial model. This must be one of the following family:

- Brown-Resnick max-stable process: spatialPower

- Geometric Gaussian max-stable process: spatialWhittleMatern for the Whittle Matern correlation model, spatialCauchy for the Cauchy correlation model, spatialPowerExp for the Power exponential model, spatialBessel for the Bessel correlation model

cov.matrix

a covariance matrix if spatial=NULL

details

get more details in the return value?

Details

mubz.lnormal uses pmnorm to compute the value of $mu(B,z)$. If the dimension of z is too large (cannot exceed 20), the computation may fail.

See Also

mubz.normal, mubz.copula

Examples

# In this example, we compute mu(B,z) for Brown Resnick spatial model
# from 10 sites uniformly distributed on the square [0,2]x[0,2]
mubz.lnormal(b=c(TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,TRUE),
    z=rep(1,10),params=c(1,1),
    spatial=list(sites=matrix(2*runif(20),ncol=2),family=spatialPower))