concprob: Pairwise empirical and extremal concurrence probabilities

In SpatialExtremes: Modelling Spatial Extremes

Pairwise empirical and extremal concurrence probabilities

Description

This function computes the pairwise empirical or the pairwise extremal concurrence probability estimates.

Usage

concprob(data, coord, fitted, n.bins, add = FALSE, xlim = c(0,
max(dist)), ylim = c(min(0, concProb), max(1, concProb)), col = 1:2,
which = "kendall", xlab, ylab, block.size = floor(nrow(data)^(1/3)),
plot = TRUE, compute.std.err = FALSE, ...)

Arguments

data	A matrix representing the data. Each column corresponds to one location.
coord	A matrix that gives the coordinates of each location. Each row corresponds to one location.
fitted	An object of class maxstab - usually the output of the fitmaxstab function. May be missing.
n.bins	The number of bins to be used. If missing, pairwise F-madogram estimates will be computed.
xlim, ylim	A numeric vector of length 2 specifying the x/y coordinate ranges.
col	The colors used for the points and optionnaly the fitted curve.
which	A character string specifying which estimator should be used. Should be one of "emp" (empirical), "boot" (bootstrap version) and "kendall" (kendall based).
xlab, ylab	The labels for the x/y-axis (may be missing).
add	Logical. If TRUE, the plot is added to the current figure; otherwhise (default) a new plot is computed.
block.size	Integer specifying the block size for the empirical and bootstrap estimator.
plot	Logical. If TRUE (default) a plot is produced.
compute.std.err	Logical. If TRUE, standard errors are estimated using a jackknife procedure. It is currently only available for the Kendall estimator.
...	Additional options to be passed to the plot function.

Value

This function returns invisibly a matrix containing the pairwise distances and the concurrence probability estimates.

Author(s)

Mathieu Ribatet

References

Dombry, C., Ribatet, M. and Stoev, S. (2017) Probabilities of concurrent extremes. To appear in JASA

See Also

fmadogram, lmadogram

Examples

n.site <- 25
locations <- matrix(runif(2*n.site, 0, 10), ncol = 2)
colnames(locations) <- c("lon", "lat")

##Simulate a max-stable process - with unit Frechet margins
n.obs <- 100
data <- rmaxstab(n.obs, locations, cov.mod = "whitmat", nugget = 0, range = 1,
smooth = 1.75)

##Compute the F-madogram
concprob(data, locations)

##Compare the F-madogram with a fitted max-stable process
fitted <- fitmaxstab(data, locations, "whitmat", nugget = 0)
concprob(fitted = fitted)

SpatialExtremes documentation built on July 16, 2024, 3:01 a.m.