madogram {SpatialExtremes} | R Documentation |
Computes madograms
Description
Computes the madogram for max-stable processes.
Usage
madogram(data, coord, fitted, n.bins, gev.param = c(0, 1, 0), which =
c("mado", "ext"), xlab, ylab, col = c(1, 2), angles = NULL, marge =
"emp", add = FALSE, xlim = c(0, max(dist)), ...)
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
|
n.bins |
The number of bins to be used. If missing, pairwise madogram estimates will be computed. |
gev.param |
Numeric vector of length 3 specifying the location, scale and shape parameters for the GEV. |
which |
A character vector of maximum size 2. It specifies if the madogram and/or the extremal coefficient functions have to be plotted. |
xlab , ylab |
The x-axis and y-axis labels. May be missing. Note
that |
col |
The colors used for the points and optionnaly for the fitted curve. |
angles |
A numeric vector. A partition of the interval
|
marge |
Character string. If 'emp', the observation are first transformed to the unit Frechet scale by using the empirical CDF. If 'mle' (default), maximum likelihood estimates are used. |
add |
Logical. If |
xlim |
A numeric vector of length 2 specifying the x coordinate range. |
... |
Additional options to be passed to the |
Details
Let Z(x)
be a stationary process. The madogram is defined
as follows:
\nu(h) = \frac{1}{2}\mbox{E}\left[|Z(x+h) - Z(x)|
\right]
If now Z(x)
is a stationary max-stable random field with
GEV marginals. Provided the GEV shape parameter \xi
is such
that \xi < 1
. The extremal coefficient
\theta(h)
satisfies:
\theta(h) =
\left\{
\begin{array}{ll}
u_\beta \left(\mu + \frac{\nu(h)}{\Gamma(1 - \xi)} \right), &\xi
\neq 0\\
\exp\left(\frac{\nu(h)}{\sigma}\right), &\xi = 0
\end{array}
\right.
where \Gamma
is the gamma function and
u_\beta
is defined as follows:
u_\beta(u) = \left(1 + \xi \frac{u - \mu}{\sigma}
\right)_+^{1/\xi}
and
\beta = (\mu, \sigma, \xi)
, i.e,
the vector of the GEV parameters.
Value
A graphic and (invisibly) a matrix with the lag distances, the madogram and extremal coefficient estimates.
Author(s)
Mathieu Ribatet
References
Cooley, D., Naveau, P. and Poncet, P. (2006) Variograms for spatial max-stable random fields. Dependence in Probability and Statistics, 373–390.
See Also
Examples
n.site <- 15
locations <- matrix(runif(2*n.site, 0, 10), ncol = 2)
colnames(locations) <- c("lon", "lat")
##Simulate a max-stable process - with unit Frechet margins
data <- rmaxstab(40, locations, cov.mod = "whitmat", nugget = 0, range = 1,
smooth = 2)
##Compute the madogram
madogram(data, locations)
##Compare the madogram with a fitted max-stable model
fitted <- fitmaxstab(data, locations, "whitmat", nugget = 0)
madogram(fitted = fitted, which = "ext")