Estimates the covariance matrix for the Smith's model
Description
Estimates the covariance matrix for the Smith's model using nonparametric estimates of the pairwise extremal coefficients.
Usage
1 2 
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. 
marge 
Character string specifying how margins are transformed
to unit Frechet. Must be one of "emp", "frech" or "mle"  see
function 
iso 
Logical. If 
control 
The control arguments to be passed to the

... 
Optional arguments to be passed to the 
start 
A named list giving the initial values for the
parameters over which the weighted sum of square is to be
minimized. If 
weighted 
Logical. Should weighted least squares be used? 
Details
The fitting procedure is based on weighted least squares. More precisely, the fitting criteria is to minimize:
∑_{i,j} [(θ_{i,j}^+  θ_{i,j}^*) / s_{i,j}]^2
where θ_{i,j}^+ is a non
parametric estimate of the extremal coefficient related to location
i
and j
, θ_{i,j}^* is
the fitted extremal coefficient derived from the Smith's model and
s_{i,j} are the standard errors related to the
estimates θ_{i,j}^+.
Value
An object of class maxstab.
Author(s)
Mathieu Ribatet
References
Smith, R. L. (1990) Maxstable processes and spatial extremes. Unpublished manuscript.
See Also
fitcovariance
, fitmaxstab
,
fitextcoeff
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13  n.site < 50
n.obs < 100
locations < matrix(runif(2*n.site, 0, 40), ncol = 2)
colnames(locations) < c("lon", "lat")
## Simulate a maxstable process  with unit Frechet margins
data < rmaxstab(50, locations, cov.mod = "gauss", cov11 = 200, cov12 =
0, cov22 = 200)
fitcovmat(data, locations)
##Force an isotropic model
fitcovmat(data, locations, iso = TRUE)

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