Description Usage Arguments Value Author(s) References See Also Examples
This function produces maps for concurrence probabilities or expected concurrence cell areas.
1 2 3 
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. 
which 
A character string specifying which estimator should be used. Should be one of "emp" (empirical), "boot" (bootstrap version) and "kendall" (kendall based). 
type 
Either "cell" for cell areas or a integer between 1 and the number of locations specifying which site should be used as reference location—see Details. 
n.grid 
Integer specifying the size of the prediction grid. 
col 
The colors used to produce the map. 
plot 
Logical. If 
plot.border 
The name of an R function that can be used to plot
the border of the study region. If 
compute.std.err 
Logical. If 
... 
Additional options to be passed to the

This function returns invisibly a list with the x and y coordinates and the corresponding values for the estimated concurrence probabilities or expected concurrence cell area.
Mathieu Ribatet
Dombry, C., Ribatet, M. and Stoev, S. (2015) Probabilities of concurrent extremes. Submitted
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  require(maps)
data(USHCNTemp)
coord < as.matrix(metadata[,2:3])
## Subset the station to have a fast example
n.site < 30
chosen.site < sample(nrow(coord), n.site)
coord < coord[chosen.site,]
maxima.summer < maxima.summer[,chosen.site]
## Define a function to plot the border
border < function(add = FALSE) maps::map("usa", add = add)
par(mar = rep(0, 4))
## Produce a pairwise concurrence probability map w.r.t. station number 15
concurrencemap(maxima.summer, coord, type = 15, plot.border = border, compute.std.err = TRUE)
## Produce the expected concurrence cell area
concurrencemap(maxima.summer, coord, plot.border = border)

Loading required package: maps
Attaching package: 'maps'
The following object is masked from 'package:SpatialExtremes':
map
Warning:
Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
(GCV) Generalized CrossValidation
minimum at right endpoint lambda = 7.193636e06 (eff. df= 28.49999 )
Warning:
Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
(GCV) Generalized CrossValidation
minimum at right endpoint lambda = 7.193636e06 (eff. df= 28.49999 )
Warning:
Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
(GCV) Generalized CrossValidation
minimum at right endpoint lambda = 94.51687 (eff. df= 3.001042 )
Warning:
Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
(GCV) Generalized CrossValidation
minimum at right endpoint lambda = 94.51687 (eff. df= 3.001042 )
Warning:
Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
(GCV) Generalized CrossValidation
minimum at right endpoint lambda = 94.51687 (eff. df= 3.001042 )
Warning:
Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
(GCV) Generalized CrossValidation
minimum at right endpoint lambda = 94.51687 (eff. df= 3.001042 )
Warning:
Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
(GCV) Generalized CrossValidation
minimum at right endpoint lambda = 94.51687 (eff. df= 3.001042 )
Warning:
Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
(GCV) Generalized CrossValidation
minimum at right endpoint lambda = 94.51687 (eff. df= 3.001042 )
Warning:
Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
(GCV) Generalized CrossValidation
minimum at right endpoint lambda = 94.51687 (eff. df= 3.001042 )
Warning:
Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
(GCV) Generalized CrossValidation
minimum at right endpoint lambda = 7.193636e06 (eff. df= 28.49999 )
Warning:
Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
(GCV) Generalized CrossValidation
minimum at right endpoint lambda = 94.51687 (eff. df= 3.001042 )
Warning:
Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
(GCV) Generalized CrossValidation
minimum at right endpoint lambda = 94.51687 (eff. df= 3.001042 )
Warning:
Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
(GCV) Generalized CrossValidation
minimum at right endpoint lambda = 94.51687 (eff. df= 3.001042 )
Warning:
Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
(GCV) Generalized CrossValidation
minimum at right endpoint lambda = 94.51687 (eff. df= 3.001042 )
Warning:
Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
(GCV) Generalized CrossValidation
minimum at right endpoint lambda = 94.51687 (eff. df= 3.001042 )
Warning:
Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
(GCV) Generalized CrossValidation
minimum at right endpoint lambda = 94.51687 (eff. df= 3.001042 )
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