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
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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 20 | ##require(maps) ## <<-- to plot US borders
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)
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Warning:
Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
(GCV) Generalized Cross-Validation
minimum at right endpoint lambda = 90.50967 (eff. df= 3.001411 )
Warning:
Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
(GCV) Generalized Cross-Validation
minimum at right endpoint lambda = 90.50967 (eff. df= 3.001411 )
Warning:
Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
(GCV) Generalized Cross-Validation
minimum at right endpoint lambda = 90.50967 (eff. df= 3.001411 )
Warning:
Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
(GCV) Generalized Cross-Validation
minimum at right endpoint lambda = 90.50967 (eff. df= 3.001411 )
Warning:
Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
(GCV) Generalized Cross-Validation
minimum at right endpoint lambda = 90.50967 (eff. df= 3.001411 )
Warning:
Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
(GCV) Generalized Cross-Validation
minimum at right endpoint lambda = 90.50967 (eff. df= 3.001411 )
Warning:
Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
(GCV) Generalized Cross-Validation
minimum at right endpoint lambda = 90.50967 (eff. df= 3.001411 )
Warning:
Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
(GCV) Generalized Cross-Validation
minimum at right endpoint lambda = 90.50967 (eff. df= 3.001411 )
Warning:
Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
(GCV) Generalized Cross-Validation
minimum at right endpoint lambda = 90.50967 (eff. df= 3.001411 )
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