# concurrencemap: Maps of concurrence probabilities/expected concurrence cell... In SpatialExtremes: Modelling Spatial Extremes

## Description

This function produces maps for concurrence probabilities or expected concurrence cell areas.

## Usage

 ```1 2 3``` ```concurrencemap(data, coord, which = "kendall", type = "cell", n.grid = 100, col = cm.colors(64), plot = TRUE, plot.border = NULL, 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. `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 `TRUE` (default), a map is produced; otherwise results are invisibly returned. `plot.border` The name of an R function that can be used to plot the border of the study region. If `NULL`, no border are plotted. `compute.std.err` Logical. If `TRUE`, a map of standard errors is also produced. It is currently only available for concurrence probability maps. `...` Additional options to be passed to the `image` function.

## Value

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

## References

Dombry, C., Ribatet, M. and Stoev, S. (2015) Probabilities of concurrent extremes. Submitted

`concprob`

## Examples

 ``` 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) ```

### Example output  ```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 Cross-Validation
minimum at  right endpoint  lambda  =  7.193636e-06 (eff. df= 28.49999 )
Warning:
Grid searches over lambda (nugget and sill variances) with  minima at the endpoints:
(GCV) Generalized Cross-Validation
minimum at  right endpoint  lambda  =  7.193636e-06 (eff. df= 28.49999 )
Warning:
Grid searches over lambda (nugget and sill variances) with  minima at the endpoints:
(GCV) Generalized Cross-Validation
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 Cross-Validation
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 Cross-Validation
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 Cross-Validation
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 Cross-Validation
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 Cross-Validation
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 Cross-Validation
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 Cross-Validation
minimum at  right endpoint  lambda  =  7.193636e-06 (eff. df= 28.49999 )
Warning:
Grid searches over lambda (nugget and sill variances) with  minima at the endpoints:
(GCV) Generalized Cross-Validation
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 Cross-Validation
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 Cross-Validation
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 Cross-Validation
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 Cross-Validation
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 Cross-Validation
minimum at  right endpoint  lambda  =  94.51687 (eff. df= 3.001042 )
```

SpatialExtremes documentation built on May 2, 2019, 5:45 p.m.