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.