R: Produces a 2D map from a fitted max-stable process

map {SpatialExtremes} | R Documentation

## Produces a 2D map from a fitted max-stable process

### Description

Produces a 2D map from a fitted max-stable process.

### Usage

    map(fitted, x, y, covariates = NULL, param = "quant", ret.per = 100, col
    = terrain.colors(64), plot.contour = TRUE, ...)

### Arguments

`fitted` | An object of class `maxstab`. Most often, it will be the output of the function `fitmaxstab`.
`x,y` | Numeric vector that gives the coordinates of the grid.
`covariates` | An array specifying the covariates at each grid point defined by `x` and `y`. If `NULL`, no covariate is needed. See the example to see how to build it.
`param` | A character string. Must be one of "loc", "scale", "shape" or "quant" for a map of the location, scale, shape parameters or for a map of a specified quantile.
`ret.per` | A numeric giving the return period for which the quantile map is plotted. It is only required if `param = "quant"`.
`col` | A list of colors such as that generated by 'rainbow', 'heat.colors', 'topo.colors', 'terrain.colors' or similar functions.
`plot.contour` | Logical. If `TRUE` (default), contour lines are added to the plot.
`...` | Several arguments to be passed to the `image` function.

### Value

A plot. Additionally, a list with the details for plotting the map is returned invisibly.

### Author(s)

Mathieu Ribatet

### See Also

`condmap`, `filled.contour`, `heatmap`, `heat.colors`, `topo.colors`, `terrain.colors`, `rainbow`

### Examples

    ##We run an artifical example using the volcano data set as a study
    ##region
    dim <- dim(volcano)
    n.x <- dim[1]
    n.y <- dim[2]

    x <- 10 * 1:n.x
    y <- 10 * 1:n.y

    n.site <- 15
    idx.x <- sample(n.x, n.site)
    idx.y <- sample(n.y, n.site)
    locations <- cbind(lon = x[idx.x], lat = y[idx.y])
    alt <- diag(volcano[idx.x, idx.y])

    ##Simulate a max-stable process - with unit Frechet margins
    data <- rmaxstab(40, locations, cov.mod = "whitmat", nugget = 0, range = 750,
    smooth = 1)

    ##Now define the spatial model for the GEV parameters
    param.loc <- -10 - 0.04 * locations[,1] + alt / 5
    param.scale <- 5 - locations[,2] / 30 + alt / 4
    param.shape <- rep(.2, n.site)

    ##Transform the unit Frechet margins to GEV
    for (i in 1:n.site)
      data[,i] <- frech2gev(data[,i], param.loc[i], param.scale[i],
    param.shape[i])

    ##Define a model for the GEV margins to be fitted
    ##shape ~ 1 stands for the GEV shape parameter is constant
    ##over the region
    loc.form <- loc ~ lon + alt
    scale.form <- scale ~ lat + alt
    shape.form <- shape ~ 1

    ##  1- Fit a max-stable process
    schlather <- fitmaxstab(data, locations, "whitmat", loc.form, scale.form,
                            shape.form, marg.cov = cbind(alt = alt), nugget
    = 0)

    ##  2- Produce a map of the pointwise 50-year return level

    ##Here we have only one covariate i.e. alt
    n.cov <- 1
    covariates <- array(volcano, dim = c(n.x, n.y, n.cov), dimnames =
    list(NULL, NULL, "alt"))

    par(mfrow = c(1,2))
    image(x, y, volcano, col = terrain.colors(64), main = "Elevation map")
    map(schlather, x, y, covariates, ret.per = 50, plot.contour = FALSE,
    main = "50-year return level")

[Package SpatialExtremes version 2.0-5 Index]