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

condmap {SpatialExtremes} | R Documentation

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

### Description

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

### Usage

    condmap(fitted, fix.coord, x, y, covariates = NULL, ret.per1 = 100,
    ret.per2 = ret.per1, 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`.
`fix.coord` | The spatial coordinates of the location from which the conditional quantile is computed.
`x,y` | Numeric vector defining the grid at which the levels are computed.
`covariates` | An array specifying the covariates at each grid point defined by `x` and `y`. If `NULL`, no covariate is needed. See `map` to see how to build it.
`ret.per1,ret.per2` | Numerics giving the return period for which the quantile map is plotted. See details.
`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.

### Details

The function solves the following equation:

Pr[Z(x_2) > z_2 | Z(x_1) > z_1] = 1 / T_2

where z_1 = -1 / log(1 - 1/T_1).

In other words, it computes, given that at location x_1 we exceed the level z_1, the levels which is expected to be exceeded in average every T_2 year.

### Value

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

### Author(s)

Mathieu Ribatet

### See Also

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

### Examples

    ##Define the coordinate of each location
    n.site <- 30
    locations <- matrix(runif(2*n.site, 0, 10), ncol = 2)
    colnames(locations) <- c("lon", "lat")

    ##Simulate a max-stable process - with unit Frechet margins
    data <- rmaxstab(50, locations, cov.mod = "whitmat", nugget = 0, range =
    2, smooth = 1)

    ##Now define the spatial model for the GEV parameters
    param.loc <- -10 - 4 * locations[,1] + locations[,2]^2
    param.scale <- 5 + locations[,2] + locations[,1]^2 / 10
    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 + I(lat^2)
    scale.form <- scale ~ lat + I(lon^2)
    shape.form <- shape ~ 1

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

    cond.coord <- c(5.1, 5.1)
    condmap(fitted, cond.coord, seq(0, 10, length = 25), seq(0,10, length
     =25), ret.per1 = 100, ret.per2 = 1.5)
    points(t(cond.coord), pch = "*", col = 2, cex = 2)

[Package SpatialExtremes version 2.0-5 Index]