condmap: Produces a conditional 2D map from a fitted max-stable...
In SpatialExtremes: Modelling Spatial Extremes

Description Usage Arguments Details Value Author(s) See Also Examples

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

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condmap(fitted, fix.coord, x, y, covariates = NULL, ret.per1 = 100,
ret.per2 = ret.per1, col = terrain.colors(64), plot.contour = TRUE,
...)

`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.

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.

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

Mathieu Ribatet

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

##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)

Computing appropriate starting values
Starting values are defined
Starting values are:
      range      smooth   locCoeff1   locCoeff2   locCoeff3 scaleCoeff1 
 1.05676396  1.79636861 -8.43385849 -4.09474844  1.00287726  5.26106517 
scaleCoeff2 scaleCoeff3 shapeCoeff1 
 1.20076160  0.08369529  0.12338157