predict {SpatialExtremes}R Documentation

Prediction of the max-stable marginal parameters

Description

This function predicts the marginal GEV parameters from a fitted max-stable process.

Usage

## S3 method for class 'maxstab':
predict(object, newdata, ret.per = NULL, ...)

Arguments

object An object of class ``maxstab''. Most often, it will be the output of the function fitmaxstab.
newdata An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used.
ret.per Numeric vector giving the return periods for which return levels are computed. If NULL (default), no return levels are computed.
... further arguments passed to or from other methods.

Value

'predict.maxstab' produces a vector of predictions or a matrix of predictions.

Author(s)

Mathieu Ribatet

See Also

predict

Examples

## 1- Simulate a max-stable random field
require(RandomFields)
n.site <- 35
locations <- matrix(runif(2*n.site, 0, 10), ncol = 2)
colnames(locations) <- c("lon", "lat")
ms0 <- MaxStableRF(locations[,1], locations[,2], grid=FALSE, model="wh",
                   param=c(0,1,0,3, .5), maxstable="extr",
                   n = 50)
## 2- Transformation to non unit Frechet margins
ms1 <- t(ms0)
param.loc <- -10 + 2 * locations[,2]
param.scale <- 5 + 2 * locations[,1]
param.shape <- rep(0.2, n.site)
for (i in 1:n.site)
  ms1[,i] <- param.scale[i] * (ms1[,i]^param.shape[i] - 1) /
  param.shape[i] + param.loc[i]

## 3- Fit a max-stable process with the following model for
##    the GEV parameters
form.loc <- loc ~ lat
form.scale <- scale ~ lon
form.shape <- shape ~ 1

schlather <- fitmaxstab(ms1, locations, "whitmat", loc.form = form.loc,
                        scale.form = form.scale, shape.form =
                        form.shape)

## 4- GEV parameters estimates at each locations or at ungauged locations
predict(schlather)
ungauged <- data.frame(lon = runif(10, 0, 10), lat = runif(10, 0, 10))
predict(schlather, ungauged)

[Package SpatialExtremes version 1.1-1 Index]