Prediction of the marginal parameters for various models

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Description

This function predicts the marginal GEV parameters from a fitted max-stable process, copula, penalized spline or spatial GEV model.

Usage

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## S3 method for class 'maxstab'
predict(object, newdata, ret.per = NULL, std.err =
TRUE, ...)
## S3 method for class 'copula'
predict(object, newdata, ret.per = NULL, std.err =
TRUE, ...)
## S3 method for class 'pspline'
predict(object, newdata, ...)
## S3 method for class 'spatgev'
predict(object, newdata, ret.per = NULL, ...)

Arguments

object

An object of class 'maxstab', 'copula', 'pspline' or 'spatgev'. Most often, it will be the output of one of the following functions: rbpspline, fitmaxstab, fitcopula or fitspatgev.

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.

std.err

Logical. If TRUE (default), standard erros will be computed if possible.

...

further arguments passed to or from other methods.

Value

Print several information on screen.

Author(s)

Mathieu Ribatet

Examples

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## 1- Simulate a max-stable random field
n.site <- 35
locations <- matrix(runif(2*n.site, 0, 10), ncol = 2)
colnames(locations) <- c("lon", "lat")

data <- rmaxstab(50, locations, cov.mod = "whitmat", nugget = 0, range = 30,
smooth = 0.5)

## 2- Transformation to non unit Frechet margins
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)
  data[,i] <- frech2gev(data[,i], param.loc[i], param.scale[i],
param.shape[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(data, 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)

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