R: Prediction of the marginal parameters for various models

predict {SpatialExtremes} | R Documentation

## Prediction of the marginal parameters for various models

### Description

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

### Usage

    ## 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

    ## 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)

[Package SpatialExtremes version 2.0-5 Index]