| predict.spatgev {SpatialExtremes} | R Documentation |
This function predicts the marginal GEV parameters from a fitted ''spatial GEV'' model.
## S3 method for class 'spatgev': predict(object, newdata, ret.per = NULL, ...)
object |
An object of class spatgev''. Most often, it will be
the output of the function 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. |
... |
further arguments passed to or from other methods. |
'predict.spatgev' produces a vector of predictions or a matrix of predictions.
Mathieu Ribatet
## 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", sill = 1, range = 3,
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 ''spatial GEV'' mdoel to data with the following models for
## the GEV parameters
form.loc <- loc ~ lat
form.scale <- scale ~ lon
form.shape <- shape ~ 1
fitted <- fitspatgev(data, locations, form.loc, form.scale, form.shape)
## 4- GEV parameters estimates at each locations or at ungauged locations
predict(fitted)
ungauged <- data.frame(lon = runif(10, 0, 10), lat = runif(10, 0, 10))
predict(fitted, ungauged)