Methods for Diagnostic Functions in Package ‘RobExtremes’

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Description

We provide wrapper to the diagnostic plots gpd.diag and gev.diag of package ismev, as well as to profilers gpd.prof, gpd.profxi and gev.prof, gev.profxi.

Usage

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gpd.diag(z,...)
## S4 method for signature 'gpd.fit'
gpd.diag(z)
## S4 method for signature 'GPDEstimate'
gpd.diag(z, npy = 365)
gev.diag(z)
## S4 method for signature 'gev.fit'
gev.diag(z)
## S4 method for signature 'GEVEstimate'
gev.diag(z)
gpd.prof(z,...)
## S4 method for signature 'gpd.fit'
gpd.prof(z, m, xlow, xup, npy = 365, conf = 0.95, nint = 100)
## S4 method for signature 'GPDEstimate'
gpd.prof(z, m, xlow, xup, npy = 365, conf = 0.95, nint = 100)
gev.prof(z,...)
## S4 method for signature 'gev.fit'
gev.prof(z, m, xlow, xup, conf = 0.95, nint = 100)
## S4 method for signature 'GEVEstimate'
gev.prof(z, m, xlow, xup, conf = 0.95, nint = 100)
gpd.profxi(z,...)
## S4 method for signature 'gpd.fit'
gpd.profxi(z,  xlow, xup, conf = 0.95, nint = 100)
## S4 method for signature 'GPDEstimate'
gpd.profxi(z,  xlow, xup, npy = 365, conf = 0.95, nint = 100)
gev.profxi(z,...)
## S4 method for signature 'gev.fit'
gev.profxi(z, xlow, xup, conf = 0.95, nint = 100)
## S4 method for signature 'GEVEstimate'
gev.profxi(z, xlow, xup, conf = 0.95, nint = 100)

Arguments

z

an argument of class gpd.fit, gev.fit (recovering the original calling convention from package ismev or of class GEVFamily or GParetoFamily.

m

The return level (i.e.\ the profile likelihood is for the value that is exceeded with probability 1/m).

...

further parameters to be passed on the specific methods.

xlow, xup

The least and greatest value at which to evaluate the profile likelihood.

npy

The number of observations per year.

conf

The confidence coefficient of the plotted profile confidence interval.

nint

The number of points at which the profile likelihood is evaluated.

Details

We provide a coercing of our fits of S4-classes "GPDEstimate" and "GEVEstimate" to the (S3-)classes gpd.fit and gev.fit of package ismev (the latter being cast to an S4 class, internally, in our package.

Value

For gpd.fit, gev.fit (quoted from package ismev: For stationary models four plots are produced; a probability plot, a quantile plot, a return level plot and a histogram of data with fitted density.

For non-stationary models two plots are produced; a residual probability plot and a residual quantile plot.

For gpd.prof, gev.prof (quoted from package ismev:

A plot of the profile likelihood is produced, with a horizontal line representing a profile confidence interval with confidence coefficient conf.

Author(s)

Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de

References

ismev: An Introduction to Statistical Modeling of Extreme Values. R package version 1.39. http://CRAN.R-project.org/package=ismev; original S functions written by Janet E. Heffernan with R port and R documentation provided by Alec G. Stephenson. (2012).

Coles, S. (2001). An introduction to statistical modeling of extreme values. London: Springer.

Examples

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if(require(ismev)){
  ## from ismev
  data(portpirie)
  data(rain)

  detach(package:ismev)
  ppfit <- ismev::gev.fit(portpirie[,2])
  gev.diag(ppfit)
  ##
  ## Not run: 
  mlE <- MLEstimator(portpirie[,2], GEVFamilyMuUnknown(withPos=FALSE))
  gev.diag(mlE)
    gev.prof(mlE, m = 10, 4.1, 5)
    gev.profxi(mlE, -0.3, 0.3)
  
## End(Not run)

  rnfit <- ismev::gpd.fit(rain,10)
  gpd.diag(rnfit)
  ##
  mlE2 <- MLEstimator(rain[rain>10], GParetoFamily(loc=10))
  gpd.diag(mlE2)
  ## Not run: 
    gpd.prof(mlE2, m = 10, 55, 77)
    gpd.profxi(mlE2, -0.02, 0.02)
  
## End(Not run)
}

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