ismevgpdgevdiag-methods {RobExtremes} | R Documentation |
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
.
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)
z |
an argument of class |
m |
The return level (i.e.\ the profile likelihood is for the
value that is exceeded with probability |
... |
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. |
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.
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
.
Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de
ismev: An Introduction to Statistical Modeling of Extreme Values. R package version 1.39. https://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.
if(require(ismev)){ ## from ismev data(portpirie) data(rain) detach(package:ismev) ppfit <- ismev::gev.fit(portpirie[,2]) gev.diag(ppfit) ## (mlE <- MLEstimator(portpirie[,2], GEVFamilyMuUnknown(withPos=FALSE))) gev.diag(mlE) ## not tested on CRAN because it takes some time... gev.prof(mlE, m = 10, 4.1, 5) gev.profxi(mlE, -0.3, 0.3) rnfit <- ismev::gpd.fit(rain,10) gpd.diag(rnfit) ## mlE2 <- MLEstimator(rain[rain>10], GParetoFamily(loc=10)) gpd.diag(mlE2) gpd.prof(mlE2, m = 10, 55, 77) gpd.profxi(mlE2, -0.02, 0.02) }