gevSim generates data from the GEV distribution,
gumbelSim generates data from the Gumbel distribution,
gevFit fits data to the GEV distribution,
gumbelFit fits data to the Gumbel distribution,
print print method for a fitted GEV object,
plot plot method for a fitted GEV object,
summary summary method for a fitted GEV object,
gevrlevelPlot k-block return level with confidence intervals. }gevSim(model = list(xi = -0.25, mu = 0, beta = 1), n = 1000, seed = NULL)
gumbelSim(model = list(mu = 0, beta = 1), n = 1000, seed = NULL)gevFit(x, block = 1, type = c("mle", "pwm"), title = NULL, description = NULL, ...)
gumbelFit(x, block = 1, type = c("mle", "pwm"), title = NULL, description = NULL, ...)
show.fGEVFIT(object)
## S3 method for class 'fGEVFIT':
plot(x, which = "ask", \dots)
## S3 method for class 'fGEVFIT':
summary(object, doplot = TRUE, which = "all", \dots)
shape, location and
scale giving the parameters of the GEV distribution.
By default the shape parameter has the value -0.25, the
location is zero"gevFit"."mle", the
default value, or by the probability weighted moment menthod
"pwm".which="ask" the
user will be interactively asked which of the plots should be
desplayed. By defaulmethod="mle" the interpretation
depends on the value of block: if no block size is specified then
data are interpreted as block xi is the shape parameter, mu the location parameter,
and sigma is the scale parameter. The default values are
xi=1, mu=0, and beta=1. Note, if control argument of
optim.
[hillPlot] -
ogevSim
returns a vector of data points from the simulated series.
gevFit
returns an object of class gev describing the fit.
print.summary
prints a report of the parameter fit.
summary
performs diagnostic analysis. The method provides two different
residual plots for assessing the fitted GEV model.
gevrlevelPlot
returns a vector containing the lower 95% bound of the confidence
interval, the estimated return level and the upper 95% bound.
hillPlot
displays a plot.
shaparmPlot
returns a list with one or two entries, depending on the
selection of the input variable both.tails. The two
entries upper and lower determine the position of
the tail. Each of the two variables is again a list with entries
pickands, hill, and dehaan. If one of the
three methods will be discarded the printout will display zeroes.gevFit and gumbelFit estimate the parameters either
by the probability weighted moment method, method="pwm" or
by maximum log likelihood estimation method="mle". The
summary method produces diagnostic plots for fitted GEV or Gumbel
models.
Methods:
print.gev, plot.gev and summary.gev are
print, plot, and summary methods for a fitted object of class
gev. Concerning the summary method, the data are
converted to unit exponentially distributed residuals under null
hypothesis that GEV fits. Two diagnostics for iid exponential data
are offered. The plot method provides two different residual plots
for assessing the fitted GEV model. Two diagnostics for
iid exponential data are offered.
Return Level Plot:
gevrlevelPlot calculates and plots the k-block return level
and 95% confidence interval based on a GEV model for block maxima,
where k is specified by the user. The k-block return level
is that level exceeded once every k blocks, on average. The
GEV likelihood is reparameterized in terms of the unknown return
level and profile likelihood arguments are used to construct a
confidence interval.
Hill Plot:
The function hillPlot investigates the shape parameter and
plots the Hill estimate of the tail index of heavy-tailed data, or
of an associated quantile estimate. This plot is usually calculated
from the alpha perspective. For a generalized Pareto analysis of
heavy-tailed data using the gpdFit function, it helps to
plot the Hill estimates for xi.
Shape Parameter Plot:
The function shaparmPlot investigates the shape parameter and
plots for the upper and lower tails the shape parameter as a function
of the taildepth. Three approaches are considered, the Pickands
estimator, the Hill estimator, and the
Decker-Einmal-deHaan estimator.## gevSim -
# Simulate GEV Data, use default length n=1000
x = gevSim(model = list(xi = 0.25, mu = 0 , beta = 1))
## gevFit -
# Fit GEV Data by Probability Weighted Moments:
fit = gevFit(x, type = "pwm")
print(fit)
## summary -
# Summarize Results:
par(mfcol = c(2, 2))
summary(fit)Run the code above in your browser using DataLab