Data above threshold is modelled using the limiting point process
of extremes.
fit.pp(
xdat,
threshold = 0,
npp = 1,
np = NULL,
method = c("nlminb", "BFGS"),
start = NULL,
show = FALSE,
fpar = NULL,
warnSE = FALSE
)a list containing the following components:
estimate a vector containing all parameters (optimized and fixed).
std.err a vector containing the standard errors.
vcov the variance covariance matrix, obtained as the numerical inverse of the observed information matrix.
threshold the threshold.
method the method used to fit the parameter. See details.
nllh the negative log-likelihood evaluated at the parameter estimate.
nat number of points lying above the threshold.
pat proportion of points lying above the threshold.
convergence components taken from the list returned by optim.
Values other than 0 indicate that the algorithm likely did not converge (in particular 1 and 50).
counts components taken from the list returned by optim.
a numeric vector of data to be fitted.
the chosen threshold.
number of observation per period. See Details
number of periods of data, if xdat only contains exceedances.
the method to be used. See Details. Can be abbreviated.
named list of starting values
logical; if TRUE (the default), print details of the fit.
a named list with optional fixed components loc, scale and shape
logical; if TRUE, a warning is printed if the standard errors cannot be returned from the observed information matrix when the shape is less than -0.5.
The parameter npp controls the frequency of observations.
If data are recorded on a daily basis, using a value of npp = 365.25
yields location and scale parameters that correspond to those of the
generalized extreme value distribution fitted to block maxima.
Coles, S. (2001), An introduction to statistical modelling of extreme values. Springer : London, 208p.
data(eskrain)
pp_mle <- fit.pp(eskrain, threshold = 30, np = 6201)
plot(pp_mle)
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