fitpp  R Documentation 
This function estimates the point process characterisation from exceedances above a threshold.
fitpp(data, threshold, noy = length(data) / 365.25, start, ..., std.err.type = "observed", corr = FALSE, method = "BFGS", warn.inf = TRUE)
data 
A numeric vector. 
threshold 
A numeric value giving the threshold for the GPD. 
noy 
Numeric. The number of year of observation. 
start 
A named list that gives the starting values for the optimization routine. Each list argument must correspond to one parameter to be estimated. May be missing. 
... 
Other optional arguments to be passed to the

std.err.type 
A character string. If "observed", the standard errors are derived from the observed Fisher information matrix. If "none", standard errors are not computed. 
corr 
Logical. Does the asymptotic correlation matrix has to be
computed? Default is "not computed"  e.g. 
method 
A character string specifying which numerical
optimization procedure has to be used. See 
warn.inf 
Logical. If 
This function returns a list with components:
fitted.values 
A vector containing the estimated parameters. 
std.err 
A vector containing the standard errors. 
fixed 
A vector containing the parameters of the model that have been held fixed. 
param 
A vector containing all parameters (optimized and fixed). 
deviance 
The deviance at the maximum likelihood estimates. 
corr 
The correlation matrix. 
convergence, counts, message 
Components taken from the
list returned by 
threshold 
The threshold passed to argument 
nat, pat 
The number and proportion of exceedances. 
data 
The data passed to the argument 
exceed 
The exceedances, or the maxima of the clusters of exceedances. 
scale 
The scale parameter for the fitted generalized Pareto distribution. 
std.err.type 
The standard error type  for 
var.thresh 
Logical. Specify if the threshold is a varying one 

Mathieu Ribatet
Coles, S. (2001) An Introduction to Statistical Modelling of Extreme Values. Springer Series in Statistics. London.
Embrechts, P and Kluppelberg, C. and Mikosch, T (1997) Modelling Extremal Events for Insurance and Finance. Springers.
Pickands, J. (1975) Statistical Inference Using Extreme Order Statistics. Annals of Statistics. 3:119–131.
x < rgpd(1000, 0, 1, 0.2) fitpp(x, 0)
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