Description Usage Arguments Details Value Warning Note References See Also Examples
Maximumlikelihood fitting for the point process model, including generalized linear modelling of each parameter.
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xdat 
A numeric vector of data to be fitted. 
threshold 
The threshold; a single number or a numeric
vector of the same length as 
npy 
The number of observations per year/block. 
ydat 
A matrix of covariates for generalized linear modelling
of the parameters (or 
mul, sigl, shl 
Numeric vectors of integers, giving the columns
of 
mulink, siglink, shlink 
Inverse link functions for generalized linear modelling of the location, scale and shape parameters repectively. 
muinit, siginit, shinit 
numeric giving initial parameter estimates. See Details section for information on default (NULL) initial values. 
show 
Logical; if 
method 
The optimization method (see 
maxit 
The maximum number of iterations. 
... 
Other control parameters for the optimization. These
are passed to components of the 
For nonstationary fitting it is recommended that the covariates
within the generalized linear models are (at least approximately)
centered and scaled (i.e.\ the columns of ydat
should be
approximately centered and scaled). Otherwise, the numerics may
become unstable.
As of version 1.32, a more accurate estimate of the exceedance rate, in the face of covariates, is used (at the expense of computational efficiency). In particular, when including covariates, parameter estimates may differ from those in Coles (2001).
Let m=mean(xdat) and s=sqrt(6*var(xdat))/pi. Then, initial values assigend when 'muinit' is NULL are m  0.57722 * s (stationary case). When 'siginit' is NULL, the initial value is taken to be s, and when 'shinit' is NULL, the initial value is taken to be 0.1. When covariates are introduced (nonstationary case), these same initial values are used by default for the constant term, and zeros for all other terms. For example, if a GEV( mu(t)=mu0+mu1*t, sigma, xi) is being fitted, then the initial value for mu0 is m  0.57722 * s, and 0 for mu1.
A list containing the following components. A subset of these
components are printed after the fit. If show
is
TRUE
, then assuming that successful convergence is
indicated, the components nexc
, nllh
, mle
and se
are always printed.
trans 
An logical indicator for a nonstationary fit. 
model 
A list with components 
link 
A character vector giving inverse link functions. 
threshold 
The threshold, or vector of thresholds. 
npy 
The number of observations per year/block. 
nexc 
The number of data points above the threshold. 
data 
The data that lie above the threshold. For nonstationary models, the data is standardized. 
conv 
The convergence code, taken from the list returned by

nllh 
The negative logarithm of the likelihood evaluated at the maximum likelihood estimates. 
vals 
A matrix with four columns containing the maximum likelihood estimates of the location, scale and shape parameters, and the threshold, at each data point. 
gpd 
A matrix with three rows containing the maximum likelihood estimates of corresponding GPD location, scale and shape parameters at each data point. 
mle 
A vector containing the maximum likelihood estimates. 
cov 
The covariance matrix. 
se 
A vector containing the standard errors. 
Different optimization methods may result in wildly different parameter estimates.
This code is adapted by Eric Gilleland from code originally written for SPlus by Stuart Coles, and ported to R by Alec Stephenson. See details section above.
Beirlant J, Goegebeur Y, Segers J and Teugels J. (2004). Statistics of Extremes, Wiley, Chichester, England.
Coles, Stuart (2001). An Introduction to Statistical Modeling of Extreme Values. SpringerVerlag, London.
pp.diag
, optim
,
pp.fitrange
, mrl.plot
,
gpd.fit
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