gev.d.fit.Rd 4.2 KB
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% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/gevdfit.R
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\name{gev.d.fit}
\alias{gev.d.fit}
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\title{Maximum-likelihood Fitting of the duration-dependent GEV Distribution}
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\usage{
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gev.d.fit(
  xdat,
  ds,
  ydat = NULL,
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  mutl = NULL,
  sigma0l = NULL,
  xil = NULL,
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  thetal = NULL,
  etal = NULL,
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  mutlink = make.link("identity"),
  sigma0link = make.link("identity"),
  xilink = make.link("identity"),
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  thetalink = make.link("identity"),
  etalink = make.link("identity"),
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  init.vals = as.list(rep(NA, 5)),
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  theta_zero = FALSE,
  show = TRUE,
  method = "Nelder-Mead",
  maxit = 10000,
  ...
)
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}
\arguments{
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\item{xdat}{A vector containing maxima for different durations.
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This can be obtained from \code{\link{IDF.agg}}.}
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\item{ds}{A vector of aggregation levels corresponding to the maxima in xdat.
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1/60 corresponds to 1 minute, 1 corresponds to 1 hour.}
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\item{ydat}{A matrix of covariates for generalized linear modeling of the parameters
(or NULL (the default) for stationary fitting). The number of rows should be the same as the
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length of xdat.}
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\item{mutl, sigma0l, xil, thetal, etal}{Numeric vectors of integers, giving the columns of ydat that contain
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covariates for generalized linear modeling of the parameters (or NULL (the default)
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if the corresponding parameter is stationary).
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Parameters are: modified location, scale offset, shape, duration offset, duration exponent, respectively.}
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\item{mutlink, sigma0link, xilink, thetalink, etalink}{Link functions for generalized linear
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modeling of the parameters, created with \code{\link{make.link}}. The default is \code{make.link("identity")}.}
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\item{init.vals}{list of length 5, giving initial values for all or some parameters
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(order: mut, sigma0, xi, theta, eta). If as.list(rep(NA,5)) (the default) is given, initial parameters are obtained
internally by fitting the GEV separately for each duration and applying a linear model to obtain the
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duration dependency of the location and shape parameter.
Initial values for covariate parameters are assumed as 0 if not given.}
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\item{theta_zero}{Logical value, indicating if theta should be estimated (FALSE, the default) or
should stay zero.}
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\item{show}{Logical; if TRUE (the default), print details of the fit.}

\item{method}{The optimization method used in \code{\link{optim}}.}

\item{maxit}{The maximum number of iterations.}

\item{...}{Other control parameters for the optimization.}
}
\value{
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A list containing the following components.
A subset of these components are printed after the fit.
If \code{show} is TRUE, then assuming that successful convergence is indicated,
the components nllh, mle and se are always printed.
\item{nllh}{single numeric giving the negative log-likelihood value}
\item{mle}{numeric vector giving the MLE's for the modified location, scale_0, shape,
duration offset and duration exponent, resp.}
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\item{se}{numeric vector giving the standard errors for the MLE's (in the same order)}
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\item{trans}{A logical indicator for a non-stationary fit.}
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\item{model}{A list with components mutl, sigma0l, xil, thetal and etal.}
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\item{link}{A character vector giving inverse link functions.}
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\item{conv}{The convergence code, taken from the list returned by \code{\link{optim}}.
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A zero indicates successful convergence.}
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\item{data}{data is standardized to standard Gumbel.}
\item{cov}{The covariance matrix.}
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\item{vals}{Parameter values for every data point.}
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\item{init.vals}{Initial values that were used.}
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\item{ds}{Durations for every data point.}
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}
\description{
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Modified \code{\link[ismev]{gev.fit}} function for Maximum-likelihood fitting
for the duration-dependent generalized extreme
value distribution, following Koutsoyiannis et al. (1998), including generalized linear
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modeling of each parameter.
}
\details{
For details on the d-GEV and the parameter definitions, see \link{IDF-package}.
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}
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\examples{
# sampled random data from d-gev with covariates
# GEV parameters:
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# mut = 4 + 0.2*cov1 +0.5*cov2
# sigma0 = 2+0.5*cov1
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# xi = 0.5
# theta = 0
# eta = 0.5

data('example',package ='IDF')

gev.d.fit(xdat=example$dat,ds = example$d,ydat=as.matrix(example[,c('cov1','cov2')])
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,mutl=c(1,2),sigma0l=1)
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}
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\seealso{
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\code{\link{IDF-package}}, \code{\link{IDF.agg}}, \code{\link{gev.fit}}, \code{\link{optim}}
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}