gev.d.fit.Rd 3.83 KB
 Jana Ulrich committed Nov 13, 2018 1 % Generated by roxygen2: do not edit by hand  2 % Please edit documentation in R/gevdfit.R  Jana Ulrich committed Nov 13, 2018 3 4 5 6 \name{gev.d.fit} \alias{gev.d.fit} \title{Maximum-likelihood Fitting of the duration dependent GEV Distribution} \usage{  Jana Ulrich committed Dec 21, 2018 7 gev.d.fit(xdat, ds, ydat = NULL, mul = NULL, sigl = NULL,  Jana Ulrich committed Nov 13, 2018 8 9 10  shl = NULL, thetal = NULL, etal = NULL, mulink = identity, siglink = identity, shlink = identity, thetalink = identity, etalink = identity, muinit = NULL, siginit = NULL, shinit = NULL,  Jana Ulrich committed Feb 05, 2019 11  thetainit = NULL, etainit = NULL, show = TRUE,  12  method = "Nelder-Mead", maxit = 10000, init.vals = NULL, ...)  Jana Ulrich committed Nov 13, 2018 13 14 } \arguments{  15 16 \item{xdat}{A vector containing maxima for different durations. This can be obtained from \code{\link{IDF.agg}}.}  Jana Ulrich committed Nov 13, 2018 17 18 19  \item{ds}{A vector of aggregation levels corresponding to the maxima in xdat.}  20 21 22 \item{ydat}{A matrix of covariates for generalized linear modelling of the parameters (or NULL (the default) for stationary fitting). The number of rows should be the same as the length of xdat.}  Jana Ulrich committed Nov 13, 2018 23 24  \item{mul, sigl, shl, thetal, etal}{Numeric vectors of integers, giving the columns of ydat that contain  25 covariates for generalized linear modelling of the parameters (or NULL (the default)  Jana Ulrich committed Nov 13, 2018 26 27 28 29 30 31 if the corresponding parameter is stationary). Parameters are: modified location, scale_0, shape, duration offset, duration exponent repectively.} \item{mulink, siglink, shlink, thetalink, etalink}{Inverse link functions for generalized linear modelling of the parameters.}  32 33 34 35 36 \item{muinit, siginit, shinit, thetainit, etainit}{initial values as numeric of length equal to total number of parameters used to model the parameters. If NULL (the default) is given, initial parameters are obtained internally by fitting the GEV seperately for each duration and applying a linear model to optain the duration dependency of the location and shape parameter.}  Jana Ulrich committed Nov 13, 2018 37 38 39 40 41 42 43  \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.}  44 45 \item{init.vals}{vector of length 5, giving initial values for parameter intercepts}  Jana Ulrich committed Nov 13, 2018 46 47 48 49 50 \item{...}{Other control parameters for the optimization.} } \value{ A list containing the following components. A subset of these components are printed after the fit.  51 52 If show is TRUE, then assuming that successful convergence is indicated, the components nllh, mle and se are always printed.  Jana Ulrich committed Nov 13, 2018 53 54 55 56 57 58 59 60 61 62 \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.} \item{se}{numeric vector giving the standard errors for the MLE's (in the same order).} \item{trans}{An logical indicator for a non-stationary fit.} \item{model}{A list with components mul, sigl, shl, thetal and etal.} \item{link}{A character vector giving inverse link functions.} \item{conv}{The convergence code, taken from the list returned by \code{\link{optim}}. A zero indicates successful convergence.} \item{data}{data is standardized to standart Gumbel.}  63 64 65 66 \item{cov}{The covariance matrix.} \item{vals}{Parameter values for every data point.} \item{init.vals}{Initial values that where used.} \item{ds}{Durations for every data point.}  Jana Ulrich committed Nov 13, 2018 67 68 } \description{  69 70 Modified \code{\link[ismev]{gev.fit}} function for Maximum-likelihood fitting for the duration dependent generalized extreme  Jana Ulrich committed Nov 13, 2018 71 value distribution, following Koutsoyiannis et al. (1988), including generalized linear  72 modelling of each parameter.  Jana Ulrich committed Nov 13, 2018 73 }  Jana Ulrich committed Feb 05, 2019 74 75 76 77 78 79 80 81 82 83 84 85 86 87 \examples{ # sampled random data from d-gev with covariates # GEV parameters: # mu = 4 + 0.2*cov1 +0.5*cov2 # sigma = 2+0.5*cov1 # 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')]) ,mul=c(1,2),sigl=1) }  Jana Ulrich committed Nov 13, 2018 88 \seealso{  89 \code{\link{dgev.d}}, \code{\link{IDF.agg}}, \code{\link{gev.fit}}, \code{\link{optim}}  Jana Ulrich committed Nov 13, 2018 90 }