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# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Library General Public
# License as published by the Free Software Foundation; either
# version 2 of the License, or (at your option) any later version.
#
# This library is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Library General Public License for more details.
#
# You should have received a copy of the GNU Library General
# Public License along with this library; if not, write to the
# Free Foundation, Inc., 59 Temple Place, Suite 330, Boston,
# MA 02111-1307 USA
################################################################################
# FUNCTION: EXTREME VALUE COPULA PARAMETER FITTING:
# evCopulaSim Simulates bivariate extreme value copula
# evCopulaFit Fits the paramter of an extreme value copula
################################################################################
################################################################################
# FUNCTION: EXTREME VALUE COPULA PARAMETER FITTING:
# evCopulaSim Simulates bivariate extreme value copula
# evCopulaFit Fits the paramter of an extreme value copula
evCopulaSim =
function(n, param = NULL, type = evList())
{ # A function implemented by Diethelm Wuertz
# Description:
# Simulates bivariate extreme value Copula
# FUNCTION:
# Match Arguments:
type = match.arg(type)
# Settings:
if (is.null(param)) param = evParam(type)$param
# Random Variates:
ans = revCopula(n = n, param = param, type = type)
# Return Value:
ans
}
# ------------------------------------------------------------------------------
evCopulaFit =
function(u, v = NULL, type = evList(), ...)
{ # A function implemented by Diethelm Wuertz
# Description:
# Fits the paramter of an elliptical copula
# Note:
# The upper limit for nu is 100
# FUNCTION:
# Match Arguments:
type = match.arg(type)
# Settings:
U <<- u
V <<- v
if (is.list(u)) {
U <<- u[[1]]
V <<- u[[2]]
}
if (is.matrix(u)) {
U = u[, 1]
V = u[, 2]
}
# Start Values:
param = evParam(type)$param
range = evRange(type)
paramLength = length(param)
# Log-Likelihood Function:
.fun = function(x, type) {
-mean( log(devCopula(u = U, v = V, param = x, type = type)) )
}
if (paramLength == 1) {
# We have only one parameter to optimize ...
fit = optimize(f = .fun, lower = range[1], upper = range[2],
maximum = FALSE, tol = .Machine$double.eps^0.25,
type = type, ...)
} else {
# Log-Likelihood Function:
range = evRange(type)
fit = nlminb(start = param, objective = .fun,
lower = range[1], upper = range[2], type = type, ...)
}
# Return Value:
fit
}
################################################################################
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