swh:1:snp:d1587d616651317fdcebcbb237dce82c32266449
Tip revision: 944271d20ffa4fb36a171791c34afaae5325f74a authored by Rmetrics Core Team on 08 February 2010, 00:00:00 UTC
version 2110.79
version 2110.79
Tip revision: 944271d
dist-sghFit.R
# 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: GENERALIZED DISTRIBUTION:
# sghFit Fits parameters of a standardized GH density
################################################################################
sghFit <-
function(x, zeta = 1, rho = 0, lambda = 1, include.lambda = TRUE,
scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE,
title = NULL, description = NULL, ...)
{
x.orig = x
x = as.vector(x)
if (scale) x = (x-mean(x)) / sd(x)
eps = .Machine$double.eps^0.5
BIG = 1000
if (include.lambda)
{
# LLH Function:
obj.include = function(x, y = x, trace) {
f = -sum(log(dsgh(y, x[1], x[2], x[3], log = FALSE)))
if (trace) {
cat("\n Objective Function Value: ", -f)
cat("\n Parameter Estimates: ", x[1], x[2], x[3], "\n")
}
f
}
# LLH Optimization:
r = nlminb(
start = c(zeta, rho, lambda),
objective = obj.include,
lower = c(eps, -0.9999, -2),
upper = c(BIG, +0.9999, +5),
y = x,
trace = trace)
names(r$par) <- c("zeta", "rho", "lambda")
} else {
# LLH Function:
obj = function(x, y = x, lambda, trace) {
f = -sum(log(dsgh(y, x[1], x[2], lambda, log = FALSE)))
if (trace) {
cat("\n Objective Function Value: ", -f)
cat("\n Parameter Estimates: ", x[1], x[2], "\n")
}
f
}
# LLH Optimization:
r = nlminb(
start = c(zeta, rho),
objective = obj,
lower = c(eps, -0.9999),
upper = c(BIG, +0.9999),
y = x,
lambda = lambda,
trace = trace)
r$par = c(r$par, lambda)
names(r$par) <- c("zeta", "rho", "fix.lambda")
}
param = .paramGH(r$par[1], r$par[2], r$par[3])
if (trace) {
cat("\n Standardized Parameters:", "\n ")
print(r$par)
names(param) = c("alpha", "beta", "delta", "mu")
cat("\n 1st Parameterization:", "\n ")
print(param)
}
# Default Title and Description:
if (is.null(title))
title = "SGH Parameter Estimation"
if (is.null(description))
description = description()
# Fit:
fit = list(
estimate = r$par,
minimum = -r$objective,
code = r$convergence,
param = param,
mean = mean(x.orig),
var = var(x.orig))
# Optional Plot:
if (doplot) {
if (span == "auto") span = seq(min(x), max(x), length = 101)
z = density(x, n = 100, ...)
x = z$x[z$y > 0]
y = z$y[z$y > 0]
y.points = dsgh(span, zeta = r$par[1], rho = r$par[2], lambda)
ylim = log(c(min(y.points), max(y.points)))
plot(x, log(y), xlim = c(span[1], span[length(span)]),
ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...)
title(main = title)
lines(x = span, y = log(y.points), col = "steelblue")
}
# Return Value:
new("fDISTFIT",
call = match.call(),
model = "Standarized GH Distribution",
data = as.data.frame(x.orig),
fit = fit,
title = as.character(title),
description = description())
}
################################################################################