Mercurial > hg > octave-nkf
view scripts/signal/diffpara.m @ 20788:7374a3a6d594
use new string_value method to handle value extraction errors
* urlwrite.cc: Use new string_value method.
author | John W. Eaton <jwe@octave.org> |
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date | Thu, 08 Oct 2015 17:26:40 -0400 |
parents | f1d0f506ee78 |
children |
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## Copyright (C) 1995-2015 Friedrich Leisch ## ## This file is part of Octave. ## ## Octave is free software; you can redistribute it and/or modify it ## under the terms of the GNU General Public License as published by ## the Free Software Foundation; either version 3 of the License, or (at ## your option) any later version. ## ## Octave 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 ## General Public License for more details. ## ## You should have received a copy of the GNU General Public License ## along with Octave; see the file COPYING. If not, see ## <http://www.gnu.org/licenses/>. ## -*- texinfo -*- ## @deftypefn {Function File} {[@var{d}, @var{dd}] =} diffpara (@var{x}, @var{a}, @var{b}) ## Return the estimator @var{d} for the differencing parameter of an ## integrated time series. ## ## The frequencies from @math{[2*pi*a/t, 2*pi*b/T]} are used for the ## estimation. If @var{b} is omitted, the interval ## @math{[2*pi/T, 2*pi*a/T]} is used. If both @var{b} and @var{a} are omitted ## then @math{a = 0.5 * sqrt (T)} and @math{b = 1.5 * sqrt (T)} is used, where ## @math{T} is the sample size. If @var{x} is a matrix, the differencing ## parameter of each column is estimated. ## ## The estimators for all frequencies in the intervals described above is ## returned in @var{dd}. ## ## The value of @var{d} is simply the mean of @var{dd}. ## ## Reference: @nospell{P.J. Brockwell & R.A. Davis}. @cite{Time Series: ## Theory and Methods}. Springer 1987. ## @end deftypefn ## Author: FL <Friedrich.Leisch@ci.tuwien.ac.at> ## Description: Estimate the fractional differencing parameter function [d, dd] = diffpara (x, a, b) if (nargin < 1 || nargin > 3) print_usage (); endif if (isvector (x)) n = length (x); k = 1; x = reshape (x, n, 1); else [n, k] = size (x); endif if (nargin == 1) a = 0.5 * sqrt (n); b = 1.5 * sqrt (n); elseif (nargin == 2) b = a; a = 1; endif if (! (isscalar (a) && isscalar (b))) error ("diffpara: A and B must be scalars"); endif dd = zeros (b - a + 1, k); for l = 1:k w = 2 * pi * (1 : n-1) / n; x = 2 * log (abs (1 - exp (-i*w))); y = log (periodogram (x(2:n,l))); x = center (x); y = center (y); for m = a:b dd(m-a+1) = - x(1:m) * y(1:m) / sumsq (x(1:m)); endfor endfor d = mean (dd); endfunction