You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
</style></head><body><divclass="content"><h1>ARFITCAPS</h1><!--introduction--><pre> Capsule function to call the arfit.m routine, part of
71
+
"ARfit: Multivariate Autoregressive Model Fitting" package.</pre><!--/introduction--><h2>Contents</h2><div><ul><li><ahref="#1">Syntax:</a></li><li><ahref="#2">Input Arguments</a></li><li><ahref="#3">Output Arguments</a></li><li><ahref="#4">Description:</a></li><li><ahref="#5">Note: As described by the authors, acf.m in ARfit needs Signal Processing</a></li><li><ahref="#6">See also: ARFIT, MVAR, MCARNS, MCARVM, CMLSM</a></li></ul></div><h2id="1">Syntax:</h2><pre> [pf,A,ef] = ARFITCAPS(u,IP)</pre><h2id="2">Input Arguments</h2><preclass="language-matlab">u: time series
A: AR estimate <spanclass="string">matrix</span><spanclass="string">provided</span><spanclass="string">by</span><spanclass="string">ARFIT</span><spanclass="string">routine</span>
</pre><h2id="4">Description:</h2><p>ARFITCAPS is capsule that calls arfit.m and arres.m routines, part of Autoregressive Model Fitting" package, which implements algorithms as described in the following articles:</p><pre> [1] Neumaier A & Schneider T, 2001. Estimation of parameters and
77
+
eigenmodes of multivariate autoregressive models. ACM Trans Math
78
+
Softw, 27:27-57.
79
+
[2] Schneider T & Neumaier A, 2001. Algorithm 808: ARfit - A Matlab
80
+
package for the estimation of parameters and eigenmodes of multivariate
81
+
autoregressive models. ACM Trans Math Softw, 27:58-65.</pre><pre>If you are interested in using ARfit algorithm for VAR model estimation, we
82
+
advise you to get the software from Tapio Schneider's website at
83
+
https://climate-dynamics.org/software/#arfit
84
+
or from Mathworks.com File Exchange site (verified on August 27, 2021, but
85
+
not tested)
86
+
https://www.mathworks.com/matlabcentral/fileexchange/174-arfit,</pre><pre>and, before using it, verify the license terms, it seems to be a copyrighted
87
+
material by the Association for Computing Machinery, Inc.</pre><h2id="5">Note: As described by the authors, acf.m in ARfit needs Signal Processing</h2><pre>Toolbox (TM), as it requires XCORR, a cross-correlation function estimator.</pre><pre>ARfit availability was checked on August 13, 2015, and August 27, 2021. KS</pre><pre>The version we have tested and included was obtained on February 24, 2011 from
88
+
www.gps.caltech.edu/~tapio/arfit/index.html,
89
+
which is now obsolete.</pre><h2id="6">See also: ARFIT, MVAR, MCARNS, MCARVM, CMLSM</h2><preclass="codeinput"><spanclass="comment">% (C) Koichi Sameshima & Luiz A. Baccalá, 2022.</span>
90
+
<spanclass="comment">% See file license.txt in installation directory for licensing terms.</span>
</style></head><body><divclass="content"><h1>ASYMP_DTF</h1><!--introduction--><pre> Compute DTF connectivity measures magnitude, from series j-->i, for
71
-
any of three of metrics -- Euclidean, diagonal and information --
70
+
</style></head><body><divclass="content"><h1>ASYMP_DTF</h1><!--introduction--><pre> Compute DTF connectivity measures magnitude, from series j-->i, for
71
+
any of three of metrics --- Euclidean, diagonal and information ---
72
72
as well as asymptotic statistics from vector autoregressive (VAR)
73
73
coefficients in the frequency domain.</pre><!--/introduction--><h2>Contents</h2><div><ul><li><ahref="#1">Syntax:</a></li><li><ahref="#2">Input Arguments:</a></li><li><ahref="#3">Output Arguments:</a></li><li><ahref="#4">Description:</a></li><li><ahref="#5">Example:</a></li><li><ahref="#6">References:</a></li><li><ahref="#7">See also: DTF_ALG, ASYMP_PDC, MVAR, MCARNS, MCARVM, CMLSM, ARFIT</a></li><li><ahref="#9">Change Log:</a></li></ul></div><h2id="1">Syntax:</h2><pre> c = ASYMP_DTF(u,A,pf,nFreqs,metric,alpha)</pre><h2id="2">Input Arguments:</h2><pre> u - multiple row vectors time series
74
74
A - AR estimate matrix obtained via MVAR routine
@@ -560,8 +560,8 @@
560
560
</pre><pclass="footer"><br><ahref="https://www.mathworks.com/products/matlab/">Published with MATLAB® R2020a</a><br></p></div><!--
561
561
##### SOURCE BEGIN #####
562
562
%% ASYMP_DTF
563
-
% Compute DTF connectivity measures magnitude, from series jREPLACE_WITH_DASH_DASH>i, for
564
-
% any of three of metrics REPLACE_WITH_DASH_DASH Euclidean, diagonal and information REPLACE_WITH_DASH_DASH
563
+
% Compute DTF connectivity measures magnitude, from series jREPLACE_WITH_DASH_DASH>i, for
564
+
% any of three of metrics REPLACE_WITH_DASH_DASH- Euclidean, diagonal and information REPLACE_WITH_DASH_DASH-
565
565
% as well as asymptotic statistics from vector autoregressive (VAR)
0 commit comments