/usr/share/octave/packages/nan-2.8.1/decovm.m is in octave-nan 2.8.1-1ubuntu2.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | function [mu,sd,COV,xc,M,R2]=decovm(XCN,NN)
% decompose extended covariance matrix into mean (mu),
% standard deviation, the (pure) Covariance (COV),
% correlation (xc) matrix and the correlation coefficients R2.
% NaN's are condsidered as missing values.
% [mu,sd,COV,xc,N,R2]=decovm(ECM[,NN])
%
% ECM is the extended covariance matrix
% NN is the number of elements, each estimate (in ECM) is based on
%
% see also: MDBC, COVM, R2
% $Id: decovm.m 2140 2009-07-02 12:03:55Z schloegl $
% Copyright (c) 1999-2002,2009 by Alois Schloegl
% This function is part of the NaN-toolbox
% http://pub.ist.ac.at/~schloegl/matlab/NaN/
% This program 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.
%
% This program 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 this program; if not, write to the Free Software
% Foundation, Inc., 51 Franklin Street - Fifth Floor, Boston, MA 02110-1301, USA.
[r,c]=size(XCN);
if r~=c,
fprintf(2,'Warning DECOVM: input argument is not a square matrix\n');
XCN = ecovm(XCN);
c = c + 1;
else
M = XCN(1,1);
if nargin<2,
XCN = XCN/(XCN(1,1));
else %if nargin==2
XCN = XCN./(NN);
end;
if any(isnan(XCN(:))),
warning('DECOVM: Extended Covariance Matrix should not contain NaN''s');
end;
if 0, %det(XCN)<0; % check removed for performance reasons
warning('DECOVM: Extended Covariance Matrix must be non-negative definite');
end;
end;
mu = XCN(1,2:c);
COV = XCN(2:c,2:c) - mu'*mu;
sd = sqrt(diag(COV))';
if nargout<4, return; end;
xc = COV./(sd'*sd);
M = XCN(1,1);
if nargout<6, return; end;
R2 = xc.*xc;
return;
mu=XCN(2:N,1)/XCN(1,1);
COV=(XCN(2:N,2:N)/XCN(1,1)-XCN(2:N,1)*XCN(1,2:N)/XCN(1,1)^2);
sd=sqrt(diag(COV));
xc=COV./(sd*sd');
% function [ECM] = ecovm(signal);
% Generates extended Covariance matrix,
% ECM= [l signal]'*[l signal]; % l is a matching column of 1's
% ECM is additive, i.e. it can be applied to subsequent blocks and summed up afterwards
% [ECM1] = ecovm(s1);
% [ECM2] = ecovm(s1);
% [ECM] = ecovm([s1;s2]);
% ECM1+ECM2==ECM;
%
% SS=sum(signal); ECM=[[size(signal,1),SS];[SS',signal'*signal]];
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