/usr/share/octave/packages/3.2/tsa-4.1.0/y2res.m is in octave-tsa 4.1.0+svn20110501-1.
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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 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | function [R]=y2res(Y)
% Y2RES evaluates basic statistics of a data series
%
% R = y2res(y)
% several statistics are estimated from each column of y
%
% OUTPUT:
% R.N number of samples, NaNs are not counted
% R.SUM sum of samples
% R.MEAN mean
% R.STD standard deviation
% R.VAR variance
% R.Max Maximum
% R.Min Minimum
% ... and many more including:
% MEDIAN, Quartiles, Variance, standard error of the mean (SEM),
% Coefficient of Variation, Quantization (QUANT), TRIMEAN, SKEWNESS,
% KURTOSIS, Root-Mean-Square (RMS), ENTROPY
%
% $Id: y2res.m 5090 2008-06-05 08:12:04Z schloegl $
% Copyright (C) 1996-2005,2008 by Alois Schloegl <a.schloegl@ieee.org>
% This is part of the TSA-toolbox
% http://octave.sourceforge.net/
% http://www.dpmi.tugraz.at/~schloegl/matlab/tsa/
%
% 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, see <http://www.gnu.org/licenses/>.
[R.SUM, R.N, R.SSQ] = sumskipnan(Y,1);
%R.S3P = sumskipnan(Y.^3,1);
R.S4P = sumskipnan(Y.^4,1);
%R.S5P = sumskipnan(Y.^5,1);
R.MEAN = R.SUM./R.N;
R.MSQ = R.SSQ./R.N;
R.RMS = sqrt(R.MSQ);
R.SSQ0 = R.SSQ-R.SUM.*R.MEAN; % sum square of mean removed
if 1,%flag_implicit_unbiased_estim,
n1 = max(R.N-1,0); % in case of n=0 and n=1, the (biased) variance, STD and STE are INF
else
n1 = R.N;
end;
R.VAR = R.SSQ0./n1; % variance (unbiased)
R.STD = sqrt(R.VAR); % standard deviation
R.SEM = sqrt(R.SSQ0./(R.N.*n1)); % standard error of the mean
R.SEV = sqrt(n1.*(n1.*R.S4P./R.N+(R.N.^2-2*R.N+3).*(R.SSQ./R.N).^2)./(R.N.^3)); % standard error of the variance
R.Coefficient_of_variation = R.STD./R.MEAN;
R.CM2 = R.SSQ0./n1;
R.Max = max(Y,[],1);
R.Min = min(Y,[],1);
%R.NormEntropy = log2(sqrt(2*pi*exp(1)))+log2(R.STD);
Q0500=repmat(nan,1,size(Y,2));
Q0250=Q0500;
Q0750=Q0500;
%MODE=Q0500;
for k = 1:size(Y,2),
tmp = sort(Y(:,k));
Q0250(k) = flix(tmp,R.N(k)/4 + 0.75);
Q0500(k) = flix(tmp,R.N(k)/2 + 0.50);
Q0750(k) = flix(tmp,R.N(k)*3/4 + 0.25);
tmp = diff(tmp);
pdf = diff([0; find(tmp>0); R.N(k)])/R.N(k); % empirical probability distribution
R.ENTROPY(k) = -sumskipnan(pdf.*log(pdf));
tmp = tmp(find(tmp));
q = min(tmp);
qerror = 0;
if isempty(q),
q = NaN;
else
tmp = tmp/q;
qerror = max(abs(tmp-round(tmp)));
end;
R.QUANT(k) = q;
R.Qerror(k) = qerror;
end;
if any(R.Qerror*1e6>R.QUANT)
warning('(Y2RES) Quantization might not be equidistant')
end;
R.MEDIAN = Q0500;
R.Quartiles = [Q0250; Q0750];
% R.IQR = H_spread = [Q0750 - Q0250];
R.TRIMEAN = [Q0250 + 2*Q0500 + Q0750]/4;
Y = Y - repmat(R.MEAN,size(Y)./size(R.MEAN));
R.CM3 = sumskipnan(Y.^3,1)./n1;
R.CM4 = sumskipnan(Y.^4,1)./n1;
%R.CM5 = sumskipnan(Y.^5,1)./n1;
R.SKEWNESS = R.CM3./(R.STD.^3);
R.KURTOSIS = R.CM4./(R.VAR.^2)-3;
%R.Skewness.Fisher = (R.CM3)./(R.STD.^3); %%% same as R.SKEWNESS
%R.Skewness.Pearson_Mode = (R.MEAN-R.MODE)./R.STD;
%R.Skewness.Pearson_coeff1 = (3*R.MEAN-R.MODE)./R.STD;
R.Skewness.Pearson_coeff2 = (3*R.MEAN-R.MEDIAN)./R.STD;
R.Skewness.Bowley = (Q0750+Q0250 - 2*Q0500)./(Q0750-Q0250); % quartile skewness coefficient
R.datatype = 'STAT Level 4';
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