/usr/share/octave/packages/3.2/nan-2.4.4/meandev.m is in octave-nan 2.4.4-1.
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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 | function R = meandev(i,DIM)
% MEANDEV estimates the Mean deviation
% (note that according to [1,2] this is the mean deviation;
% not the mean absolute deviation)
%
% y = meandev(x,DIM)
% calculates the mean deviation of x in dimension DIM
%
% DIM dimension
% 1: STATS of columns
% 2: STATS of rows
% default or []: first DIMENSION, with more than 1 element
%
% features:
% - can deal with NaN's (missing values)
% - dimension argument
% - compatible to Matlab and Octave
%
% see also: SUMSKIPNAN, VAR, STD, MAD
%
% REFERENCE(S):
% [1] http://mathworld.wolfram.com/MeanDeviation.html
% [2] L. Sachs, "Applied Statistics: A Handbook of Techniques", Springer-Verlag, 1984, page 253.
% [3] http://mathworld.wolfram.com/MeanAbsoluteDeviation.html
% [4] Kenney, J. F. and Keeping, E. S. "Mean Absolute Deviation." ยง6.4 in Mathematics of Statistics, Pt. 1, 3rd ed. Princeton, NJ: Van Nostrand, pp. 76-77 1962.
% 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 2 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/>.
% $Id: meandev.m 8223 2011-04-20 09:16:06Z schloegl $
% Copyright (C) 2000-2002,2010 by Alois Schloegl <alois.schloegl@gmail.com>
% This function is part of the NaN-toolbox for Octave and Matlab
% http://pub.ist.ac.at/~schloegl/matlab/NaN/
if nargin==1,
DIM = find(size(i)>1,1);
if isempty(DIM), DIM=1; end;
end;
[S,N] = sumskipnan(i,DIM); % sum
i = i - repmat(S./N,size(i)./size(S)); % remove mean
[S,N] = sumskipnan(abs(i),DIM); %
%if flag_implicit_unbiased_estim; %% ------- unbiased estimates -----------
n1 = max(N-1,0); % in case of n=0 and n=1, the (biased) variance, STD and STE are INF
%else
% n1 = N;
%end;
R = S./n1;
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