?? moment.m
字號:
function M=moment(i,p,opt,DIM)
% MOMENT estimates the p-th moment
%
% M = moment(x, p [,opt] [,DIM])
% M = moment(H, p [,opt])
% calculates p-th central moment from data x in dimension DIM
% of from Histogram H
%
% p moment of order p
% opt 'ac': absolute 'a' and/or central ('c') moment
% DEFAULT: '' raw moments are estimated
% 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: STD, VAR, SKEWNESS, KURTOSIS, STATISTIC,
%
% REFERENCE(S):
% http://mathworld.wolfram.com/Moment.html
% 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, write to the Free Software
% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
% Version 1.24; 09 Dec 2002
% Copyright (c) 2000-2002 by Alois Schloegl <a.schloegl@ieee.org>
if nargin==2,
DIM=[];
opt=[];
elseif nargin==3,
DIM=[];
elseif nargin==4,
else
fprintf('Error MOMENT: invalid number of arguments\n');
return;
end;
if p<=0;
fprintf('Error MOMENT: invalid model order p=%f\n',p);
return;
end;
if isnumeric(opt) | ~isnumeric(DIM),
tmp = DIM;
DIM = opt;
opt = tmp;
end;
if isempty(opt),
opt='r';
end;
if isempty(DIM),
DIM = min(find(size(i)>1));
if isempty(DIM), DIM=1; end;
end;
N = nan;
if isstruct(i),
if isfield(i,'HISTOGRAM'),
sz = size(i.H)./size(i.X);
X = repmat(i.X,sz);
if any(opt=='c'),
N = sumskipnan(i.H,1); % N
N = max(N-1,0); % for unbiased estimation
S = sumskipnan(i.H.*X,1); % sum
X = X - repmat(S./N, size(X)./size(S)); % remove mean
end;
if any(opt=='a'),
X = abs(X);
end;
[M,n] = sumskipnan(X.^p.*i.H,1);
else
warning('invalid datatype')
end;
else
if any(opt=='c'),
[S,N] = sumskipnan(i,DIM); % gemerate N and SUM
N = max(N-1,0); % for unbiased estimation
i = i - repmat(S./N, size(i)./size(S)); % remove mean
end;
if any(opt=='a'),
i = abs(i);
end;
[M,n] = sumskipnan(i.^p,DIM);
end;
if isnan(N), N=n; end;
M = M./N;
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