function [R]=hist2res2(H,fun)% Evaluates Histogram data% [R]=hist2res2(H)%% [y]=hist2res2(H,fun)%	estimates fun-statistic%% fun	'mean'	mean%	'std'	standard deviation%	'var'	variance%	'sem'	standard error of the mean%	'rms'	root mean square%	'meansq' mean of squares%	'sum'	sum%	'sumsq'	sum of squares%	'CM#'	central moment of order #%	'skewness' skewness %	'kurtosis' excess coefficient (Fisher kurtosis)%% see also: NaN/statistic%% REFERENCES:% [1] C.L. Nikias and A.P. Petropulu "Higher-Order Spectra Analysis" Prentice Hall, 1993.% [2] C.E. Shannon and W. Weaver "The mathematical theory of communication" University of Illinois Press, Urbana 1949 (reprint 1963).% [3] http://www.itl.nist.gov/% [4] http://mathworld.wolfram.com/%	Version 2.90%	last revision 02 Apr 2002%	Copyright (c) 1996-2002 by Alois Schloegl%	e-mail: a.schloegl@ieee.org	% .CHANGELOG% 20.09.2001  calc of Quantiles improved, using FLIX.M    % 16.02.2002  major revision, compatible to NaN/statistic % 01.03.2002  minor bug fix, works for HISTO3 and HISTO2 results % 02.03.2002  global FLAG_implicit_unbiased_estimation implemented% 02.04.2002  bug fixedif ~strcmp(H.datatype,'HISTOGRAM')        fprintf(2,'ERROR: arg1 is not a histogram\n');        return;end;if nargin<2, fun=[]; end;global FLAG_implicit_unbiased_estimation; %%% check whether FLAG was already defined if exist('FLAG_implicit_unbiased_estimation')~=1,	FLAG_implicit_unbiased_estimation=[];end;%%% set DEFAULT value of FLAGif isempty(FLAG_implicit_unbiased_estimation),	FLAG_implicit_unbiased_estimation=logical(1);end;sz 	= size(H.H)./size(H.X);R.N 	= sumskipnan(H.H,1);R.SUM 	= sumskipnan(H.H.*repmat(H.X,sz),1);R.SSQ 	= sumskipnan(H.H.*repmat(H.X.*H.X,sz),1);%R.S3P 	= sumskipnan(H.H.*repmat(H.X.^3,sz),1);	% sum of 3rd powerR.S4P 	= sumskipnan(H.H.*repmat(H.X.^4,sz),1);	% sum of 4th power%R.S5P 	= sumskipnan(H.H.*repmat(H.X.^5,sz),1);	% sum of 5th powerR.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 removedif FLAG_implicit_unbiased_estimation,    n1 	= max(R.N-1,0);			% in case of n=0 and n=1, the (biased) variance, STD and STE are INFelse    n1	= R.N;end;R.VAR  	= R.SSQ0./n1;	     		% variance (unbiased) R.STD  	= sqrt(R.VAR);		     	% standard deviationR.SEM  	= sqrt(R.SSQ0./(R.N.*n1)); 	% standard error of the meanR.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 varianceR.Coefficient_of_variation = R.STD./R.MEAN;R.CM2	= R.SSQ0./n1;x       = repmat(H.X,sz) - repmat(R.MEAN,size(H.X,1),1);R.CM3 	= sumskipnan(H.H.*(x.^3),1)./n1;R.CM4 	= sumskipnan(H.H.*(x.^4),1)./n1;%R.CM5 	= sumskipnan(H.H.*(x.^5),1)./n1;R.SKEWNESS = R.CM3./(R.STD.^3);R.KURTOSIS = R.CM4./(R.VAR.^2)-3;R.MAD = sumskipnan(H.H.*abs(x),1)./R.N; % mean absolute deviationH.PDF = H.H./H.N(ones(size(H.H,1),1),:);R.ENTROPY = -sumskipnan(H.PDF.*log2(H.PDF),1);if ~isempty(fun),        fun = upper(fun);        if strncmp(fun,'CM',2)                 oo = str2double(fun(3:length(fun)));                R = sumskipnan(H.PDF.*(x.^oo),1);    	else	            		R = getfield(R,fun);	end;end;