Verified Commit 4e314a52 authored by Sébastien Villemot's avatar Sébastien Villemot
Browse files

Bump minimal required Octave version to 4.4

parent fd99f4fe
...@@ -187,12 +187,6 @@ Copyright: 2013-2019 Ben Abbott ...@@ -187,12 +187,6 @@ Copyright: 2013-2019 Ben Abbott
2019 Dynare Team 2019 Dynare Team
License: GPL-3+ License: GPL-3+
Files: matlab/missing/corrcoef/corrcoef.m matlab/missing/corrcoef/sumskipnan.m
matlab/missing/corrcoef/flag_implicit_skip_nan.m matlab/missing/corrcoef/tcdf.m
Copyright: 2000-2005,2008,2009,2011 by Alois Schloegl <alois.schloegl@gmail.com>
2014 Dynare Team
License: GPL-3+
Files: matlab/lmmcp/catstruct.m Files: matlab/lmmcp/catstruct.m
Copyright: 2005 Jos van der Geest <jos@jasen.nl> Copyright: 2005 Jos van der Geest <jos@jasen.nl>
2013 Christophe Gouel 2013 Christophe Gouel
......
...@@ -78,14 +78,14 @@ if isoctave ...@@ -78,14 +78,14 @@ if isoctave
'of precompiled mex files and some\nfeatures, like solution ' ... 'of precompiled mex files and some\nfeatures, like solution ' ...
'of models approximated at third order, will not be available.'], supported_octave_version()) 'of models approximated at third order, will not be available.'], supported_octave_version())
skipline() skipline()
elseif octave_ver_less_than('4.2') % Should match the test in mex/build/octave/configure.ac elseif octave_ver_less_than('4.4') % Should match the test in mex/build/octave/configure.ac
% and in m4/ax_mexopts.m4
skipline() skipline()
warning(['This version of Dynare has only been tested on Octave 4.2 and above. Dynare may fail to run or give unexpected result. Consider upgrading your version of Octave.']) warning(['This version of Dynare has only been tested on Octave 4.4 and above. Dynare may fail to run or give unexpected result. Consider upgrading your version of Octave.'])
skipline() skipline()
end end
else else
if matlab_ver_less_than('7.9') % Should match the test in mex/build/matlab/configure.ac if matlab_ver_less_than('7.9') % Should match the test in mex/build/matlab/configure.ac
% and in m4/ax_mexopts.m4
skipline() skipline()
warning('This version of Dynare has only been tested on MATLAB 7.9 (R2009b) and above. Since your MATLAB version is older than that, Dynare may fail to run, or give unexpected results. Consider upgrading your MATLAB installation, or switch to Octave.'); warning('This version of Dynare has only been tested on MATLAB 7.9 (R2009b) and above. Since your MATLAB version is older than that, Dynare may fail to run, or give unexpected results. Consider upgrading your MATLAB installation, or switch to Octave.');
skipline() skipline()
......
...@@ -86,11 +86,6 @@ if isoctave && octave_ver_less_than('5') ...@@ -86,11 +86,6 @@ if isoctave && octave_ver_less_than('5')
p{end+1} = '/missing/ordeig'; p{end+1} = '/missing/ordeig';
end end
% corrcoef with two outputs is missing in Octave < 4.4 (ticket #796)
if isoctave && octave_ver_less_than('4.4') && ~user_has_octave_forge_package('nan')
p{end+1} = '/missing/corrcoef';
end
%% intersect(…, 'stable') doesn't exist in Octave and in MATLAB < R2013a %% intersect(…, 'stable') doesn't exist in Octave and in MATLAB < R2013a
if isoctave || matlab_ver_less_than('8.1') if isoctave || matlab_ver_less_than('8.1')
p{end+1} = '/missing/intersect_stable'; p{end+1} = '/missing/intersect_stable';
...@@ -98,10 +93,10 @@ end ...@@ -98,10 +93,10 @@ end
% Replacements for functions of the MATLAB statistics toolbox % Replacements for functions of the MATLAB statistics toolbox
if isoctave if isoctave
% These functions were part of Octave < 4.4, they are now in the statistics Forge package % Under Octave, these functions are in the statistics Forge package.
if ~octave_ver_less_than('4.4') && ~user_has_octave_forge_package('statistics') % Our replacement functions don't work under Octave (because of gamrnd, see
% Our replacement functions don't work under Octave (because of gamrnd, see % #1638), hence the statistics toolbox is now a hard requirement
% #1638), hence the statistics toolbox is now a hard requirement if ~user_has_octave_forge_package('statistics')
error('You must install the "statistics" package from Octave Forge, either with your distribution package manager or with "pkg install -forge statistics"') error('You must install the "statistics" package from Octave Forge, either with your distribution package manager or with "pkg install -forge statistics"')
end end
else else
......
function [R,sig,ci1,ci2,nan_sig] = corrcoef(X,Y,varargin)
% CORRCOEF calculates the correlation matrix from pairwise correlations.
% The input data can contain missing values encoded with NaN.
% Missing data (NaN's) are handled by pairwise deletion [15].
% In order to avoid possible pitfalls, use case-wise deletion or
% or check the correlation of NaN's with your data (see below).
% A significance test for testing the Hypothesis
% 'correlation coefficient R is significantly different to zero'
% is included.
%
% [...] = CORRCOEF(X);
% calculates the (auto-)correlation matrix of X
% [...] = CORRCOEF(X,Y);
% calculates the crosscorrelation between X and Y
%
% [...] = CORRCOEF(..., Mode);
% Mode='Pearson' or 'parametric' [default]
% gives the correlation coefficient
% also known as the 'product-moment coefficient of correlation'
% or 'Pearson''s correlation' [1]
% Mode='Spearman' gives 'Spearman''s Rank Correlation Coefficient'
% This replaces SPEARMAN.M
% Mode='Rank' gives a nonparametric Rank Correlation Coefficient
% This is the "Spearman rank correlation with proper handling of ties"
% This replaces RANKCORR.M
%
% [...] = CORRCOEF(..., param1, value1, param2, value2, ... );
% param value
% 'Mode' type of correlation
% 'Pearson','parametric'
% 'Spearman'
% 'rank'
% 'rows' how do deal with missing values encoded as NaN's.
% 'complete': remove all rows with at least one NaN
% 'pairwise': [default]
% 'alpha' 0.01 : significance level to compute confidence interval
%
% [R,p,ci1,ci2,nansig] = CORRCOEF(...);
% R is the correlation matrix
% R(i,j) is the correlation coefficient r between X(:,i) and Y(:,j)
% p gives the significance of R
% It tests the null hypothesis that the product moment correlation coefficient is zero
% using Student's t-test on the statistic t = r*sqrt(N-2)/sqrt(1-r^2)
% where N is the number of samples (Statistics, M. Spiegel, Schaum series).
% p > alpha: do not reject the Null hypothesis: 'R is zero'.
% p < alpha: The alternative hypothesis 'R is larger than zero' is true with probability (1-alpha).
% ci1 lower (1-alpha) confidence interval
% ci2 upper (1-alpha) confidence interval
% If no alpha is provided, the default alpha is 0.01. This can be changed with function flag_implicit_significance.
% nan_sig p-value whether H0: 'NaN''s are not correlated' could be correct
% if nan_sig < alpha, H1 ('NaNs are correlated') is very likely.
%
% The result is only valid if the occurence of NaN's is uncorrelated. In
% order to avoid this pitfall, the correlation of NaN's should be checked
% or case-wise deletion should be applied.
% Case-Wise deletion can be implemented
% ix = ~any(isnan([X,Y]),2);
% [...] = CORRCOEF(X(ix,:),Y(ix,:),...);
%
% Correlation (non-random distribution) of NaN's can be checked with
% [nan_R,nan_sig]=corrcoef(X,isnan(X))
% or [nan_R,nan_sig]=corrcoef([X,Y],isnan([X,Y]))
% or [R,p,ci1,ci2] = CORRCOEF(...);
%
% Further recommandation related to the correlation coefficient:
% + LOOK AT THE SCATTERPLOTS to make sure that the relationship is linear
% + Correlation is not causation because
% it is not clear which parameter is 'cause' and which is 'effect' and
% the observed correlation between two variables might be due to the action of other, unobserved variables.
%
% see also: SUMSKIPNAN, COVM, COV, COR, SPEARMAN, RANKCORR, RANKS,
% PARTCORRCOEF, flag_implicit_significance
%
% REFERENCES:
% on the correlation coefficient
% [ 1] http://mathworld.wolfram.com/CorrelationCoefficient.html
% [ 2] http://www.geography.btinternet.co.uk/spearman.htm
% [ 3] Hogg, R. V. and Craig, A. T. Introduction to Mathematical Statistics, 5th ed. New York: Macmillan, pp. 338 and 400, 1995.
% [ 4] Lehmann, E. L. and D'Abrera, H. J. M. Nonparametrics: Statistical Methods Based on Ranks, rev. ed. Englewood Cliffs, NJ: Prentice-Hall, pp. 292, 300, and 323, 1998.
% [ 5] Press, W. H.; Flannery, B. P.; Teukolsky, S. A.; and Vetterling, W. T. Numerical Recipes in FORTRAN: The Art of Scientific Computing, 2nd ed. Cambridge, England: Cambridge University Press, pp. 634-637, 1992
% [ 6] http://mathworld.wolfram.com/SpearmanRankCorrelationCoefficient.html
% on the significance test of the correlation coefficient
% [11] http://www.met.rdg.ac.uk/cag/STATS/corr.html
% [12] http://www.janda.org/c10/Lectures/topic06/L24-significanceR.htm
% [13] http://faculty.vassar.edu/lowry/ch4apx.html
% [14] http://davidmlane.com/hyperstat/B134689.html
% [15] http://www.statsoft.com/textbook/stbasic.html%Correlations
% others
% [20] http://www.tufts.edu/~gdallal/corr.htm
% [21] Fisher transformation http://en.wikipedia.org/wiki/Fisher_transformation
% $Id: corrcoef.m 9387 2011-12-15 10:42:14Z schloegl $
% Copyright (C) 2000-2004,2008,2009,2011 by Alois Schloegl <alois.schloegl@gmail.com>
% Copyright (C) 2014-2017 Dynare Team
% 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, see <http://www.gnu.org/licenses/>.
% Features:
% + handles missing values (encoded as NaN's)
% + pairwise deletion of missing data
% + checks independence of missing values (NaNs)
% + parametric and non-parametric (rank) correlation
% + Pearson's correlation
% + Spearman's rank correlation
% + Rank correlation (non-parametric, Spearman rank correlation with proper handling of ties)
% + is fast, using an efficient algorithm O(n.log(n)) for calculating the ranks
% + significance test for null-hypthesis: r=0
% + confidence interval included
% - rank correlation works for cell arrays, too (no check for missing values).
% + compatible with Octave and Matlab
global FLAG_NANS_OCCURED;
NARG = nargout; % needed because nargout is not reentrant in Octave, and corrcoef is recursive
mode = [];
if nargin==1
Y = [];
Mode='Pearson';
elseif nargin==0
fprintf(2,'Error CORRCOEF: Missing argument(s)\n');
elseif nargin>1
if ischar(Y)
varg = [Y,varargin];
Y=[];
else
varg = varargin;
end
if length(varg)<1
Mode = 'Pearson';
elseif length(varg)==1
Mode = varg{1};
else
for k = 2:2:length(varg)
mode = setfield(mode,lower(varg{k-1}),varg{k});
end
if isfield(mode,'mode')
Mode = mode.mode;
end
end
end
if isempty(Mode), Mode='pearson'; end
Mode=[Mode,' '];
FLAG_WARNING = warning; % save warning status
warning('off');
[r1,c1]=size(X);
if ~isempty(Y)
[r2,c2]=size(Y);
if r1~=r2
fprintf(2,'Error CORRCOEF: X and Y must have the same number of observations (rows).\n');
return
end
NN = real(~isnan(X)')*real(~isnan(Y));
else
[r2,c2]=size(X);
NN = real(~isnan(X)')*real(~isnan(X));
end
%%%%% generate combinations using indices for pairwise calculation of the correlation
YESNAN = any(isnan(X(:))) | any(isnan(Y(:)));
if YESNAN
FLAG_NANS_OCCURED=(1==1);
if isfield(mode,'rows')
if strcmp(mode.rows,'complete')
ix = ~any([X,Y],2);
X = X(ix,:);
if ~isempty(Y)
Y = Y(ix,:);
end
YESNAN = 0;
NN = size(X,1);
elseif strcmp(mode.rows,'all')
fprintf(1,'Warning: data contains NaNs, rows=pairwise is used.');
%%NN(NN < size(X,1)) = NaN;
elseif strcmp(mode.rows,'pairwise')
%%% default
end
end
end
if isempty(Y)
IX = ones(c1)-diag(ones(c1,1));
[jx, jy ] = find(IX);
[jxo,jyo] = find(IX);
R = eye(c1);
else
IX = sparse([],[],[],c1+c2,c1+c2,c1*c2);
IX(1:c1,c1+(1:c2)) = 1;
[jx,jy] = find(IX);
IX = ones(c1,c2);
[jxo,jyo] = find(IX);
R = zeros(c1,c2);
end
if strcmp(lower(Mode(1:7)),'pearson')
% see http://mathworld.wolfram.com/CorrelationCoefficient.html
if ~YESNAN
[S,N,SSQ] = sumskipnan(X,1);
if ~isempty(Y)
[S2,N2,SSQ2] = sumskipnan(Y,1);
CC = X'*Y;
M1 = S./N;
M2 = S2./N2;
cc = CC./NN - M1'*M2;
R = cc./sqrt((SSQ./N-M1.*M1)'*(SSQ2./N2-M2.*M2));
else
CC = X'*X;
M = S./N;
cc = CC./NN - M'*M;
v = SSQ./N - M.*M; %max(N-1,0);
R = cc./sqrt(v'*v);
end
else
if ~isempty(Y)
X = [X,Y];
end
for k = 1:length(jx)
%ik = ~any(isnan(X(:,[jx(k),jy(k)])),2);
ik = ~isnan(X(:,jx(k))) & ~isnan(X(:,jy(k)));
[s,n,s2] = sumskipnan(X(ik,[jx(k),jy(k)]),1);
v = (s2-s.*s./n)./n;
cc = X(ik,jx(k))'*X(ik,jy(k));
cc = cc/n(1) - prod(s./n);
%r(k) = cc./sqrt(prod(v));
R(jxo(k),jyo(k)) = cc./sqrt(prod(v));
end
end
elseif strcmp(lower(Mode(1:4)),'rank')
% see [ 6] http://mathworld.wolfram.com/SpearmanRankCorrelationCoefficient.html
if ~YESNAN
if isempty(Y)
R = corrcoef(ranks(X));
else
R = corrcoef(ranks(X),ranks(Y));
end
else
if ~isempty(Y)
X = [X,Y];
end
for k = 1:length(jx)
%ik = ~any(isnan(X(:,[jx(k),jy(k)])),2);
ik = ~isnan(X(:,jx(k))) & ~isnan(X(:,jy(k)));
il = ranks(X(ik,[jx(k),jy(k)]));
R(jxo(k),jyo(k)) = corrcoef(il(:,1),il(:,2));
end
X = ranks(X);
end
elseif strcmp(lower(Mode(1:8)),'spearman')
% see [ 6] http://mathworld.wolfram.com/SpearmanRankCorrelationCoefficient.html
if ~isempty(Y)
X = [X,Y];
end
n = repmat(nan,c1,c2);
if ~YESNAN
iy = ranks(X); % calculates ranks;
for k = 1:length(jx)
[R(jxo(k),jyo(k)),n(jxo(k),jyo(k))] = sumskipnan((iy(:,jx(k)) - iy(:,jy(k))).^2); % NN is the number of non-missing values
end
else
for k = 1:length(jx)
%ik = ~any(isnan(X(:,[jx(k),jy(k)])),2);
ik = ~isnan(X(:,jx(k))) & ~isnan(X(:,jy(k)));
il = ranks(X(ik,[jx(k),jy(k)]));
% NN is the number of non-missing values
[R(jxo(k),jyo(k)),n(jxo(k),jyo(k))] = sumskipnan((il(:,1) - il(:,2)).^2);
end
X = ranks(X);
end
R = 1 - 6 * R ./ (n.*(n.*n-1));
elseif strcmp(lower(Mode(1:7)),'partial')
fprintf(2,'Error CORRCOEF: use PARTCORRCOEF \n',Mode);
return
elseif strcmp(lower(Mode(1:7)),'kendall')
fprintf(2,'Error CORRCOEF: mode ''%s'' not implemented yet.\n',Mode);
return
else
fprintf(2,'Error CORRCOEF: unknown mode ''%s''\n',Mode);
end
if (NARG<2)
warning(FLAG_WARNING); % restore warning status
return
end
% CONFIDENCE INTERVAL
if isfield(mode,'alpha')
alpha = mode.alpha;
elseif exist('flag_implicit_significance','file')
alpha = flag_implicit_significance;
else
alpha = 0.01;
end
% fprintf(1,'CORRCOEF: confidence interval is based on alpha=%f\n',alpha);
% SIGNIFICANCE TEST
R(isnan(R))=0;
tmp = 1 - R.*R;
tmp(tmp<0) = 0; % prevent tmp<0 i.e. imag(t)~=0
t = R.*sqrt(max(NN-2,0)./tmp);
if exist('t_cdf','file')
sig = t_cdf(t,NN-2);
elseif exist('tcdf','file')>1
sig = tcdf(t,NN-2);
else
fprintf('CORRCOEF: significance test not completed because of missing TCDF-function\n')
sig = repmat(nan,size(R));
end
sig = 2 * min(sig,1 - sig);
if NARG<3
warning(FLAG_WARNING); % restore warning status
return
end
tmp = R;
%tmp(ix1 | ix2) = nan; % avoid division-by-zero warning
z = log((1+tmp)./(1-tmp))/2; % Fisher transformation [21]
%sz = 1./sqrt(NN-3); % standard error of z
sz = sqrt(2)*erfinv(1-alpha)./sqrt(NN-3); % confidence interval for alpha of z
ci1 = tanh(z-sz);
ci2 = tanh(z+sz);
%ci1(isnan(ci1))=R(isnan(ci1)); % in case of isnan(ci), the interval limits are exactly the R value
%ci2(isnan(ci2))=R(isnan(ci2));
if (NARG<5) || ~YESNAN
nan_sig = repmat(NaN,size(R));
warning(FLAG_WARNING); % restore warning status
return
end
%%%%% ----- check independence of NaNs (missing values) -----
[nan_R, nan_sig] = corrcoef(X,double(isnan(X)));
% remove diagonal elements, because these have not any meaning %
nan_sig(isnan(nan_R)) = nan;
% remove diagonal elements, because these have not any meaning %
nan_R(isnan(nan_R)) = 0;
if 0, any(nan_sig(:) < alpha)
tmp = nan_sig(:); % Hack to skip NaN's in MIN(X)
min_sig = min(tmp(~isnan(tmp))); % Necessary, because Octave returns NaN rather than min(X) for min(NaN,X)
fprintf(1,'CORRCOFF Warning: Missing Values (i.e. NaNs) are not independent of data (p-value=%f)\n', min_sig);
fprintf(1,' Its recommended to remove all samples (i.e. rows) with any missing value (NaN).\n');
fprintf(1,' The null-hypotheses (NaNs are uncorrelated) is rejected for the following parameter pair(s).\n');
[ix,iy] = find(nan_sig < alpha);
disp([ix,iy])
end
%%%%% ----- end of independence check ------
warning(FLAG_WARNING); % restore warning status
function FLAG = flag_implicit_skip_nan(i)
% FLAG_IMPLICIT_SKIP_NAN sets and gets default mode for handling NaNs
% 1 skips NaN's (the default mode if no mode is set)
% 0 NaNs are propagated; input NaN's give NaN's at the output
%
% FLAG = flag_implicit_skip_nan()
% gets current mode
%
% flag_implicit_skip_nan(FLAG) % sets mode
%
% prevFLAG = flag_implicit_skip_nan(nextFLAG)
% gets previous set FLAG and sets FLAG for the future
% flag_implicit_skip_nan(prevFLAG)
% resets FLAG to previous mode
%
% It is used in:
% SUMSKIPNAN, MEDIAN, QUANTILES, TRIMEAN
% and affects many other functions like:
% CENTER, KURTOSIS, MAD, MEAN, MOMENT, RMS, SEM, SKEWNESS,
% STATISTIC, STD, VAR, ZSCORE etc.
%
% The mode is stored in the global variable FLAG_implicit_skip_nan
% It is recommended to use flag_implicit_skip_nan(1) as default and
% flag_implicit_skip_nan(0) should be used for exceptional cases only.
% This feature might disappear without further notice, so you should really not
% rely on it.
% 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
% $Id: flag_implicit_skip_nan.m 8351 2011-06-24 17:35:07Z carandraug $
% Copyright (C) 2001-2003,2009 by Alois Schloegl <alois.schloegl@gmail.com>
% Copyright (C) 2014-2017 Dynare Team
%
% This function is part of the NaN-toolbox
% http://pub.ist.ac.at/~schloegl/matlab/NaN/
persistent FLAG_implicit_skip_nan;
%% if strcmp(version,'3.6'), FLAG_implicit_skip_nan=(1==1); end; %% hack for the use with Freemat3.6
%%% set DEFAULT value of FLAG
if isempty(FLAG_implicit_skip_nan)
FLAG_implicit_skip_nan = (1==1); %logical(1); % logical.m not available on 2.0.16
end
FLAG = FLAG_implicit_skip_nan;
if nargin>0
FLAG_implicit_skip_nan = (i~=0); %logical(i); %logical.m not available in 2.0.16
if (~i)
warning('flag_implicit_skipnan(0): You are warned!!! You have turned off skipping NaN in sumskipnan. This is not recommended. Make sure you really know what you do.')
end
end
function [o,count,SSQ] = sumskipnan(x, DIM, W)
% SUMSKIPNAN adds all non-NaN values.
%
% All NaN's are skipped; NaN's are considered as missing values.
% SUMSKIPNAN of NaN's only gives O; and the number of valid elements is return.
% SUMSKIPNAN is also the elementary function for calculating
% various statistics (e.g. MEAN, STD, VAR, RMS, MEANSQ, SKEWNESS,
% KURTOSIS, MOMENT, STATISTIC etc.) from data with missing values.
% SUMSKIPNAN implements the DIMENSION-argument for data with missing values.
% Also the second output argument return the number of valid elements (not NaNs)
%
% Y = sumskipnan(x [,DIM])
% [Y,N,SSQ] = sumskipnan(x [,DIM])
% [...] = sumskipnan(x, DIM, W)
%
% x input data
% DIM dimension (default: [])
% empty DIM sets DIM to first non singleton dimension
% W weight vector for weighted sum, numel(W) must fit size(x,DIM)
% Y resulting sum
% N number of valid (not missing) elements
% SSQ sum of squares
%
% the function FLAG_NANS_OCCURED() returns whether any value in x
% is a not-a-number (NaN)
%
% features:
% - can deal with NaN's (missing values)
% - implements dimension argument.
% - computes weighted sum
% - compatible with Matlab and Octave
%
% see also: FLAG_NANS_OCCURED, SUM, NANSUM, MEAN, STD, VAR, RMS, MEANSQ,
% SSQ, MOMENT, SKEWNESS, KURTOSIS, SEM
% 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/>.
% $Id: sumskipnan.m 9033 2011-11-08 20:58:07Z schloegl $
%
% Copyright (C) 2000-2005,2009,2011 by Alois Schloegl <alois.schloegl@gmail.com>
% Copyright (C) 2014-2017 Dynare Team
% This function is part of the NaN-toolbox
% http://pub.ist.ac.at/~schloegl/matlab/NaN/
global FLAG_NANS_OCCURED;
if nargin<2
DIM = [];
end
if nargin<3
W = [];
end
% an efficient implementation in C of the following lines
% could significantly increase performance
% only one loop and only one check for isnan is needed
% An MEX-Implementation is available in sumskipnan.cpp
%
% Outline of the algorithm:
% for { k=1,o=0,count=0; k++; k<N}
% if ~isnan(i(k))
% { o += x(k);
% count += 1;
% tmp = x(k)*x(k)
% o2 += tmp;