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Commits (2)
 ... ... @@ -34,7 +34,7 @@ for i=1:vobs(o) end end o.data = flip(o.data); o.data = flipud(o.data); return %@test:1 ... ...
 function m = nanmean(o) % --*-- Unitary tests --*-- function m = nanmean(o, geometric) % --*-- Unitary tests --*-- % Returns the mean of the variables in a @dseries object o. % Returns the mean of the variables in a @dseries object o (robust % to the presence of NaNs). % % INPUTS % o o dseries object [mandatory]. % o geometric logical [default is false], if true returns the geometric mean. % - o dseries object [mandatory]. % - geometric logical [default is false], if true returns the geometric mean. % % OUTPUTS % o m 1*vobs(o) vector of doubles. % - m 1*vobs(o) vector of doubles. % Copyright (C) 2019 Dynare Team % Copyright © 2019-2020 Dynare Team % % This file is part of Dynare. % ... ... @@ -26,26 +27,59 @@ function m = nanmean(o) % --*-- Unitary tests --*-- % You should have received a copy of the GNU General Public License % along with Dynare. If not, see . m = nanmean(o.data); if nargin<2 geometric = false; end if geometric m = NaN(1,o.vobs()); for i = 1:o.vobs() tmp = o.data(~isnan(o.data(:,i)),i); m(i) = prod(tmp)^(1.0/length(tmp)); end else m = nanmean(o.data); end return %@test:1 %\$ % Define a dataset. %\$ A = repmat([1.005, 1.05], 10, 1); %\$ A(3,1) = NaN; %\$ A(5,2) = NaN; %\$ %\$ % Instantiate a time series object and compute the mean. %\$ try %\$ ts = dseries(A); %\$ m = nanmean(ts); %\$ t(1) = 1; %\$ catch %\$ t = 0; %\$ end %\$ %\$ if t(1) %\$ t(2) = dassert(isequal(size(m),[1, 2]), true); %\$ t(3) = dassert(m, [1.005, 1.05]); %\$ end %\$ T = all(t); %@eof:1 \ No newline at end of file % Define a dataset. A = repmat([1.005, 1.05], 10, 1); A(3,1) = NaN; A(5,2) = NaN; % Instantiate a time series object and compute the mean. try ts = dseries(A); m = nanmean(ts); t(1) = 1; catch t = 0; end if t(1) t(2) = dassert(isequal(size(m),[1, 2]), true); t(3) = dassert(m, [1.005, 1.05]); end T = all(t); %@eof:1 %@test:2 % Define a dataset. a = [1 0; NaN 2; 3 4]; % Instantiate a time series object and compute the geometric mean. try ts = dseries(a); m = nanmean(ts, true); t(1) = 1; catch t = 0; end if t(1) t(2) = dassert(isequal(size(m),[1, 2]), true); t(3) = dassert(m, [sqrt(3), 0]); end T = all(t); %@eof:2 \ No newline at end of file