function m = mean(o, geometric) % --*-- Unitary tests --*-- % Returns the mean of the variables in a @dseries object o. % % INPUTS % o o dseries object [mandatory]. % o geometric logical [default is false], if true returns the geometric mean. % % OUTPUTS % o m 1*vobs(o) vector of doubles. % Copyright (C) 2016 Dynare Team % % This file is part of Dynare. % % Dynare 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. % % Dynare 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 Dynare. If not, see . if nargin<2 geometric = false; end if geometric m = prod(o.data, 1).^(1/nobs(o)); else m = mean(o.data); end %@test:1 %\$ % Define a dataset. %\$ A = repmat([1.005, 1.05], 10, 1); %\$ %\$ % Instantiate a time series object and compute the mean. %\$ try %\$ ts = dseries(A); %\$ m = mean(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, [1.005, 1.05]); %\$ end %\$ T = all(t); %@eof:1 %@test:2 %\$ % Define a dataset. %\$ A = repmat([1.005, 1.05], 10, 1); %\$ %\$ % Instantiate a time series object and compute the mean. %\$ try %\$ ts = dseries(A); %\$ m = ts.mean(true); %\$ 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:2 %@test:3 %\$ % Define a dataset. %\$ A = bsxfun(@plus, randn(100000000,2)*.1, [.5, 2]); %\$ %\$ % Instantiate time series objects and compute the mean. %\$ try %\$ ts = dseries(A); %\$ m1 = mean(ts); %\$ m2 = mean(ts, true); %\$ t(1) = 1; %\$ catch %\$ t = 0; %\$ end %\$ %\$ if t(1) %\$ t(2) = dassert(isequal(size(m1),[1, 2]), true); %\$ t(3) = dassert(isequal(size(m2),[1, 2]), true); %\$ t(4) = dassert(max(abs(m1-[.5, 2]))<.0001, true); %\$ t(5) = isinf(m2(2)); %\$ t(6) = isequal(m2(1), 0); %\$ end %\$ T = all(t); %@eof:3 %@test:4 %\$ % Define a dataset. %\$ A = bsxfun(@plus, randn(100000000,2)*.1, [.5, 2]); %\$ %\$ % Instantiate time series objects and compute the mean. %\$ try %\$ ts = dseries(A); %\$ m1 = ts.mean(); %\$ m2 = ts.mean(true); %\$ m3 = ts.mean(false); %\$ t(1) = 1; %\$ catch %\$ t = 0; %\$ end %\$ %\$ if t(1) %\$ t(2) = dassert(isequal(size(m1),[1, 2]), true); %\$ t(3) = dassert(isequal(size(m2),[1, 2]), true); %\$ t(4) = dassert(max(abs(m1-[.5, 2]))<.0001, true); %\$ t(5) = isinf(m2(2)); %\$ t(6) = isequal(m2(1), 0); %\$ t(7) = isequal(m1, m3); %\$ end %\$ T = all(t); %@eof:4