From b8547bc8408b00f5c2a5cca9a44bb39b32ac6604 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?St=C3=A9phane=20Adjemian=20=28Hermes=29?=
<stephane.adjemian@univ-lemans.fr>
Date: Thu, 18 Feb 2016 00:42:51 +0100
Subject: [PATCH] Added mean and std methods.
Returns the arithmetic or geometric mean and standard deviation.
---
src/@dseries/mean.m | 127 +++++++++++++++++++++++++++++++++++++++++
src/@dseries/std.m | 125 ++++++++++++++++++++++++++++++++++++++++
src/@dseries/subsref.m | 2 +-
3 files changed, 253 insertions(+), 1 deletion(-)
create mode 100644 src/@dseries/mean.m
create mode 100644 src/@dseries/std.m
diff --git a/src/@dseries/mean.m b/src/@dseries/mean.m
new file mode 100644
index 0000000..0488765
--- /dev/null
+++ b/src/@dseries/mean.m
@@ -0,0 +1,127 @@
+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 <http://www.gnu.org/licenses/>.
+
+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
\ No newline at end of file
diff --git a/src/@dseries/std.m b/src/@dseries/std.m
new file mode 100644
index 0000000..7cedf39
--- /dev/null
+++ b/src/@dseries/std.m
@@ -0,0 +1,125 @@
+function s = std(o, geometric) % --*-- Unitary tests --*--
+
+% Returns the standard deviation of the variables in a @dseries object o.
+% See https://en.wikipedia.org/wiki/Geometric_standard_deviation
+%
+% INPUTS
+% o o dseries object [mandatory].
+% o geometric logical [default is false], if true returns the geometric standard deviation.
+%
+% OUTPUTS
+% o s 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 <http://www.gnu.org/licenses/>.
+
+if nargin<2
+ geometric = false;
+end
+
+if geometric
+ m = mean(o, true);
+ s = exp(sqrt(sum(log(bsxfun(@rdivide, o.data, m)).^2, 1)/nobs(o)));
+else
+ s = std(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);
+%$ s1 = std(ts, true);
+%$ s2 = std(ts);
+%$ t(1) = 1;
+%$ catch
+%$ t = 0;
+%$ end
+%$
+%$ if t(1)
+%$ t(2) = dassert(isequal(size(s1),[1, 2]), true);
+%$ t(3) = dassert(isequal(size(s2),[1, 2]), true);
+%$ t(4) = dassert(s1, [1, 1]);
+%$ t(4) = all(abs(s2)<1e-12);
+%$ 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);
+%$ s1 = ts.std(true);
+%$ s2 = ts.std();
+%$ t(1) = 1;
+%$ catch
+%$ t = 0;
+%$ end
+%$
+%$ if t(1)
+%$ t(2) = dassert(isequal(size(s1),[1, 2]), true);
+%$ t(3) = dassert(isequal(size(s2),[1, 2]), true);
+%$ t(4) = dassert(s1, [1, 1]);
+%$ t(4) = all(abs(s2)<1e-12);
+%$ 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);
+%$ s = std(ts);
+%$ t(1) = 1;
+%$ catch
+%$ t = 0;
+%$ end
+%$
+%$ if t(1)
+%$ t(2) = dassert(isequal(size(s),[1, 2]), true);
+%$ t(3) = dassert(max(abs(s-[.1, .1]))<.0001, true);
+%$ 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);
+%$ s = ts.std();
+%$ t(1) = 1;
+%$ catch
+%$ t = 0;
+%$ end
+%$
+%$ if t(1)
+%$ t(2) = dassert(isequal(size(s),[1, 2]), true);
+%$ t(3) = dassert(max(abs(s-[.1, .1]))<.0001, true);
+%$ end
+%$ T = all(t);
+%@eof:4
\ No newline at end of file
diff --git a/src/@dseries/subsref.m b/src/@dseries/subsref.m
index 6a165a3..ec731ef 100644
--- a/src/@dseries/subsref.m
+++ b/src/@dseries/subsref.m
@@ -90,7 +90,7 @@ switch S(1).type
case 'freq'
% Returns an integer characterizing the data frequency (1, 4, 12 or 52)
B = A.dates.freq;
- case {'lag','lead','hptrend','hpcycle','chain','detrend','exist'} % Methods with less than two arguments.
+ case {'lag','lead','hptrend','hpcycle','chain','detrend','exist','mean','std'} % Methods with less than two arguments.
if length(S)>1 && isequal(S(2).type,'()')
if isempty(S(2).subs)
B = feval(S(1).subs,A);
--
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