From b4204f8b9ecbfe63b660bb019169e2f352730f8c Mon Sep 17 00:00:00 2001
From: Johannes Pfeifer <jpfeifer@gmx.de>
Date: Thu, 11 Jan 2018 22:08:03 +0100
Subject: [PATCH] Make sure that mean and covariance of data are correctly
computed with only one observation
Need to always compute mean along first dimension
(cherry picked from commit 2d371b1997f5fa07fcbbf47e5923d7817d07c6b9)
---
matlab/utilities/dataset/makedataset.m | 2 +-
matlab/utilities/dataset/nancovariance.m | 4 ++--
2 files changed, 3 insertions(+), 3 deletions(-)
diff --git a/matlab/utilities/dataset/makedataset.m b/matlab/utilities/dataset/makedataset.m
index c5c244b540..a26a074c5f 100644
--- a/matlab/utilities/dataset/makedataset.m
+++ b/matlab/utilities/dataset/makedataset.m
@@ -267,7 +267,7 @@ else
end
% Compute the empirical mean of the observed variables.
-DatasetInfo.descriptive.mean = nanmean(DynareDataset.data);
+DatasetInfo.descriptive.mean = nanmean(DynareDataset.data,1);
% Compute the empirical covariance matrix of the observed variables.
DatasetInfo.descriptive.covariance = nancovariance(DynareDataset.data);
diff --git a/matlab/utilities/dataset/nancovariance.m b/matlab/utilities/dataset/nancovariance.m
index 8e2e2b2275..09f2a38af6 100644
--- a/matlab/utilities/dataset/nancovariance.m
+++ b/matlab/utilities/dataset/nancovariance.m
@@ -51,7 +51,7 @@ function CovarianceMatrix = nancovariance(data)
CovarianceMatrix = zeros(size(data,2));
if isanynan(data)
- data = bsxfun(@minus,data,nanmean(data));
+ data = bsxfun(@minus,data,nanmean(data,1));
for i=1:size(data,2)
for j=i:size(data,2)
CovarianceMatrix(i,j) = nanmean(data(:,i).*data(:,j));
@@ -61,7 +61,7 @@ if isanynan(data)
end
end
else
- data = bsxfun(@minus,data,mean(data));
+ data = bsxfun(@minus,data,mean(data,1));
CovarianceMatrix = (transpose(data)*data)/size(data,1);
end
--
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