nancov
(Not recommended) Covariance ignoring NaN values
nancov is not recommended. Use the MATLAB® function cov instead. With the cov function, you can specify
whether to include or omit NaN values for the calculation. For more
information, see Version History.
Syntax
Y = nancov(X)
Y = nancov(X1,X2)
Y
= nancov(...,1)
Y = nancov(...,'pairwise')
Description
Y = nancov(X) is the covariance cov of X, computed after removing observations with
NaN values.
For vectors x, nancov(x) is the sample variance
of the remaining elements, once NaN values are removed. For matrices
X, nancov(X) is the sample covariance of the
remaining observations, once observations (rows) containing any NaN
values are removed.
Y = nancov(X1,X2), where X1 and
X2 are matrices with the same number of elements, is equivalent
to nancov(X), where X = [X1(:) X2(:)].
nancov removes the mean from each variable (column for matrix
X) before calculating Y. If n
is the number of remaining observations after removing observations with
NaN values, nancov normalizes
Y by either n – 1 or n ,
depending on whether n > 1 or n = 1,
respectively. To specify normalization by n, use
Y
= nancov(...,1).
Y = nancov(...,'pairwise') computes Y(i,j)
using rows with no NaN values in columns i or
j. The result Y may not be a positive definite
matrix.
Examples
Generate random data for two variables (columns) with random missing values:
X = rand(10,2);
p = randperm(numel(X));
X(p(1:5)) = NaN
X =
0.8147 0.1576
NaN NaN
0.1270 0.9572
0.9134 NaN
0.6324 NaN
0.0975 0.1419
0.2785 0.4218
0.5469 0.9157
0.9575 0.7922
0.9649 NaNEstablish a correlation between a third variable and the other two variables:
X(:,3) = sum(X,2)
X =
0.8147 0.1576 0.9723
NaN NaN NaN
0.1270 0.9572 1.0842
0.9134 NaN NaN
0.6324 NaN NaN
0.0975 0.1419 0.2394
0.2785 0.4218 0.7003
0.5469 0.9157 1.4626
0.9575 0.7922 1.7497
0.9649 NaN NaNCompute the covariance matrix for the three variables after removing observations
(rows) with NaN values:
Y = nancov(X)
Y =
0.1311 0.0096 0.1407
0.0096 0.1388 0.1483
0.1407 0.1483 0.2890