by An Uncommon Lab

randcov

Create a random n-by-n covariance matrix with eigenvalues between the given bounds.

Inputs

Outputs

Example

Create a covariance matrix, make random draws, and see that their covariance is approximate as requested.

% Generate the covariance matrix
C = randcov(5)
 
% Draw a bunch of random samples from a binomial distribution.
d = mnddraw(C, 100000);
 
% Find the covariance of the draws; note that it looks like C.
C_empirical = cov(d.')

C =
    0.2547    0.0298    0.1850   -0.1950   -0.0339
    0.0298    0.2380   -0.0108    0.0758    0.1653
    0.1850   -0.0108    0.3915   -0.3330   -0.0362
   -0.1950    0.0758   -0.3330    0.4366    0.0326
   -0.0339    0.1653   -0.0362    0.0326    0.2857
C_empirical =
    0.2559    0.0296    0.1852   -0.1950   -0.0346
    0.0296    0.2391   -0.0108    0.0755    0.1651
    0.1852   -0.0108    0.3917   -0.3324   -0.0360
   -0.1950    0.0755   -0.3324    0.4352    0.0319
   -0.0346    0.1651   -0.0360    0.0319    0.2858

Create a "matrix square root" of a covariance matrix with eigenvalues between 1 and 4 and get the generated eigenvalues too.

[sqC, v] = randcov(4, 1, 4, 'sqrt');
 
% Calculate the full covariance matrix and get its eigenvalues.
C = sqC * sqC.';
eig(C).'
 
% Compare to the eigenvalues used to generate sqC.
sort(v)

ans =
    1.6209    1.9528    3.2386    3.3837
ans =
    1.6209    1.9528    3.2386    3.3837

While we're here, let's use the "matrix square root" to make some random draws and then examine the covariance.

d = sqC * randn(4, 100000);
C_empirical = cov(d.')
C

C_empirical =
    2.1932   -0.1192   -0.1265   -0.5776
   -0.1192    2.6547   -0.7807   -0.0465
   -0.1265   -0.7807    2.3431    0.0947
   -0.5776   -0.0465    0.0947    3.0091
C =
    2.1788   -0.1236   -0.1120   -0.5674
   -0.1236    2.6326   -0.7939   -0.0444
   -0.1120   -0.7939    2.3581    0.0911
   -0.5674   -0.0444    0.0911    3.0265

See Also

mnddraw, sqrtpsdm