# scipy.stats.random_correlation

scipy.stats.random_correlation = <scipy.stats._multivariate.random_correlation_gen object>

  A random correlation matrix.

  Return a random correlation matrix, given a vector of eigenvalues.

  The eigs keyword specifies the eigenvalues of the correlation matrix, and implies the dimension.

  Parameters
    eigs 1d ndarray

      Eigenvalues of correlation matrix.

  Notes

  Generates a random correlation matrix following a numerically stable algorithm spelled out by Davies & Higham. This algorithm uses a single O(N) similarity transformation to construct a symmetric positive semi-definite matrix, and applies a series of Givens rotations to scale it to have ones on the diagonal.

  References

  1

    Davies, Philip I; Higham, Nicholas J; “Numerically stable generation of correlation matrices and their factors”, BIT 2000, Vol. 40, No. 4, pp. 640 651

  Examples

      >>> from scipy.stats import random_correlation
      >>> rng = np.random.default_rng()
      >>> x = random_correlation.rvs((.5, .8, 1.2, 1.5), random_state=rng)
      >>> x
      array([[ 1.        , -0.07198934, -0.20411041, -0.24385796],
             [-0.07198934,  1.        ,  0.12968613, -0.29471382],
             [-0.20411041,  0.12968613,  1.        ,  0.2828693 ],
             [-0.24385796, -0.29471382,  0.2828693 ,  1.        ]])
      >>> import scipy.linalg
      >>> e, v = scipy.linalg.eigh(x)
      >>> e
      array([ 0.5,  0.8,  1.2,  1.5])

  Methods

  ``rvs(eigs=None, random_state=None)`` | Draw random correlation matrices, all with eigenvalues eigs.

scipy.stats.unitary_group scipy.stats.multivariate_t