Statistics Toolbox™

cholcov - Cholesky-like covariance decomposition

Syntax

T = cholcov(SIGMA)
[T,num] = cholcov(SIGMA)
[T,num] = cholcov(SIGMA,0)

Description

T = cholcov(SIGMA) computes T such that SIGMA = T'*T. SIGMA must be square, symmetric, and positive semi-definite. If SIGMA is positive definite, then T is the square, upper triangular Cholesky factor. If SIGMA is not positive definite, T is computed from an eigenvalue decomposition of SIGMA. T is not necessarily triangular or square in this case. Any eigenvectors whose corresponding eigenvalue is close to zero (within a small tolerance) are omitted. If any remaining eigenvectors are negative, T is empty.

[T,num] = cholcov(SIGMA) returns the number num of negative eigenvalues of SIGMA, and T is empty if num is positive. If num is zero, SIGMA is positive semi-definite. If SIGMA is not square and symmetric, num is NaN and T is empty.

[T,num] = cholcov(SIGMA,0) returns num equal to zero if SIGMA is positive definite, and T is the Cholesky factor. If SIGMA is not positive definite, num is a positive integer and T is empty. [...] = cholcov(SIGMA,1) is equivalent to [...] = cholcov(SIGMA).

Example

The following 4-by-4 covariance matrix is rank-deficient:

C1 = [2 1 1 2;1 2 1 2;1 1 2 2;2 2 2 3]
C1 =
     2     1     1     2
     1     2     1     2
     1     1     2     2
     2     2     2     3
rank(C1)
ans =
     3

Use cholcov to factor C1:

T = cholcov(C1)
T =
   -0.2113    0.7887   -0.5774         0
    0.7887   -0.2113   -0.5774         0
    1.1547    1.1547    1.1547    1.7321

C2 = T'*T
C2 =
    2.0000   1.0000   1.0000   2.0000
    1.0000   2.0000   1.0000   2.0000
    1.0000   1.0000   2.0000   2.0000
    2.0000   2.0000   2.0000   3.0000

Use T to generate random data with the specified covariance:

C3 = cov(randn(1e6,3)*T)
C3 =
    1.9973    0.9982    0.9995    1.9975
    0.9982    1.9962    0.9969    1.9956
    0.9995    0.9969    1.9980    1.9972
    1.9975    1.9956    1.9972    2.9951

See Also

chol, cov

children

classcount