corrm, corrvc, corrx#

Purpose#

Computes an unbiased estimate of a correlation matrix.

Format#

cx = corrm(m)#
cx = corrvc(vc)#
cx = corrx(x)#
Parameters:

  • m (KxK moment (x'x) matrix) – A constant term MUST have been the first variable when the moment matrix was computed.

  • vc (KxK variance-covariance matrix) – data or parameters

  • x (NxK matrix) – data

Returns:

cx (PxP correlation matrix) – For corrm(), \(P = K-1\). For corrvc() and corrx(), \(P = K\).

Examples#

// Set rnd seed for reproducible results
rndseed   8989;

// Assign x1 and x2
x1 = rndn(3, 3);
x2 = ones(3, 1)~x1;

print "x1 :" x1 ;
print "x2 :" x2;

After the above code, x1 and x2 look like:

x1 :
  0.010555555     -0.045969063       0.12701699
    1.6454828        1.2380373       0.53988699
    1.1556776      -0.53575797       0.14056238
x2 :
    1.0000000      0.010555555     -0.045969063       0.12701699
    1.0000000        1.6454828        1.2380373       0.53988699
    1.0000000        1.1556776      -0.53575797       0.14056238

Continuing from above code,

// Correlation of x1 with x1
print "corrx(x1) :" corrx(x1);

// Correlation of moment = x2'x2
m = x2'x2;
print "corrm(x2'x2) :" corrm(m);

// Correlation of vc of x2
vc = varCovMS(x2'x2);
print "corrvc(varCovMS(x2'x2)):" corrvc(vc);

After the above code,

corrx(x1) :
     1.0000000       0.52196856       0.75039768
    0.52196856        1.0000000       0.95548228
    0.75039768       0.95548228        1.0000000

corrm(x2'x2) :
     1.0000000       0.52196856       0.75039768
    0.52196856        1.0000000       0.95548228
    0.75039768       0.95548228        1.0000000

corrvc(vc):
     1.0000000       0.52196856       0.75039768
    0.52196856        1.0000000       0.95548228
    0.75039768       0.95548228        1.0000000

Remarks#

The corrm() and corrx() computes the population correlation matrix. For the sample correlation matrix, use corrm() or corrx().

Source#

corr.src

See also

Functions momentd(), corrms(), corrxs(), varCovX(), varCovM()

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