pcacov
              Perform principal component analysis on covariance matrix
 coeff = pcacov (K) performs principal component analysis
 on the square covariance matrix K and returns the principal component
 coefficients, also known as loadings.  The columns are in order of decreasing
 component variance.
 [coeff, latent] = pcacov (K) also returns a vector
 with the principal component variances, i.e. the eigenvalues of K.
 latent has a length of size (coeff, 1).
 [coeff, latent, explained] = pcacov (K) also
 returns a vector with the percentage of the total variance explained by each
 principal component.  explained has the same size as latent.
 The entries in explained range from 0 (none of the variance is
 explained) to 100 (all of the variance is explained).
 pcacov does not standardize K to have unit variances.  In order
 to perform principal component analysis on standardized variables, use the
 correlation matrix R = K ./ (SD * SD'), where
 SD = sqrt (diag (K)), in place of K.  To perform
 principal component analysis directly on the data matrix, use pca.
See also: bartlett, factoran, pcares, pca
Source Code: pcacov
| 
 x = [ 7    26     6    60;
       1    29    15    52;
      11    56     8    20;
      11    31     8    47;
       7    52     6    33;
      11    55     9    22;
       3    71    17     6;
       1    31    22    44;
       2    54    18    22;
      21    47     4    26;
       1    40    23    34;
      11    66     9    12;
      10    68     8    12
     ];
 Kxx = cov (x);
 [coeff, latent, explained] = pcacov (Kxx)
coeff =
  -0.067800  -0.646018   0.567315   0.506180
  -0.678516  -0.019993  -0.543969   0.493268
   0.029021   0.755310   0.403553   0.515567
   0.730874  -0.108480  -0.468398   0.484416
latent =
   517.7969
    67.4964
    12.4054
     0.2372
explained =
   8.6597e+01
   1.1288e+01
   2.0747e+00
   3.9662e-02
                     |