Statistics Toolbox

pcacov

Principal Components Analysis (PCA) using the covariance matrix.

Syntax

pc = pcacov(X)
[pc,latent,explained] = pcacov(X)

Description

[pc,latent,explained] = pcacov(X) takes the covariance matrix X and returns the principal components in pc, the eigenvalues of the covariance matrix of X in latent, and the percentage of the total variance in the observations explained by each eigenvector in explained.

Example

load hald
covx = cov(ingredients);
[pc,variances,explained] = pcacov(covx)
pc =
    0.0678   -0.6460    0.5673   -0.5062
    0.6785   -0.0200   -0.5440   -0.4933
   -0.0290    0.7553    0.4036   -0.5156
   -0.7309   -0.1085   -0.4684   -0.4844
variances =
  517.7969
   67.4964
   12.4054
    0.2372
explained =
   86.5974
   11.2882
    2.0747
    0.0397

References

Jackson, J. E., A User's Guide to Principal Components, John Wiley and Sons, Inc. 1991. pp. 1-25.

See Also
barttest, pcares, princomp

pareto

pcares