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

[1]  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