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Principal component analysis on covariance matrix

performs principal component analysis on the square covariance matrix `coeff`

= pcacov(`V`

)`V`

and returns the principal component coefficients, also known as loadings.

`pcacov`

does not standardize `V`

to have unit variances. To perform principal component analysis on standardized variables, use the correlation matrix `R = V./(SD*SD')`

, where `SD = sqrt(diag(V))`

, in place of `V`

. To perform principal component analysis directly on the data matrix, use `pca`

.

[1] Jackson, J. E. *A User's Guide to Principal Components*. Hoboken, NJ: John Wiley and Sons, 1991.

[2] Jolliffe, I. T. *Principal Component Analysis*. 2nd ed. New York: Springer-Verlag, 2002.

[3] Krzanowski, W. J. *Principles of Multivariate Analysis: A User's Perspective*. New York: Oxford University Press, 1988.

[4] Seber, G. A. F. *Multivariate Observations*, Wiley, 1984.