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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.