Principal component analysis (PCA) on data
princomp has been removed. Use
[COEFF,SCORE] = princomp(X)
[COEFF,SCORE,latent] = princomp(X)
[COEFF,SCORE,latent,tsquare] = princomp(X)
[...] = princomp(X,'econ')
COEFF = princomp(X) performs principal components
analysis (PCA) on the n-by-p data
X, and returns the principal component coefficients,
also known as loadings. Rows of
X correspond to
observations, columns to variables.
COEFF is a p-by-p matrix,
each column containing coefficients for one principal component. The
columns are in order of decreasing component variance.
subtracting off column means, but does not rescale the columns of
To perform principal components analysis with standardized variables,
that is, based on correlations, use
To perform principal components analysis directly on a covariance
or correlation matrix, use
[COEFF,SCORE] = princomp(X) returns
the principal component scores; that is, the representation of
the principal component space. Rows of
to observations, columns to components.
[COEFF,SCORE,latent] = princomp(X) returns
a vector containing the eigenvalues of the covariance matrix of
[COEFF,SCORE,latent,tsquare] = princomp(X) returns
which contains Hotelling's T2 statistic
for each data point.
The scores are the data formed by transforming the original
data into the space of the principal components. The values of the
latent are the variance of the columns of
Hotelling's T2 is a measure of the multivariate
distance of each observation from the center of the data set.
n <= p,
necessarily zero, and the columns of
directions that are orthogonal to
[...] = princomp(X,'econ') returns only
the elements of
latent that are not necessarily
zero, and the corresponding columns of
that is, when
n <= p, only the first
This can be significantly faster when
p is much
 Jackson, J. E., A User's Guide to Principal Components, John Wiley and Sons, 1991, p. 592.
 Jolliffe, I. T., Principal Component Analysis, 2nd edition, Springer, 2002.
 Krzanowski, W. J. Principles of Multivariate Analysis: A User's Perspective. New York: Oxford University Press, 1988.
 Seber, G. A. F., Multivariate Observations, Wiley, 1984.