pca
              Performs a principal component analysis on a data matrix.
A principal component analysis of a data matrix of observations in a dimensional space returns a transformation matrix, to perform a change of basis on the data. The first component of the new basis is the direction that maximizes the variance of the projected data.
Input argument:
The following Name, Value pair arguments can be used:
"Algorithm" defines the algorithm to use:
 "svd" (default), for singular value decomposition
 "eig" for eigenvalue decomposition
 "Centered" is a boolean indicator for centering the observation data.
 It is true by default.
 "Economy" is a boolean indicator for the economy size output.  It is
 true by default.  Hence, pca returns only the elements of
 latent that are not necessarily zero, and the corresponding columns of
 coeff and score, that is, when , only the first
 .
 "NumComponents" defines the number of components  to return.
 If , then only the first  columns of coeff and
 score are returned.
 "Rows" defines how to handle missing values:
 "complete" (default), missing values are removed before
 computation.
 "pairwise" (only valid when "Algorithm" is
 "eig"), the covariance of rows with missing data is computed using
 the available data, but the covariance matrix could be not positive definite,
 which triggers the termination of pca.
 "complete", missing values are not allowed, pca
 terminates with an error if there are any.
 "Weights" defines observation weights as a vector of positive values
 of length .
 "VariableWeights" defines variable weights:
 "variance" to use the sample variance as weights.
 Return values:
Matlab compatibility note: the alternating least square method ’als’ and associated options ’Coeff0’, ’Score0’, and ’Options’ are not yet implemented
See also: barttest, factoran, pcacov, pcares
Source Code: pca