The PCOMP function computes the principal components of an m -column, n -row array, where m is the number of variables and n is the number of observations or samples. The principal components of a multivariate data set may be used to restate the data in terms of derived variables or may be used to reduce the dimensionality of the data by reducing the number of variables (columns). The result is an nvariables -column ( nvariables £ m ), n -row array of derived variables.
Use this keyword to specify a named variable that will contain the principal components used to compute the derived variables. The principal components are the coefficients of the derived variables and are returned in an m -column, m -row array. The rows of this array correspond to the coefficients of the derived variables. The coefficients are scaled so that the sums of their squares are equal to the eigenvalue from which they are computed. This keyword must be initialized to a nonzero value before calling PCOMP if the principal components are desired.
Use this keyword to specify a named variable that will contain a one-column, m -row array of eigenvalues that correspond to the principal components. The eigenvalues are listed in descending order. This keyword must be initialized to a nonzero value before calling PCOMP if the eigenvalues are desired.
Use this keyword to specify the number of derived variables. A value of zero, negative values, and values in excess of the input array's column dimension result in a complete set ( m -columns and n -rows) of derived variables.