The MVPMODEL procedure performs principal component analysis (PCA) on multivariate process measurement data that consist of continuous variables which are assumed to be correlated. The input data set for PROC MVPMODEL provides the values of the variables that are to be analyzed.

The MVPMODEL procedure computes various quantities:

• the loadings from the principal components analysis

• the eigenvalues from the principal components analysis, which are the variances of the principal component variables

• the scores from the principal components analysis

• the statistic for each observation

• the SPE (squared prediction error) statistic for each observation, also known as SSE, Q, or DModX.

By default, principal components are computed from the correlation matrix of the variables and optionally from their covariance matrix. The number of principal components in the model (denoted by , where ) can be specified or determined by one of several cross validation methods.

By default, PROC MVPMODEL outputs the correlation matrix of the input variables and the eigenvalues of the correlation matrix. When ODS Graphics is enabled, the output can also include the following plots:

• a scree plot and a variance-explained plot for the principal components (these plots are created by default)

• when using cross validation, plots of and root mean PRESS (predicted sum of squares) for each principal component

• pairwise score plots of principal component scores

• pairwise loading plots of principal component loadings

The procedure saves information about the PCA model in two output data sets, which can subsequently serve as inputs to the MVPMONITOR procedure:

• an output data set that contains all of the variables and observations in the input data set together with observationwise statistics, such as scores, residuals, , and SPE

• an output data set which contains the loadings for each process variable and the eigenvalues associated with the principal components