The MVPMODEL Procedure

The input data set provides the set of process variables that are analyzed. You can specify the input data set by using the DATA= option in the PROC MVPMODEL statement. If you do not specify the DATA= option, the procedure uses the last data set created as its input data set.

The MVPMODEL procedure treats each observation in the DATA= data set as an individual multivariate observation. The observations do not need to be identified or sorted by time because the sequence of the data is not used to build the principal component model. If you provide a time variable in the input data set, it is preserved in the OUT= data set and can be used subsequently by the MVPMONITOR procedure to create control charts.

In basic applications of the MVPMODEL procedure, the observations in the DATA= data set represent measurements from a single process. You can build different principal component models for two or more processes by grouping their measurements in the DATA= data and processing them as BY groups.

In some applications, it is desirable to combine the data from two or more processes and build a common principal component model. This might be the case with processes that are peers in the sense that they are believed to share the same pattern of common cause variation. When you provide the MVPMONITOR procedure with a common model for a set of peer processes, it uses the model to construct identical control limits for each process. This enables you to decide whether a particular process exhibits unusual variation relative to the behavior of its peers.