The MVPMONITOR Procedure

Input Data Sets

Subsections:

The MVPMONITOR procedure accepts a single primary input data set of one of three types.

  • A DATA= data set contains new process data to be analyzed by using an existing PCA model (Phase II analysis).

  • A HISTORY= data set contains process data and the accompanying scores, residuals, and statistics produced by applying a PCA model. The process data can be the original data that was used to create the model (Phase I analysis) or subsequent data that was analyzed by using a previously created model (Phase II analysis).

  • A TABLE= data set contains a summary of score charts, SPE charts, or $T^2$ charts, which consists of the statistics, control limits, and other information.

These options are mutually exclusive. If you do not specify an option identifying a primary input data set, PROC MVPMONITOR uses the most recently created SAS data set as a DATA= data set.

When you specify a DATA= data set, you must also specify a LOADINGS= data set that contains loadings and other information describing the PCA model. When you specify a HISTORY= data set, you must also specify a LOADINGS= data set if you specify the CONTRIBUTIONS option in a TSQUARECHART statement.

DATA= Data Set

A DATA= data set provides the process measurement data for a Phase II analysis. In addition to the process variables, a DATA= data set can include the following:

When you specify a DATA= data set, you must also specify a LOADINGS= data set that contains the loadings for the principal component model that describes the variation of the process. These loadings are used to score the new data from the DATA= data set. The process variables in the LOADINGS= data set must have the same names as those in the DATA= data set.

HISTORY= Data Set

A HISTORY= data set provides the input data set for a Phase I or Phase II analysis. In addition to the original process variables, it contains principal component scores, residuals, SPE and $T^2$ statistics, and a count of the observations that are used to construct the principal component model, as summarized in Table 13.5.

Table 13.5: Variables in the HISTORY= Data Set

Variable

Description

Prin1–Prinj

Principal component scores

R_$var1$–R_$varp$

Residuals

_NOBS_

Number of observations used to build the principal component model

_SPE_

Squared prediction error (SPE)

_TSQUARE_

$T^2$ statistic computed from principal component scores


A HISTORY= data set must include variables that contain principal component scores. The score variables names must consist of a common prefix followed by the numbers 1, 2, …, j, where j is the number of principal components. By default, the common prefix is Prin. You can use the PREFIX= option to specify another prefix for score variables.

If the number of principal components is less than the total number of process variables, the HISTORY= data set should also contain residual variables. A residual variable name consists of a common prefix followed by the corresponding process variable name. The default residual variable prefix is R_. For example, if the process variables are A, B, and C, the default residual variable names are R_A, R_B, and R_C. You can use the RPREFIX= option to specify a different residual variable prefix.

Note: Usually you create a HISTORY= data set by specifying the PROC MVPMODEL OUT= option or the PROC MVPMONITOR OUTHISTORY= option. If the PREFIX= or RPREFIX= option is used when such an output data set is created, you must specify the same prefixes to identify the score and residual variables when you read it as a HISTORY= data set.

LOADINGS= Data Set

The LOADINGS= data set contains the following information about the principal component model:

  • eigenvalues of the correlation or covariance matrix used to construct the model

  • principal component loadings

  • process variable means used to center the variable values

  • process variable standard deviations used to scale the variable values

You can produce a LOADINGS= data set by using the PROC MVPMODEL OUTLOADINGS= option. Table 13.6 lists the variables that are required in a LOADINGS= data set.

Table 13.6: Variables in the LOADINGS= Data Set

Variable

Description

_VALUE_

The value contained in process variables for a given observation

_NOBS_

Number of observations used to build the principal component model

_PC_

Principal component number; 0 for the observation that contains eigenvalues

process variables

Values associated with the process variables


Valid values for the _VALUE_ variable are as follows:

EIGEN

eigenvalues from the principal component analysis

LOADING

principal component loadings

MEAN

process variable means

STD

process variable standard deviations

The LOADINGS= data set contains one EIGEN observation and j LOADING observations, where j is the number of principal components in the model. The presence of a MEAN observation indicates that the process variables were centered when the principal component model was constructed, and the presence of a STD observation indicates that the process variables were scaled when the principal component model was constructed. The means and standard deviations are used to center and scale new data in a Phase II analysis.

TABLE= Data Set

A TABLE= data set contains a summary of one or more score charts, SPE charts, or $T^2$ control charts. Usually, you create a TABLE= data set by specifying the OUTTABLE= option in a SCORECHART , SPECHART , or TSQUARECHART statement. Each type of TABLE= data set contains different variables, and when you specify a TABLE= data set you can only specify chart statements of the corresponding type. For example, if you use a TABLE= data set that contains SPE chart summary data, you cannot specify a SCORECHART or TSQUARECHART statement.

You can use a TABLE= data set to display previously created control charts or to specify custom control limits by computing your own _LCL_ and _UCL_ values.

Table 13.7, Table 13.8, and Table 13.9 list the variables that are contained in the three types of TABLE= data set.

Note:

  1. SPE chart and $T^2$ chart TABLE= data sets contain one observation per time value. Score chart TABLE= data sets contain one observation for each principal component per time value.

  2. SPE chart and $T^2$ chart TABLE= data sets contain residual variables corresponding to the process variables. Each residual variable has the same name as the corresponding process variable

Table 13.7: Score Chart TABLE= Data Set Variables

Variable

Description

_COMP_

Principal component number

_EXLIM_

Flag that indicates control limit was exceeded

_LCL_

Lower control limit

_MEAN_

Center line

_SCORE_

Principal component score

series

Optional SERIES variable

_SIGMAS_

Multiple of score standard deviation used to compute control limits

time

Optional TIME variable

_UCL_

Upper control limit


Table 13.8: SPE Chart TABLE= Data Set Variables

Variable

Description

_ALPHA_

Probability ($\alpha $) of exceeding control limits

_EXLIM_

Flag to indicate control limit was exceeded

_LCL_

Lower control limit

_MEDIAN_

Center line

residuals

Residual variables

series

Optional SERIES variable

_SPE_

Squared prediction error (SPE) statistic

time

Optional TIME variable

_UCL_

Upper control limit


Table 13.9: $T^2$ Chart TABLE= Data Set Variables

Variable

Description

_ALPHA_

Probability ($\alpha $) of exceeding control limits

_EXLIM_

Flag to indicate control limit was exceeded

_LCL_

Lower control limit

_MEDIAN_

Center line

residuals

Residual variables

series

Optional SERIES variable

time

Optional TIME variable

_TSQUARE_

$T^2$ statistic (TSQUARECHART statement only)

_UCL_

Upper control limit