The MVPMONITOR Procedure
Input Data Sets
The MVPMONITOR procedure accepts a single primary input data set of one of three types.
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A DATA= data set contains new process data to be analyzed by using an existing PCA model (Phase II analysis).
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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).
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A TABLE= data set contains a summary of an SPE or
control chart, consisting 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 statistics, and a count of the observations that are used to construct the principal component model, as summarized in Table 13.4.
Table 13.4: Variables in the HISTORY= Data Set
|
Variable |
Description |
|---|---|
|
Prin1–Prinj |
Principal component scores |
|
R_ |
Residuals |
|
_NOBS_ |
Number of observations used to build the principal component model |
|
_SPE_ |
Squared prediction error (SPE) |
|
_TSQUARE_ |
|
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:
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eigenvalues of the correlation or covariance matrix used to construct the model
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principal component loadings
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process variable means used to center the variable values
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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 13.5 lists the variables that are required in a LOADINGS= data set.
Table 13.5: 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 an SPE or control chart. Usually, you create a TABLE= data set by specifying the OUTTABLE= option in an SPECHART or TSQUARECHART statement. A TABLE= data set contains the variables listed in Table 13.6.
Table 13.6: Variables in an OUTTABLE= Data Set
|
Variable |
Description |
|---|---|
|
|
Probability ( |
|
|
Flag to indicate control limit was exceeded |
|
|
Lower control limit |
|
|
Center line |
|
|
Squared prediction error (SPE) statistic (SPECHART statement only) |
|
time |
Optional TIME variable |
|
|
|
|
|
Upper control limit |
A TABLE= data set must contain either an _SPE_ or _TSQUARE_ variable but not both. When you use a TABLE= input data set, you can specify only chart statements that correspond to the
statistic in the data set.
You can use a TABLE= data set to display a previously created control chart or to specify custom control limits by computing
your own _LCL_ and _UCL_ values.