The MVPMODEL Procedure

Output Data Sets

Subsections:

OUT= Data Set

The OUT= data set contains all the variables in the input data set plus new variables that contain the principal component scores, residuals, and other computed values listed in Table 12.2.

The names of the score variables are formed by concatenating the value given by the PREFIX= option (or the default Prin, if PREFIX= is not specified) and the numbers 1, 2, …, j, where j is the number of principal components in the model.

The names of the residual variables are formed by concatenating the value given by the RPREFIX= option (or the default R_, if RPREFIX= is not specified) and the names of the process variables used in the analysis. Residual variables are created only when the number of principal components in the model is less than the number of process measurement variables in the input data set.

Table 12.2: Computed Variables in the OUT= Data Set

Variable

Description

Prin1–Prinj

Principal component scores

R_$var1$–R_$varp$

Residuals

_NOBS_

Number of observations used in the analysis

_SPE_

Squared prediction error (SPE)

_TSQUARE_

$T^2$ statistic computed from principal component scores


OUTLOADINGS= Data Set

The OUTLOADINGS= data set contains the eigenvalues of the correlation (or covariance) matrix, the loadings computed for the process variables, and other information about the principal component model. The variables that are saved in the OUTLOADINGS= data set are listed in Table 12.3.

Table 12.3: Variables in the OUTLOADINGS= Data Set

Variable

Description

_VALUE_

Character variable identifying the type of values in an observation

_PC_

Principal component number

_NOBS_

Number of observations used in the analysis

process variables

Eigenvalues, means, standard deviations, and loadings for 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

For an observation where _VALUE_ is equal to LOADING, the _PC_ variable identifies the principal component whose loadings are recorded in that observation.

The process variable means and standard deviations are used by the other MVP procedures to center and scale new data in a Phase II analysis. If you specify the NOCENTER option, the OUTLOADINGS= data set does not contain a MEAN observation. If you specify the NOSCALE option, the OUTLOADINGS= data set does not contain a STD observation.