computes the principal components from the covariance matrix. By default, the correlation matrix is analyzed. Use of the COV option causes variables with large variances to be more strongly associated with components with large eigenvalues and causes variables with small variances to be more strongly associated with components with small eigenvalues. You should not specify the COV option unless the units in which the variables are measured are comparable or the variables are standardized in some way.

Specifying the NOSCALE option is equivalent to specifying the COV option.

specifies the cross validation method to be used. By default, no cross validation is performed. The option CV=ONE requests one-at-a-time cross validation, CV=SPLIT requests that every nth observation be excluded, CV=BLOCK requests that n blocks of consecutive observations be excluded, and CV=RANDOM requests that observations be excluded at random. Optionally, you can specify n for CV=SPLIT and CV=BLOCK; the default is n=7 in each case. You can also specify the following optional cv-random-options in parentheses after the CV=RANDOM option:

You cannot specify the CV= option together with the NCOMP= option. See the section Cross Validation for more information.

specifies the input SAS data set to be analyzed. If the DATA= option is omitted, the procedure uses the most recently created SAS data set.

specifies how observations with missing values are to be handled in computing the fit. The default is MISSING=NONE, which excludes observations with missing values for any process variables from the analysis. MISSING=AVG specifies that the fit be computed by replacing missing values of a process variable with the average of its nonmissing values.

specifies the number of principal components to extract. The default is , where is the number of process variables and is the number of observations (runs). You cannot specify the NCOMP= option together with the CV= option.

suppresses centering of the process variables before fitting. This is useful if the variables are already centered and scaled. See the section Centering and Scaling for more information.

suppresses re-centering and rescaling of the process variables before each model is fit in the cross validation. See the section Centering and Scaling for more information.

suppresses the display of all results, both tabular and graphical. This is useful when you only want to produce output data sets.

suppresses scaling of the process variables before fitting. This is useful if the variables are already centered and scaled.

Specifying the COV option is equivalent to specifying the NOSCALE option.

creates an output data set that contains all the original data from the input data set, principal component scores, and multivariate summary statistics. See the section Output Data Sets for details.

creates an output data set that contains the loadings for the principal components and the eigenvalues of the correlation (or covariance) matrix. See the section Output Data Sets for details.

controls the plots produced through ODS Graphics. When you specify only one plot request, you can omit the parentheses from around the plot request. For example:

plots=none
plots=score
plots=loadings

ODS Graphics must be enabled before you request plots.

For general information about ODS Graphics, see Chapter 21, Statistical Graphics Using ODS (SAS/STAT User's Guide).

The global-plot-options include the following:

The plot-requests include the following:

specifies a prefix for naming the principal component scores in the OUT= data set. By default, the names are Prin1, Prin2, ..., Prin. If you specify PREFIX=ABC, the components are named ABC1, ABC2, ABC3, and so on. The number of characters in the prefix plus the number of digits in should not exceed the current name length defined by the VALIDVARNAME= system option.

specifies a prefix for naming the residual variables in the OUT= data set. By default, the prefix is R_. Residual variable names are formed by appending process variable names to the prefix. The number of characters in the prefix plus the maximum length of the process variable names should not exceed the current name length defined by the VALIDVARNAME= system option.

standardizes the principal component scores in the OUT= data set to unit variance. If you omit the STDSCORES option, the scores have variance equal to the corresponding eigenvalue. STDSCORES has no effect on the eigenvalues themselves.

The TIMEGROUP= option specifies a variable in the DATA= input data set whose values indicate the time or chronological order associated with process variable measurements. This variable is not included in the principal component analysis, but you must specify this variable to compute the statistics needed to create an SPE chart when there is more than one observation per time value.