The MVPMODEL Procedure

computes the principal components from the covariance matrix. By default, the correlation matrix is analyzed. The COV option causes variables with large variances to be more strongly associated with components that have large eigenvalues, and it causes variables with small variances to be more strongly associated with components that have 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.

Note: Specifying the COV option has the same effect as specifying the NOSCALE option.

specifies that cross validation be performed to determine the number of principal components and specifies the method to be used. If you do not specify the CV= option, no cross validation is performed.

In cross validation, the input data are repeatedly divided into a training set, which is used to compute a model, and a test set, which is used to test the model fit. The cross validation that is performed here is along both observations and variables, as described in Eastment and Krzanowski (1982), which is a more detailed version of the “alternative scheme” of Wold (1978). The observations and variables are separately divided into groups. Each test set is the intersection of one observation group and one variable group, so the number of test sets that are used is the product of the number of observation groups and the number of variable groups. See the section Cross Validation for more information.

Note: The CV= option is experimental in this release.

CV=ONE requests one-at-a-time cross validation, in which each observation group contains one observation and each variable group contains one variable. This approach is very computationally intensive because it computes separate principal component models for each potential number of principal components, where n is the number of observations in the input data set and p is the number of process variables.

CV=BLOCK requests blocked cross validation, in which observation groups consist of blocks of nobs consecutive observations and variable groups consist of blocks of nvar consecutive variables. You can specify the following cv-block-options in parentheses after the CV=BLOCK option:

CV=SPLIT requests split-sample cross validation, in which observation groups are formed by selecting every nobsth observation and variable groups are formed by selecting very nvarth variable. You can specify the following cv-split-options in parentheses after the CV=SPLIT option:

CV=RANDOM requests that observations and variables be assigned to groups randomly. You can specify the following cv-random-options in parentheses after the CV=RANDOM option:

Note: You cannot specify the CV= option together with the NCOMP= option.

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

specifies the number of principal components to extract. The default is , where p is the number of process variables and N is the number of observations (runs). You can specify NCOMP=ALL to override the limit of 15 principal components. You cannot specify the NCOMP= option together with the CV= option. If the number of nonzero eigenvalues of the correlation matrix is less than the number of components specified, p, then the p will be reset to the number of nonzero eigenvalues.

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 want to produce only output data sets.

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

Note: Specifying the NOSCALE option has the same effect as specifying the COV 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 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 in SAS/STAT 12.1 User's Guide.

You can specify the following global-plot-options:

You can specify the following plot-requests:

specifies a prefix for naming the principal component scores in the OUT= data set. By default, the names are Prin1, Prin2, …, Prinj. 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 j 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. The default is R_. Residual variable names are formed by appending process variable names to the prefix.

If the length of the resulting residual variable exceeds the maximum name length defined by the VALIDVARNAME= system option, characters are removed from the middle of the process variable name before it is appended to the residual prefix. For example, if you specify RPREFIX=Residual_, the maximum variable name length is 32, and there is a process variable named PrimaryThermometerReading, then the corresponding residual variable name is Residual_PrimaryThermeterReading.

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