The GENSELECT Procedure

specifies a global significance level for the construction of confidence intervals. The confidence level is 1 – number. The value of number must be between 0 and 1. You can override this global significance level by specifying this option in the OUTPUT statement. By default, ALPHA=0.05.

specifies the maximum number of observations to be included in a batch. During the optimization, the GENSELECT procedure reads at most number observations from the data table into memory, performs the appropriate log-likelihood, gradient, and Hessian computations on that batch of observations, then discards those observations and reads in the next batch of data for processing. Generally, a smaller PAGESIZE value decreases memory usage but might lead to longer computation times, whereas a larger PAGESIZE value might lead to shorter computation times but increases memory usage. The default PAGESIZE=AUTO option determines if the entire data table can be held in a subset of your available memory; if not, then it sets PAGESIZE=256.

creates the "Parameter Estimates Correlation Matrix" table. The correlation matrix is computed by normalizing the covariance matrix . That is, if is an element of , then the corresponding element of the correlation matrix is , where .

creates the "Parameter Estimates Covariance Matrix" table. The covariance matrix is computed as the inverse of the negative of the matrix of second derivatives of the log-likelihood function with respect to the model parameters (the Hessian matrix).

names the input data table for PROC GENSELECT to use. The default is the most recently created data table. CAS-libref.data-table is a two-level name, where

generates the "Iteration History" table.

specifies the base regularization parameter for the LASSO model selection method. The regularization parameter for step i is r. By default, LASSORHO=0.8.

specifies the maximum number of steps for LASSO model selection. By default, LASSOSTEPS=20.

specifies the convergence tolerance for the optimization algorithm that solves for the LASSO parameter estimates at each step of LASSO model selection. By default, LASSOTOL=1E–6.

disables the checking process that determines whether maximum likelihood estimates of the regression parameters exist. For more information, see the section Existence of Maximum Likelihood Estimates.

suppresses the display of the "Class Level Information" table if you do not specify number. If you specify number, the values of the classification variables are displayed for only those variables whose number of levels is less than number. Specifying number helps to reduce the size of the "Class Level Information" table if some classification variables have a large number of levels.

suppresses computation of the covariance matrix and the standard errors of the regression coefficients. When the model contains many variables (thousands), the inversion of the Hessian matrix to derive the covariance matrix and the standard errors of the regression coefficients can be time-consuming. The CORRB, COVB, and TYPE3 options are not available when the NOSTDERR option is specified. This option also disables the quasi-complete separation check; for more information, see the section Existence of Maximum Likelihood Estimates.

displays fit statistics in the "Fit Statistics" table that are usually produced when your data are partitioned. This option is not required when you specify a PARTITION statement. The statistic that is added to the table is the average square error (or Brier score).

displays the standardized estimates of the parameters in the "Parameter Estimates" table. The standardized estimate of is given by , where is the total sample standard deviation for the ith explanatory variable and

The sample standard deviations for parameters that are associated with CLASS variables are computed using their codings. The standardized estimates are not computed for the intercept parameters.