The LOGSELECT 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 the global specification by specifying the ALPHA= option in the OUTPUT statement. By default, ALPHA=0.05.

displays measures of association between predicted probabilities and observed responses for binary, binomial, and ordinal response models. These measures assess the predictive ability of the model. The displayed statistics are the concordance index c (the area under the ROC curve, AUC), Somers’ D statistic (Gini’s coefficient), the Goodman-Kruskal gamma statistic, and Kendall’s tau-a statistic. These statistics are based on the number of pairs of observations that have different response values, the number of concordant pairs, and the number of discordant pairs. For more information, see the section Association Statistics.

specifies the precision to use for the association, classification, and LACKFIT option computations. The predicted probabilities or scores are rounded to the nearest multiple of number, which effectively bins and sorts the observations. You can specify a number between 0 and 1; if number does not evenly divide the unit interval, then it is reduced to a valid value. By default, BINEPS=1e–5. For more information, see the section Classification Table and ROC Curves.

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).

displays a table for binary or binomial response models that, for a set of probabilities or cutpoints, contains the frequencies of observations that are correctly and incorrectly classified as events and nonevents, and optionally the sensitivity, the 1–specificity, the positive and negative predictive values, the correct classification rate, the error rate, and the lift.

Because the number of cutpoints that are generated from your data can be very large, you can write the classification table to an output data table, or you can specify your own list of cutpoints.

If you specify the PRIOR= option, then the reported predicted probabilities are unchanged, but the statistics that are specified by the keywords PPV, NPV, PC, ACCURACY, MISCLASS, and LIFT are modified.

If you specify a PARTITION statement, then a table is created for each role. If you do not specify the PRIOR= option, then the training proportions are used as the prevalences for computing the statistics that are specified by the keywords PPV, NPV, PC, ACCURACY, MISCLASS, and LIFT for the validation and test data.

You can specify the following options. For more information, see the section Classification Table and ROC Curves.

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

names an input data table that contains starting values. CAS-libref.data-table is a two-level name, where CAS-libref refers to the caslib and session identifier, and data-table specifies the name of the output data table. For more information about this two-level name, see the DATA= option and the section Using CAS Sessions and CAS Engine Librefs.

The input data table must contain an Estimate column and a ParmName column, and it can optionally contain your BY variables. You can create a version of this table by using an ODS OUTPUT statement to produce the "Parameter Estimates" table from a PROC LOGSELECT program. For example:

ods output parameterestimates=pe;

You can then modify the parameter estimates in this table and read them back in by using the following statement:

proc logselect inparmest=pe;

generates the "Iteration History" table.

performs the Hosmer-Lemeshow goodness-of-fit test for binary and polytomous response models (Hosmer and Lemeshow 2000; Fagerland, Hosmer, and Bofin 2008; Fagerland and Hosmer 2013, 2016). The subjects are divided into at most G groups of approximately the same size. You can specify G as any integer greater than or equal to 5; by default, G = 10. The discrepancies between the observed and expected numbers of observations in these groups are summarized by the Pearson chi-square statistic, which is then compared to a chi-square distribution. The number of degrees of freedom that PROC LOGSELECT uses for this test depends on the specified model; you can override this number by specifying a nonnegative integer, df, and by modifying the reduction, r. A small p-value suggests that the fitted model is not an adequate model. For more information, see the section The Hosmer-Lemeshow Goodness-of-Fit Test.

The following options are available:

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.

specifies the maximum number of response levels that are allowed when you have a polytomous response variable. By default, MAXRESPONSELEVELS=100.

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.

specifies the maximum number of observations to be included in a batch. During the optimization, the LOGSELECT 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 number decreases memory usage but might lead to longer computation times, whereas a larger number might lead to shorter computation times but increases memory usage. The default PAGEOBS=AUTO option determines whether the entire data table can be conveniently held in memory; if it cannot, then number is set to 256. For more information, see the section Memory Usage.

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 additional statistics include the R-square, the adjusted R-square, McFadden’s R-square, the average square error (Brier score), the misclassification rate, the null-model log likelihood, and the difference in means.

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.

continues to perform computations by using the last iteration of the optimization when the optimization fails. By default, computations cease when a failure occurs.