Fits generalized linear regression models.
| Parameter | Description |
|---|---|
| alpha | Specifies the significance level to use for the construction of all confidence intervals. Default: 0.05. Range: (0, 1). |
| applyRowOrder | When set to True, uses the available groupBy and orderBy information to group and order the data. Default: FALSE. |
| attributes | Changes the attributes of variables used in this action. Currently, attributes specified on the inputs and nominals parameter are ignored. For more information, see the common casinvardesc parameter. |
| class | Names the classification variables to be used as explanatory variables in the analysis. For more information, see class Parameter (Shared Concepts) and the common classStatement parameter. |
| classGlobalOpts | Lists options that apply to all classification variables. For more information, see the common classopts parameter. |
| classLevelsPrint | When set to False, suppresses the display of class levels. Default: TRUE. |
| clb | When set to True, displays upper and lower confidence limits for the parameter estimates. Possible values: TRUE, FALSE, "WALD", "PL". |
| code | Writes SAS DATA step code for computing predicted values of the fitted model. For more information, see the common aircodegen parameter. |
| collection | Defines a set of variables that are treated as a single effect that has multiple degrees of freedom. For more information, see Collection Effects (Shared Concepts). |
| corrB | When set to True, displays the correlation matrix of the parameters. Default: FALSE. |
| covB | When set to True, displays the covariance matrix of the parameters. Default: FALSE. |
| display | Specifies a list of results tables to send to the client for display. For more information, see display Parameter (Shared Concepts) and the common displayTables parameter. |
| fitData | When set to True, specifies that the data to be scored were also used to fit the model. Default: FALSE. |
| freq | Names the numeric variable that contains the frequency of occurrence of each observation. |
| inputs | Specifies variables to use for analysis. For more information, see the common casinvardesc parameter. |
| lsmeans | Specifies the effects and subparameters for least squares means. |
| maxOptBatch | Controls the number of observations processed in one batch. For more information, see the PAGEOBS= option in the PROC GENSELECT statement (GENSELECT Procedure in SAS Visual Statistics: Procedures). |
| maxResponseLevels | Specifies the maximum number of levels allowed for a multinomial response. Default: 100. Minimum value: 2. |
| model | Names the dependent variable, explanatory effects, and model options. For information about model specification, see Introduction (Specifying Linear Models for SAS Viya Analytical Actions). |
| multimember | Uses one or more classification variables specified in the vars parameter in such a way that each observation can be associated with one or more levels of the union of the levels of the classification variables. For more information, see Multimember Effects (Shared Concepts) and the common multimember parameter. |
| nClassLevelsPrint | Limits the display of class levels. The value 0 suppresses all levels. Minimum value: 0. |
| noCheck | When set to True, does not check logistic models for separation. Default: FALSE. |
| nominals | Specifies nominal variables to use for analysis. For more information, see the common casinvardesc parameter. |
| normalize | When set to True, divides the log likelihood by the total number of observations during the optimization. Default: TRUE. |
| nostderr | When set to True, the covariance matrix and any statistic that depends on it are not computed. Default: FALSE. |
| noxpx | When set to True, does not compute X'WX and Hessian matrices, and disables all methods and suppresses all outputs that rely on them. Default: FALSE. |
| optimization | Specifies the technique and options for performing the optimization. For more information, see the description of the parameters in Optimization Parameters (Shared Concepts). |
| output | Creates a table on the server that contains observationwise statistics, which are computed after fitting the model. For more information, see Predicted Values and Regression Diagnostics (GENSELECT Procedure in SAS Visual Statistics: Procedures) and OUTPUT Statement (GENSELECT Procedure in SAS Visual Statistics: Procedures). |
| outputTables | Lists the names of results tables to save as CAS tables on the server. For more information, see the common outputTables parameter. |
| parmEstLevDetails | Specifies whether to add raw and formatted values of classification variables in the ParameterEstimates table. Possible values: "NONE", "RAW", "RAW_AND_FORMATTED". Default: RAW. |
| partByFrac | Specifies the fractions of the data to be used for validation and testing. For more information, see partByFrac and partByVar Partitioning Parameters (Shared Concepts). |
| partByVar | Names the variable and its values used to partition the data into training, validation, and testing roles. For more information, see partByFrac and partByVar Partitioning Parameters (Shared Concepts). |
| partFit | When set to True, displays the fit statistics that are produced when your data are partitioned. Default: FALSE. |
| plConv | Specifies the convergence criterion for the profile likelihood computations. Default: 0.0001. Range: 0–1. |
| plMaxIter | Specifies the maximum number of iterations for the profile likelihood computations. Default: 25. Minimum value: 0. |
| plSingular | Specifies the tolerance for testing singularity for profile likelihood computations. Range: 0–1. |
| polynomial | Specifies a polynomial effect. All specified variables must be numeric. A design matrix column is generated for each term of the specified polynomial. By default, each of these terms is treated as a separate effect for the purpose of model building. For more information, see Polynomial Effects (Shared Concepts) and the common polynomial parameter. |
| repeated | Specifies the options for repeated measures analysis. |
| restrictions | Specifies linear restrictions to be imposed on the parameter estimates. |
| seed | Specifies a seed for starting the pseudorandom number generator. Default: 0. Range: 0–4294967295. |
| selection | Specifies the method and options for performing model selection. For more information, see selection Parameter (Shared Concepts). |
| spline | Expands variables into spline bases whose form depends on the specified parameters. For more information, see Spline Effects (Shared Concepts) and the common spline parameter. |
| ss3 | When set to True, performs Type 3 effect tests. Under full-rank parameterizations or models with constructed effects, Type 3 effect tests are replaced by joint tests. The joint test for an effect is a test that all the parameters associated with that effect are zero. Such joint tests might not be equivalent to Type 3 effect tests under GLM parameterization. Default: FALSE. |
| store | Stores regression models to a binary large object (BLOB). |
| storetext | Specifies text to store that gets displayed when you restore the model. |
| table | Specifies the input data table. For more information, see the common castable (Form 1) parameter. |
| target | Specifies the target variable to use for analysis. |
| useLastIter | When equal to 1, displays all tables even if there is an optimization error. Default: FALSE. |
| weight | Names the numeric variable to use to perform a weighted analysis of the data. |
| weightNorm | Adjusts the weights so the total weight equals the total frequency. Default: FALSE. |