The LOGISTIC Procedure
- Overview
- Getting Started
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Syntax
PROC LOGISTIC StatementBY StatementCLASS StatementCODE StatementCONTRAST StatementEFFECT StatementEFFECTPLOT StatementESTIMATE StatementEXACT StatementEXACTOPTIONS StatementFREQ StatementID StatementLSMEANS StatementLSMESTIMATE StatementMODEL StatementNLOPTIONS StatementODDSRATIO StatementOUTPUT StatementROC StatementROCCONTRAST StatementSCORE StatementSLICE StatementSTORE StatementSTRATA StatementTEST StatementUNITS StatementWEIGHT Statement -
DetailsMissing ValuesResponse Level OrderingLink Functions and the Corresponding DistributionsDetermining Observations for Likelihood ContributionsIterative Algorithms for Model FittingConvergence CriteriaExistence of Maximum Likelihood EstimatesEffect-Selection MethodsModel Fitting InformationGeneralized Coefficient of DeterminationScore Statistics and TestsConfidence Intervals for ParametersOdds Ratio EstimationRank Correlation of Observed Responses and Predicted ProbabilitiesLinear Predictor, Predicted Probability, and Confidence LimitsClassification TableOverdispersionThe Hosmer-Lemeshow Goodness-of-Fit TestReceiver Operating Characteristic CurvesTesting Linear Hypotheses about the Regression CoefficientsRegression DiagnosticsScoring Data SetsConditional Logistic RegressionExact Conditional Logistic RegressionInput and Output Data SetsComputational ResourcesDisplayed OutputODS Table NamesODS Graphics
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ExamplesStepwise Logistic Regression and Predicted ValuesLogistic Modeling with Categorical PredictorsOrdinal Logistic RegressionNominal Response Data: Generalized Logits ModelStratified SamplingLogistic Regression DiagnosticsROC Curve, Customized Odds Ratios, Goodness-of-Fit Statistics, R-Square, and Confidence LimitsComparing Receiver Operating Characteristic CurvesGoodness-of-Fit Tests and SubpopulationsOverdispersionConditional Logistic Regression for Matched Pairs DataFirth’s Penalized Likelihood Compared with Other ApproachesComplementary Log-Log Model for Infection RatesComplementary Log-Log Model for Interval-Censored Survival TimesScoring Data SetsUsing the LSMEANS StatementPartial Proportional Odds Model
- References
EFFECT name=effect-type (variables </ options>) ;
The EFFECT statement enables you to construct special collections of columns for design matrices. These collections are referred to as constructed effects to distinguish them from the usual model effects that are formed from continuous or classification variables, as discussed in the section GLM Parameterization of Classification Variables and Effects in Chapter 19: Shared Concepts and Topics.
You can specify the following effect-types:
- COLLECTION
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is a collection effect that defines one or more variables as a single effect with multiple degrees of freedom. The variables in a collection are considered as a unit for estimation and inference.
- LAG
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is a classification effect in which the level that is used for a given period corresponds to the level in the preceding period.
- MULTIMEMBER | MM
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is a multimember classification effect whose levels are determined by one or more variables that appear in a CLASS statement.
- POLYNOMIAL | POLY
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is a multivariate polynomial effect in the specified numeric variables.
- SPLINE
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is a regression spline effect whose columns are univariate spline expansions of one or more variables. A spline expansion replaces the original variable with an expanded or larger set of new variables.
Table 58.3 summarizes the options available in the EFFECT statement.
Table 58.3: EFFECT Statement Options
|
Option |
Description |
|---|---|
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Collection Effects Options |
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Displays the constituents of the collection effect |
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Lag Effects Options |
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Names a variable that controls to which lag design an observation is assigned |
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Displays the lag design of the lag effect |
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Specifies the number of periods in the lag |
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Names the variable that defines the period |
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Names the variable or variables that define the group within which each period is defined |
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Multimember Effects Options |
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Specifies that observations with all missing levels for the multimember variables should have zero values in the corresponding design matrix columns |
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Specifies the weight variable for the contributions of each of the classification effects |
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Polynomial Effects Options |
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Specifies the degree of the polynomial |
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Specifies the maximum degree of any variable in a term of the polynomial |
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Specifies centering and scaling suboptions for the variables that define the polynomial |
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Spline Effects Options |
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Specifies the type of basis (B-spline basis or truncated power function basis) for the spline effect |
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Specifies the degree of the spline effect |
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Specifies how to construct the knots for the spline effect |
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For more information about the syntax of these effect-types and how columns of constructed effects are computed, see the section EFFECT Statement in Chapter 19: Shared Concepts and Topics.