The LOGISTIC Procedure
- Overview
- Getting Started
-
Syntax
PROC LOGISTIC Statement BY Statement CLASS Statement CONTRAST Statement EFFECT Statement EFFECTPLOT Statement ESTIMATE Statement EXACT Statement EXACTOPTIONS Statement FREQ Statement LSMEANS Statement LSMESTIMATE Statement MODEL Statement ODDSRATIO Statement OUTPUT Statement ROC Statement ROCCONTRAST Statement SCORE Statement SLICE Statement STORE Statement STRATA Statement TEST Statement UNITS Statement WEIGHT Statement -
Details
Missing Values Response Level Ordering Link Functions and the Corresponding Distributions Determining Observations for Likelihood Contributions Iterative Algorithms for Model Fitting Convergence Criteria Existence of Maximum Likelihood Estimates Effect-Selection Methods Model Fitting Information Generalized Coefficient of Determination Score Statistics and Tests Confidence Intervals for Parameters Odds Ratio Estimation Rank Correlation of Observed Responses and Predicted Probabilities Linear Predictor, Predicted Probability, and Confidence Limits Classification Table Overdispersion The Hosmer-Lemeshow Goodness-of-Fit Test Receiver Operating Characteristic Curves Testing Linear Hypotheses about the Regression Coefficients Regression Diagnostics Scoring Data Sets Conditional Logistic Regression Exact Conditional Logistic Regression Input and Output Data Sets Computational Resources Displayed Output ODS Table Names ODS Graphics
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Examples
Stepwise Logistic Regression and Predicted Values Logistic Modeling with Categorical Predictors Ordinal Logistic Regression Nominal Response Data: Generalized Logits Model Stratified Sampling Logistic Regression Diagnostics ROC Curve, Customized Odds Ratios, Goodness-of-Fit Statistics, R-Square, and Confidence Limits Comparing Receiver Operating Characteristic Curves Goodness-of-Fit Tests and Subpopulations Overdispersion Conditional Logistic Regression for Matched Pairs Data Firth’s Penalized Likelihood Compared with Other Approaches Complementary Log-Log Model for Infection Rates Complementary Log-Log Model for Interval-Censored Survival Times Scoring Data Sets Using the LSMEANS Statement
- References
| EFFECT Statement |
- 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 formed from continuous or classification variables, as discussed in the section GLM Parameterization of Classification Variables and Effects of Chapter 19, Shared Concepts and Topics.
The following effect-types are available:
- COLLECTION
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
-
is a classification effect in which the level that is used for a given period corresponds to the level in the preceding period.
Note: The LAG effect-type is experimental in this release.
- MULTIMEMBER
|MM is a multimember classification effect whose levels are determined by one or more variables that appear in a CLASS statement.
- POLYNOMIAL|POLY
is a multivariate polynomial effect in the specified numeric variables.
- SPLINE
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 53.2 summarizes important options for each type of EFFECT statement.
Option |
Description |
|---|---|
Options for Collection Effects |
|
Displays the constituents of the collection effect |
|
Options for Lag Effects |
|
Names a variable that controls to which lag design an observation is assigned |
|
Displays the lag design of the lag effect |
|
Specifies the number of periods in the lag |
|
Names the variable that defines the period |
|
Names the variable or variables that define the group within which each period is defined |
|
Options for Multimember Effects |
|
Specifies that observations with all missing levels for the multimember variables should have zero values in the corresponding design matrix columns |
|
Specifies the weight variable for the contributions of each of the classification effects |
|
Options for Polynomial Effects |
|
Specifies the degree of the polynomial |
|
Specifies the maximum degree of any variable in a term of the polynomial |
|
Specifies centering and scaling suboptions for the variables that define the polynomial |
|
Options for Spline Effects |
|
Specifies the type of basis (B-spline basis or truncated power function basis) for the spline expansion |
|
Specifies the degree of the spline transformation |
|
Specifies how to construct the knots for spline effects |
|
For further details about the syntax of these effect-types and how columns of constructed effects are computed, see the section EFFECT Statement of Chapter 19, Shared Concepts and Topics.