The LOGISTIC Procedure
- Overview
- Getting Started
-
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 CoefficientsJoint Tests and Type 3 TestsRegression 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 DataExact Conditional Logistic RegressionFirth’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
The EFFECTPLOT statement produces a display of the fitted model and provides options for changing and enhancing the displays. Table 60.4 describes the available plot-types and their plot-definition-options.
Table 60.4: Plot-Types and Plot-Definition-Options
|
Plot-Type and Description |
Plot-Definition-Options |
|---|---|
|
Displays a box plot of continuous response data at each level of a CLASS effect, with predicted values superimposed and connected by a line. This is an alternative to the INTERACTION plot-type. |
PLOTBY=
variable or CLASS effect |
|
Displays a contour plot of predicted values against two continuous covariates |
PLOTBY=
variable or CLASS effect |
|
Displays a curve of predicted values versus a continuous variable |
|
|
Displays a plot of predicted values (possibly with error bars) versus the levels of a CLASS effect. The predicted values are connected with lines and can be grouped by the levels of another CLASS effect. |
PLOTBY=
variable or CLASS effect |
|
Displays a mosaic plot of predicted values by using up to three CLASS effects |
|
|
Displays a curve of predicted values versus a continuous variable, grouped by the levels of a CLASS effect |
PLOTBY=
variable or CLASS effect |
For full details about the syntax and options of the EFFECTPLOT statement, see the section EFFECTPLOT Statement in Chapter 19: Shared Concepts and Topics.
See Outputs Output 60.2.11, Output 60.2.12, Output 60.3.5, Output 60.4.8, Output 60.7.4, and Output 60.16.4 for examples of plots produced by this statement.