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 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
ODS Graphics
Statistical procedures use ODS Graphics to create graphs as part of their output. ODS Graphics is described in detail in Chapter 21: Statistical Graphics Using ODS.
Before you create graphs, ODS Graphics must be enabled (for example, by specifying the ODS GRAPHICS ON statement). For more information about enabling and disabling ODS Graphics, see the section Enabling and Disabling ODS Graphics in Chapter 21: Statistical Graphics Using ODS.
The overall appearance of graphs is controlled by ODS styles. Styles and other aspects of using ODS Graphics are discussed in the section A Primer on ODS Statistical Graphics in Chapter 21: Statistical Graphics Using ODS.
You must also specify the options in the PROC LOGISTIC statement that are indicated in Table 54.14.
When ODS Graphics is enabled, then the EFFECT, EFFECTPLOT, ESTIMATE, LSMEANS, LSMESTIMATE, and SLICE statements can produce plots that are associated with their analyses. For information about these plots, see the corresponding sections of Chapter 19: Shared Concepts and Topics.
PROC LOGISTIC assigns a name to each graph it creates using ODS. You can use these names to reference the graphs when using ODS. The names are listed in Table 54.14.
Table 54.14: Graphs Produced by PROC LOGISTIC
|
ODS Graph Name |
Plot Description |
Statement or Option |
|
|---|---|---|---|
|
Panel of dfbetas by case number |
PLOTS=DFBETASor MODEL / INFLUENCE or IPLOTS |
||
|
Effect dfbetas by case number |
PLOTS=DFBETAS(UNPACK) |
||
|
Difchisq and/or difdev by predicted probability by CI displacement C |
PLOTS=DPC |
||
|
Predicted probability |
PLOTS=EFFECT |
||
|
Panel of influence statistics by case number |
PLOTS=INFLUENCEor MODEL / INFLUENCE or IPLOTS |
||
|
CBarPlot |
CI displacement Cbar by case number |
PLOTS=INFLUENCE(UNPACK) |
|
|
CPlot |
CI displacement C by case number |
PLOTS=INFLUENCE(UNPACK) |
|
|
DevianceResidualPlot |
Deviance residual by case number |
PLOTS=INFLUENCE(UNPACK) |
|
|
DifChisqPlot |
Difchisq by case number |
PLOTS=INFLUENCE(UNPACK) |
|
|
DifDeviancePlot |
Difdev by case number |
PLOTS=INFLUENCE(UNPACK) |
|
|
LeveragePlot |
Hat diagonal by case number |
PLOTS=INFLUENCE(UNPACK) |
|
|
LikelihoodResidualPlot |
Likelihood residual by case number |
PLOTS=INFLUENCE(UNPACK STDRES) |
|
|
PearsonResidualPlot |
Pearson chi-square residual by case number |
PLOTS=INFLUENCE(UNPACK) |
|
|
StdDevianceResidualPlot |
Standardized deviance residual by case number |
PLOTS=INFLUENCE(UNPACK STDRES) |
|
|
StdPearsonResidualPlot |
Standardized Pearson chi-square residual by case number |
PLOTS=INFLUENCE(UNPACK STDRES) |
|
|
Panel of influence statistics by leverage |
PLOTS=LEVERAGE |
||
|
LeverageCPlot |
CI displacement C by leverage |
PLOTS=LEVERAGE(UNPACK) |
|
|
LeverageDifChisqPlot |
Difchisq by leverage |
PLOTS=LEVERAGE(UNPACK) |
|
|
LeverageDifDevPlot |
Difdev by leverage |
PLOTS=LEVERAGE(UNPACK) |
|
|
LeveragePhatPlot |
Predicted probability by leverage |
PLOTS=LEVERAGE(UNPACK) |
|
|
Odds ratios |
Default orPLOTS=ODDSRATIO andMODEL / CLODDS=or ODDSRATIO |
||
|
Panel of influence by predicted probability |
PLOTS=PHAT |
||
|
PhatCPlot |
CI displacement C by predicted probability |
PLOTS=PHAT(UNPACK) |
|
|
PhatDifChisqPlot |
Difchisq by predicted probability |
PLOTS=PHAT(UNPACK) |
|
|
PhatDifDevPlot |
Difdev by predicted probability |
PLOTS=PHAT(UNPACK) |
|
|
PhatLeveragePlot |
Leverage by predicted probability |
PLOTS=PHAT(UNPACK) |
|
|
Receiver operating characteristics curve |
PLOTS=ROCor MODEL / OUTROC=or SCORE OUTROC=or ROC |
||
|
ROC curves for comparisons |
PLOTS=ROC andMODEL / SELECTION=or ROC |
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