ODS Table Names

The LOGISTIC Procedure

ODS Table Names

PROC LOGISTIC assigns a name to each table it creates. You can use these names to reference the table when using the Output Delivery System (ODS) to select tables and create output data sets. These names are listed in the following table. For more information on ODS, see Chapter 15, "Using the Output Delivery System."

Table 39.2: ODS Tables Produced in PROC LOGISTIC

ODS Table Name	Description	Statement	Option
Association	Association of predicted probabilities and observed responses	MODEL	default
BestSubsets	Best subset selection	MODEL	SELECTION=SCORE
ClassFreq	Frequency breakdown of CLASS variables	PROC	Simple (with CLASS vars)
ClassLevelInfo	CLASS variable levels and design variables	MODEL	default (with CLASS vars)
Classification	Classification table	MODEL	CTABLE
ClassWgt	Weight breakdown of CLASS variables	PROC, WEIGHT	Simple (with CLASS vars)
CLOddsPL	Profile likelihood confidence limits for odds ratios	MODEL	CLODDS=PL
CLOddsWald	Wald's confidence limits for odds ratios	MODEL	CLODDS=WALD
CLParmPL	Profile likelihood confidence limits for parameters	MODEL	CLPARM=PL
CLParmWald	Wald's confidence limits for parameters	MODEL	CLPARM=WALD
ContrastCoeff	L matrix from CONTRAST	CONTRAST	E
ContrastEstimate	Estimates from CONTRAST	CONTRAST	ESTIMATE=
ContrastTest	Wald test for CONTRAST	CONTRAST	default
ConvergenceStatus	Convergence status	MODEL	default
CorrB	Estimated correlation matrix of parameter estimators	MODEL	CORRB
CovB	Estimated covariance matrix of parameter estimators	MODEL	COVB
CumulativeModelTest	Test of the cumulative model assumption	MODEL	(ordinal response)
EffectNotInModel	Test for effects not in model	MODEL	SELECTION=S/F
FastElimination	Fast backward elimination	MODEL	SELECTION=B,FAST
FitStatistics	Model fit statistics	MODEL	default
GlobalScore	Global score test	MODEL	NOFIT
GlobalTests	Test for global null hypothesis	MODEL	default
GoodnessOfFit	Pearson and deviance goodness-of-fit tests	MODEL	SCALE
IndexPlots	Batch capture of the index plots	MODEL	IPLOTS
Influence	Regression diagnostics	MODEL	INFLUENCE
IterHistory	Iteration history	MODEL	ITPRINT
LackFitChiSq	Hosmer-Lemeshow chi-square test results	MODEL	LACKFIT
LackFitPartition	Partition for the Hosmer- Lemeshow test	MODEL	LACKFIT
LastGradient	Last evaluation of gradient	MODEL	ITPRINT
LogLikeChange	Final change in the log likelihood	MODEL	ITPRINT
ModelBuildingSummary	Summary of model building	MODEL	SELECTION=B/F/S
ModelInfo	Model information	PROC	default
OddsRatios	Odds ratios	MODEL	default
ParameterEstimates	Maximum likelihood estimates of model parameters	MODEL	default
RSquare	R-square	MODEL	RSQUARE
ResidualChiSq	Residual chi-square	MODEL	SELECTION=F/B
ResponseProfile	Response profile	PROC	default
SimpleStatistics	Summary statistics for explanatory variables	PROC	SIMPLE
TestPrint1	L[cov(b)]L' and Lb-c	TEST	PRINT
TestPrint2	Ginv(L[cov(b)]L') and Ginv(L[cov(b)]L')(Lb-c)	TEST	PRINT
TestStmts	Linear hypotheses testing results	TEST	default
TypeIII	Type III tests of effects	MODEL	default (with CLASS variables)

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