PROC GENMOD: ODS Table Names :: SAS/STAT(R) 9.2 User's Guide, Second Edition

The GENMOD Procedure

ODS Table Names

PROC GENMOD assigns a name to each table that 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 separately in Table 37.4 for a maximum likelihood analysis and in Table 37.5 for a Bayesian analysis. For more information about ODS, see Chapter 20, Using the Output Delivery System.

Table 37.4 ODS Tables Produced in PROC GENMOD for a Classical Analysis
ODS Table Name	Description	Statement	Option
AssessmentSummary	Model assessment summary	ASSESS	default
ClassLevels	Classification variable levels	CLASS	default
Contrasts	Tests of contrasts	CONTRAST	default
ContrastCoef	Contrast coefficients	CONTRAST	E
ConvergenceStatus	Convergence status	MODEL	default
CorrB	Parameter estimate correlation matrix	MODEL	CORRB
CovB	Parameter estimate covariance matrix	MODEL	COVB
Estimates	Estimates of contrasts	ESTIMATE	default
EstimateCoef	Contrast coefficients	ESTIMATE	E
GEEEmpPEst	GEE parameter estimates with empirical standard errors	REPEATED	default
GEEFitCriteria	GEE QIC fit criteria	REPEATED	default
GEELogORInfo	GEE log odds ratio model information	REPEATED	LOGOR=
GEEModInfo	GEE model information	REPEATED	default
GEEModPEst	GEE parameter estimates with model-based standard errors	REPEATED	MODELSE
GEENCorr	GEE model-based correlation matrix	REPEATED	MCORRB
GEENCov	GEE model-based covariance matrix	REPEATED	MCOVB
GEERCorr	GEE empirical correlation matrix	REPEATED	ECORRB
GEERCov	GEE empirical covariance matrix	REPEATED	ECOVB
GEEWCorr	GEE working correlation matrix	REPEATED	CORRW
IterContrasts	Iteration history for contrasts	MODEL CONTRAST	ITPRINT
IterLRCI	Iteration history for likelihood ratio confidence intervals	MODEL	LRCI ITPRINT
IterParms	Iteration history for parameter estimates	MODEL	ITPRINT
IterParmsGEE	Iteration history for GEE parameter estimates	MODEL REPEATED	ITPRINT
IterType3	Iteration history for Type 3 statistics	MODEL	TYPE3 ITPRINT
LRCI	Likelihood ratio confidence intervals	MODEL	LRCI ITPRINT
LSMeanCoef	Coefficients for least squares means	LSMEANS	E
LSMeanDiffs	Least squares means differences	LSMEANS	DIFF
LSMeans	Least squares means	LSMEANS	default
LagrangeStatistics	Lagrange statistics	MODEL	NOINT \| NOSCALE
LastGEEGrad	Last evaluation of the generalized gradient and Hessian	MODEL REPEATED	ITPRINT
LastGradHess	Last evaluation of the gradient and Hessian	MODEL	ITPRINT
LinDep	Linearly dependent rows of contrasts	CONTRAST	default
ModelInfo	Model information	MODEL	default
Modelfit	Goodness-of-fit statistics	MODEL	default
NObs	Number of observations summary		default
NonEst	Nonestimable rows of contrasts	CONTRAST	default
ObStats	Observation-wise statistics	MODEL	OBSTATS \| CL \| PREDICTED \| RESIDUALS \| XVARS
ParameterEstimates	Parameter estimates	MODEL	default
ParmInfo	Parameter indices	MODEL	default
ResponseProfiles	Frequency counts for multinomial and binary models	MODEL	DIST=MULTINOMIAL \| DIST=BINOMIAL
Type1	Type 1 tests	MODEL	TYPE1
Type3	Type 3 tests	MODEL	TYPE3
ZeroParameterEstimates	Parameter estimates for zero-inflated model	ZEROMODEL	default

Table 37.5 ODS Tables Produced in PROC GENMOD for a Bayesian Analysis
ODS Table Name	Description	Statement	Option
AutoCorr	Autocorrelations of the posterior samples	BAYES	default
ClassLevels	Classification variable levels	CLASS	default
CoeffPrior	Prior distribution of the regression coefficients	BAYES	default
ConvergenceStatus	Convergence status of maximum likelihood estimation	MODEL	default
Corr	Correlation matrix of the posterior samples	BAYES	SUMMARY=CORR
ESS	Effective sample size	BAYES	default
FitStatistics	Fit statistics	BAYES	default
Gelman	Gelman and Rubin convergence diagnostics	BAYES	DIAG=GELMAN
Geweke	Geweke convergence diagnostics	BAYES	default
Heidelberger	Heidelberger and Welch convergence diagnostics	BAYES	DIAG=HEIDELBERGER
InitialValues	Initial values of the Markov chains	BAYES	default
IterParms	Iteration history for parameter estimates	MODEL	ITPRINT
LastGradHess	Last evaluation of the gradient and Hessian for maximum likelihood estimation	MODEL	ITPRINT
ModelInfo	Model information	PROC	default
NObs	Number of observations		default
ParameterEstimates	Maximum likelihood estimates of model parameters	MODEL	default
ParmInfo	Parameter indices	MODEL	default
ParmPrior	Prior distribution for scale and shape	BAYES	default
PostIntervals	HPD and equal-tail intervals of the posterior samples	BAYES	default
PosteriorSample	Posterior samples (for ODS output data set only)	BAYES
PostSummaries	Summary statistics of the posterior samples	BAYES	default
Raftery	Raftery and Lewis convergence diagnostics	BAYES	DIAG=RAFTERY

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