PROC LIFEREG: ODS Table Names :: SAS/STAT(R) 9.22 User's Guide

The LIFEREG Procedure

ODS Table Names

PROC LIFEREG 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 separately in Table 48.4 for a maximum likelihood analysis and in Table 48.5 for a Bayesian analysis. For more information about ODS, see Chapter 20, Using the Output Delivery System.

Table 48.4 ODS Tables Produced in PROC LIFEREG for a Classical Analysis
ODS Table Name	Description	Statement	Option
ClassLevels	Classification variable levels	CLASS	Default $\text{[math]}$
ConvergenceStatus	Convergence status	MODEL	Default
CorrB	Parameter estimate correlation matrix	MODEL	CORRB
CovB	Parameter estimate covariance matrix	MODEL	COVB
IterEM	Iteration history for Turnbull algorithm	PROBPLOT	ITPRINTEM
FitStatistics	Fit statistics	MODEL	Default
FitStatisticsUL	Fit statistics for unlogged response	MODEL	DISTRIBUTION=WEIBULL, LOGNORMAL, LLOGISTIC, or GAMMA
IterHistory	Iteration history	MODEL	ITPRINT
LagrangeStatistics	Lagrange statistics	MODEL	NOINT \| NOSCALE
LastGrad	Last evaluation of the gradient	MODEL	ITPRINT
LastHess	Last evaluation of the Hessian	MODEL	ITPRINT
ModelInfo	Model information	MODEL	Default
NObs	Number of observations	MODEL	Default
ParameterEstimates	Parameter estimates	MODEL	Default
ParmInfo	Parameter indices	MODEL	Default
ProbabilityEstimates	Nonparametric CDF estimates	PROBPLOT	PPOUT
TConvergenceStatus	Convergence status for Turnbull algorithm	PROBPLOT	Default
Turnbull	Probability estimates from Turnbull algorithm	PROBPLOT	ITPRINTEM
Type3Analysis	Type 3 tests	MODEL	Default $\text{[math]}$

$\text{[math]}$ Depends on data.

Table 48.5 ODS Tables Produced in PROC LIFEREG for a Bayesian Analysis
ODS Table Name	Description	Statement	Option
AutoCorr	Autocorrelations of the posterior samples	BAYES	Default
ClassLevels	Classification variable levels	CLASS	Default $\text{[math]}$
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
MCError	Monte Carlo standard errors	BAYES	DIAG=MCSE
ModelInfo	Model information	MODEL	Default
NObs	Number of observations	MODEL	Default
ParameterEstimates	Maximum likelihood estimates of model parameters	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 output data set only)	BAYES
PostSummaries	Summary statistics of the posterior samples	BAYES	Default
Raftery	Raftery and Lewis convergence diagnostics	BAYES	DIAG=RAFTERY

$\text{[math]}$ Depends on data.

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