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 50.6 for a maximum likelihood analysis and in Table 50.7 for a Bayesian analysis. For more information about ODS, see Chapter 20, Using the Output Delivery System.

Table 50.6 ODS Tables Produced in PROC LIFEREG for a Classical Analysis

ODS Table Name

Description

Statement

Option

ClassLevels

Classification variable levels

CLASS

Default

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

Depending on the data.

Table 50.7 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

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

Depending on the data.