BestSubsets

Best subset selection

MODEL / SELECTION=SCORE

CensoredSummary

Summary of event and censored observations

default

ClassLevelFreq

Frequency distribution of CLASS variables

CLASS, PROC / SIMPLE

ClassLevelInfo

CLASS variable levels and design variables

CLASS

ContrastCoeff

L matrix for contrasts

CONTRAST / E

ContrastEstimate

Individual contrast estimates

CONTRAST / ESTIMATE=

ContrastTest

Wald test for contrasts

CONTRAST

ConvergenceStatus

Convergence status

default

CorrB

Estimated correlation matrix of parameter estimators

MODEL / CORRB

CovB

Estimated covariance matrix of parameter estimators

MODEL / COVB

EffectsToEnter

Analysis of effects for entry

MODEL / SELECTION=F|S

EffectsToremove

Analysis of effects for removal

MODEL / SELECTION=B|S

FitStatistics

Model fit statistics

default

FunctionalFormSupTest

Supremum test for functional form

ASSESS / VAR=

GlobalScore

Global chi-square test

MODEL / NOFIT

GlobalTests

Tests of the global null
hypothesis

default

HazardRatios

Hazard ratios and confidence limits

HAZARDRATIO

IterHistory

Iteration history

MODEL /ITPRINT

LastGradient

Last evaluation of gradient

MODEL / ITPRINT

ModelBuildingSummary

Summary of model building

MODEL / SELECTION=B|F|S

ModelInfo

Model information

default

NObs

Number of observations

default

ParameterEstimates

Maximum likelihood estimates of model parameters

default

ProportionalHazardsSupTest

Supremum test for proportional hazards assumption

ASSESS / PH

ResidualChiSq

Residual chi-square

MODEL / SELECTION=F|B

ReferenceSet

Reference set of covariates for plotting

PROC / PLOTS=

SimpleStatistics

Summary statistics of input continuous explanatory variables

PROC / SIMPLE

TestAverage

Average effect for test

TEST / AVERAGE

TestCoeff

Coefficients for linear hypotheses

TEST / E

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

Type3

Type 3 chi-square tests

CLASS

AutoCorr

Autocorrelations of the posterior samples

BAYES

CensoredSummary

Numbers of the event and censored observations

PROC

ClassLevelFreq

Frequency distribution of CLASS variables

CLASS, PROC / SIMPLE

ClassLevelInfo

CLASS variable levels and design variables

CLASS

CoeffPrior

Prior distribution of the regression coefficients

BAYES

Corr

Posterior correlation matrix

BAYES / SUMMARY=CORR

Cov

Posterior covariance Matrix

BAYES / SUMMARY=COV

ESS

Effective sample sizes

BAYES / DIAGNOSTICS=ESS

FitStatistics

Fit statistics

BAYES

Gelman

Gelman-Rubin convergence diagnostics

BAYES / DIAGNOSTICS=GELMAN

Geweke

Geweke convergence diagnostics

BAYES

HazardPrior

Prior distribution of the baseline hazards

BAYES / PIECEWISE

HazardRatios

Posterior summary statistics for hazard ratios

HAZARDRATIO

Heidelberger

Heidelberger-Welch convergence diagnostics

BAYES / DIAGNOSTICS=HEIDELBERGER

InitialValues

Initial values of the Markov chains

BAYES

ModelInfo

Model information

default

NObs

Number of observations

default

MCError

Monte Carlo standard errors

BAYES / DIAGNOSTICS=MCERROR

ParameterEstimates

Maximum likelihood estimates of model parameters

default

ParmInfo

Names of regression coefficients

CLASS,BAYES

Partition

Partition of constant baseline hazard intervals

BAYES / PIECEWISE

PostIntervals

equal-tail and high probability density intervals of the posterior samples

BAYES

PosteriorSample

Posterior samples

BAYES / (for ODS output data set only)

PostSummaries

Summary statistics of the posterior samples

BAYES

Raftery

Raftery-Lewis convergence diagnostics

BAYES / DIAGNOSTICS=RAFTERY

ReferenceSet

Reference set of covariates for plotting

PROC / PLOTS=

SimpleStatistics

Summary statistics of input continuous explanatory variables

PROC / SIMPLE