Each table created by PROC FMM has a name associated with it, and you must use this name to reference the table when you use ODS statements. These names are listed in Table 37.9.
Table 37.9: ODS Tables Produced by PROC FMM
Table Name 
Description 
Required Statement / Option 

Autocorr 
Autocorrelation among posterior estimates 

BayesInfo 
Basic information about Bayesian estimation 

ClassLevels 
Level information from the CLASS statement 

CompDescription 
Component description in models with varying number of components 

CompEvaluation 
Comparison of mixture models with varying number of components 

CompInfo 
Component information 
COMPONENTINFO option in PROC FMM statement 
ConvergenceStatus 
Status of optimization at conclusion of optimization 
Default output 
Constraints 
Linear equality and inequality constraints 
RESTRICT statement or EQUATE=EFFECTS option in MODEL statement 
Corr 
Asymptotic correlation matrix of parameter estimates (ML) or empirical correlation matrix of the Bayesian posterior estimates 

Cov 
Asymptotic covariance matrix of parameter estimates (ML) or empirical covariance matrix of the Bayesian posterior estimates 

CovI 
Inverse of the covariance matrix of the parameter estimates 

ESS 
Effective sample size 

FitStatistics 
Fit statistics 
Default output 
Geweke 
Geweke diagnostics (Geweke, 1992) for Markov chain 
DIAG=GEWEKE option in BAYES statement 
Hessian 
Hessian matrix from the maximum likelihood optimization, evaluated at the converged estimates 

IterHistory 
Iteration history 
Default output for ML estimation 
MCSE 
Monte Carlo standard errors 
DIAG=MCERROR in BAYES statement 
MixingProbs 
Solutions for the parameter estimates associated with effects in PROBMODEL statements 
Default output for ML estimation if number of components is greater than 1 
ModelInfo 
Model information 
Default output 
NObs 
Number of observations read and used, number of trials and events 
Default output 
OptInfo 
Optimization information 
Default output for ML estimation 
ParameterEstimates 
Solutions for the parameter estimates associated with effects in MODEL statements 
Default output for ML estimation 
ParameterMap 
Mapping of parameter names to OUTPOST= data set 

PriorInfo 
Prior distributions and initial value of Markov chain 

PostSummaries 
Summary statistics for posterior estimates 

PostIntervals 
Equaltail and highest posterior density intervals for posterior estimates 

ResponseProfile 
Response categories and category modeled 
Default output in models with binary response 