The MCMC Procedure
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Overview
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Getting Started
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Syntax
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Details
How PROC MCMC Works Blocking of Parameters Sampling Methods Tuning the Proposal Distribution Conjugate Sampling Initial Values of the Markov Chains Assignments of Parameters Standard Distributions Usage of Multivariate Distributions Specifying a New Distribution Using Density Functions in the Programming Statements Truncation and Censoring Some Useful SAS Functions Matrix Functions in PROC MCMC Create Design Matrix Modeling Joint Likelihood Regenerating Diagnostics Plots Caterpillar Plot Posterior Predictive Distribution Handling of Missing Data Floating Point Errors and Overflows Handling Error Messages Computational Resources Displayed Output ODS Table Names ODS Graphics
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Examples
Simulating Samples From a Known Density Box-Cox Transformation Logistic Regression Model with a Diffuse Prior Logistic Regression Model with Jeffreys’ Prior Poisson Regression Nonlinear Poisson Regression Models Logistic Regression Random-Effects Model Nonlinear Poisson Regression Random-Effects Model Multivariate Normal Random-Effects Model Change Point Models Exponential and Weibull Survival Analysis Time Independent Cox Model Time Dependent Cox Model Piecewise Exponential Frailty Model Normal Regression with Interval Censoring Constrained Analysis Implement a New Sampling Algorithm Using a Transformation to Improve Mixing Gelman-Rubin Diagnostics
- References
| ODS Table Names |
PROC MCMC 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 in the following table. For more information about ODS, see Chapter 21, Statistical Graphics Using ODS.
ODS Table Name |
Description |
Statement or Option |
|---|---|---|
Autocorrelation statistics for each parameter |
Default |
|
History of burn-in phase sampling |
MCHISTORY=BRIEF | DETAILED |
|
Optimization convergence status |
PROPCOV=method(ITPRINT) |
|
Correlation matrix of the posterior samples |
STATS=CORR |
|
Covariance matrix of the posterior samples |
STATS=COV |
|
Deviance information criterion |
DIC |
|
Effective sample size for each parameter |
Default |
|
Monte Carlo standard error for each parameter |
Default |
|
Geweke diagnostics for each parameter |
Default |
|
Heidelberger-Welch diagnostics for each parameter |
DIAGNOSTICS=HEIDEL |
|
Optimization input table |
PROPCOV=method(ITPRINT) |
|
Equal-tail and HPD intervals for each parameter |
Default |
|
Optimization iteration history |
PROPCOV=method(ITPRINT) |
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Optimization results table |
PROPCOV=method(ITPRINT) |
|
Number of observations |
Default |
|
Parameter values after either optimization |
PROPCOV=method(ITPRINT) |
|
Covariance used in proposal distribution after optimization |
PROPCOV=method(ITPRINT) |
|
Summary of the PARMS, BLOCKING, PRIOR, sampling method, and initial value specification |
Default |
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Parameter values after the tuning phase |
INIT=PINIT |
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Posterior samples for each parameter |
(for ODS output data set only) |
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Basic posterior statistics for each parameter, including sample size, mean, standard deviation, and percentiles |
Default |
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Basic posterior statistics for each prediction |
Default with any PREDDIST statement |
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Equal-tail and HPD intervals for each prediction |
Default with any PREDDIST statement |
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Optimization table |
PROPCOV=method(ITPRINT) |
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Random effect, subject variable, number of levels, and prior distribution of the random effect |
Default with any RANDOM statement |
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Raftery-Lewis diagnostics for each parameter |
DIAGNOSTICS=RAFTERY |
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History of main phase sampling |
MCHISTORY=BRIEF | DETAILED |
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Proposal covariance matrix (for continuous parameters) after the tuning phase |
INIT=PINIT |
|
Proposal probability vector (for discrete parameters) after the tuning phase |
INIT=PINIT and DISCRETE=GEO |
|
History of proposal distribution tuning |
MCHISTORY=BRIEF | DETAILED |