PROC MCMC Statement 
This statement invokes PROC MCMC.
A number of options are available in the PROC MCMC statement; the following table categorizes them according to function.
Option 
Description 

Basic options 

Names the input data set 

Names the output data set for posterior samples of parameters 

Debugging output 

Displays model program and variables 

Displays compiled model program 

Displays detailed model execution messages 

Frequently used MCMC options 

Specifies the maximum number of tuning loops 

Specifies the minimum number of tuning loops 

Specifies the number of burnin iterations 

Specifies the number of MCMC iterations, excluding the burnin iterations 

Specifies the number of tuning iterations 

Controls options for constructing the initial proposal covariance matrix 

Specifies the random seed for simulation 

Specifies the thinning rate 

Less frequently used MCMC options 

Specifies the acceptance rate tolerance 

Controls sampling discrete parameters 

Controls generating initial values 

Displays Markov chain sampling history 

Specifies the proposal distribution 

Specifies the initial scale applied to the proposal distribution 

Specifies the target acceptance rate for random walk sampler 

Specifies the target acceptance rate for independence sampler 

Specifies the weight used in covariance updating 

Summary, diagnostics, and plotting options 

Specifies the number of autocorrelation lags used to compute effective sample sizes and Monte Carlo errors 

Controls the convergence diagnostics 

Computes deviance information criterion (DIC) 

Outputs analysis for a list of symbols of interest 

Controls plotting 

Controls posterior statistics 

Other Options 

Specifies the machine numerical limit for infinity 

Specifies joint loglikelihood function 

Indicates how missing values are handled. 

Controls the frequency of report for expected run time 

Specifies the singularity tolerance 
These options are described in alphabetical order.
specifies a tolerance for acceptance probabilities. By default, ACCEPTTOL=0.075.
specifies the maximum number of autocorrelation lags used in computing the effective sample size; see the section Effective Sample Size for more details. The value is used in the calculation of the Monte Carlo standard error; see the section Standard Error of the Mean Estimate. By default, AUTOCORLAG=MIN(500, MCsample/4), where MCsample is the Markov chain sample size kept after thinning—that is, MCsample . If AUTOCORLAG= is set too low, you might observe significant lags, and the effective sample size cannot be calculated accurately. A WARNING message appears, and you can either increase AUTOCORLAG= or NMC=, accordingly.
specifies the proposal distribution used in sampling discrete parameters. The default is DISCRETE=BINNING.
uses continuous proposal distributions for all discrete parameter blocks. The proposed sample is then discretized (binned) before further calculations. This sampling method approximates the correlation structure among the discrete parameters in the block and could improve mixing in some cases.
uses independent symmetric geometric proposal distributions for all discrete parameter blocks. This proposal does not take parameter correlations into account. However, it can work better than the BINNING option in cases where the range of the parameters is relatively small and a normal approximation can perform poorly.
specifies options for MCMC convergence diagnostics. By default, PROC MCMC computes the Geweke test, sample autocorrelations, effective sample sizes, and Monte Carlo errors. The RafteryLewis and HeidelbergerWelch tests are also available. See the section Assessing Markov Chain Convergence for more details on convergence diagnostics. You can request all of the diagnostic tests by specifying DIAGNOSTICS=ALL. You can suppress all the tests by specifying DIAGNOSTICS=NONE.
The following options are available.
computes all diagnostic tests and statistics. You can combine the option ALL with any other specific tests to modify test options. For example DIAGNOSTICS=(ALL AUTOCORR(LAGS=(1 5 35))) computes all tests with default settings and autocorrelations at lags 1, 5, and 35.
computes default autocorrelations at lags 1, 5, 10, and 50 for each variable. You can choose other lags by using the following autocorroptions:
specifies autocorrelation lags. The numericlist must take positive integer values.
computes the effective sample sizes (Kass et al. (1998)) of the posterior samples of each parameter. It also computes the correlation time and the efficiency of the chain for each parameter. Small values of ESS might indicate a lack of convergence. See the section Effective Sample Size for more details.
computes the Geweke spectral density diagnostics; this is a twosample test between the first portion and the last portion of the chain. See the section Geweke Diagnostics for more details. The default is FRAC1=0.1 and FRAC2=0.5, but you can choose other fractions by using the following Gewekeoptions:
specifies the beginning FRAC1 proportion of the Markov chain. By default, FRAC1=0.1.
specifies the end FRAC2 proportion of the Markov chain. By default, FRAC2=0.5.
computes the Heidelberger and Welch diagnostic (which consists of a stationarity test and a halfwidth test) for each variable. The stationary diagnostic test tests the null hypothesis that the posterior samples are generated from a stationary process. If the stationarity test is passed, a halfwidth test is then carried out. See the section Heidelberger and Welch Diagnostics for more details.
These diagnostics are not performed by default. You can specify the DIAGNOSTICS=HEIDELBERGER option to request these diagnostics, and you can also specify suboptions, such as DIAGNOSTICS=HEIDELBERGER(EPS=0.05), as follows:
specifies the level for the stationarity test. By default, SALPHA=0.05.
specifies the level for the halfwidth test. By default, HALPHA=0.05.
specifies a small positive number such that if the halfwidth is less than times the sample mean of the retaining iterates, the halfwidth test is passed. By default, EPS=0.1.
computes the Monte Carlo standard error for the posterior samples of each parameter.
suppresses all of the diagnostic tests and statistics. This is not recommended.
computes the Raftery and Lewis diagnostics, which evaluate the accuracy of the estimated quantile ( for a given Q ) of a chain. can achieve any degree of accuracy when the chain is allowed to run for a long time. The algorithm stops when the estimated probability reaches within of the value Q with probability S; that is, . See the section Raftery and Lewis Diagnostics for more details. The Rafteryoptions enable you to specify Q, R, S, and a precision level for a stationary test.
These diagnostics are not performed by default. You can specify the DIAGNOSTICS=RAFERTY option to request these diagnostics, and you can also specify suboptions, such as DIAGNOSTICS=RAFERTY(QUANTILE=0.05), as follows:
specifies the order (a value between 0 and 1) of the quantile of interest. By default, QUANTILE=0.025.
specifies a small positive number as the margin of error for measuring the accuracy of estimation of the quantile. By default, ACCURACY=0.005.
specifies the probability of attaining the accuracy of the estimation of the quantile. By default, PROB=0.95.
specifies the tolerance level (a small positive number) for the stationary test. By default, EPS=0.001.
computes the Deviance Information Criterion (DIC). DIC is calculated using the posterior mean estimates of the parameters. See the section Deviance Information Criterion (DIC) for more details.
specifies the input data set. Observations in this data set are used to compute the loglikelihood function that you specify with PROC MCMC statements.
specifies the numerical definition of infinity in the procedure. The default is INF= . For example, PROC MCMC considers to be outside of the support of the normal distribution and assigns a missing value to the log density evaluation. You can select a larger value with the INF= option. The minimum value allowed is .
specifies options for generating the initial values for the parameters. These options apply only to prior distributions that are recognized by PROC MCMC. See the section Standard Distributions for a list of these distributions. If either of the functions GENERAL or DGENERAL is used, you must supply explicit initial values for the parameters. By default, INIT=MODE. The following keywords are used:
uses the mode of the prior density as the initial value of the parameter, if you did not provide one. If the mode does not exist or if it is on the boundary of the support of the density, the mean value is used. If the mean is outside of the support or on the boundary, which can happen if the prior distribution is truncated, a random number drawn from the prior is used as the initial value.
tabulates parameter values after the tuning phase. This option also tabulates the tuned proposal parameters used by the Metropolis algorithm. These proposal parameters include covariance matrices for continuous parameters and probability vectors for discrete parameters for each block. By default, PROC MCMC does not display the initial values or the tuned proposal parameters after the tuning phase.
generates a random number from the prior density and uses it as the initial value of the parameter, if you did not provide one.
resets the parameters, after the tuning phase, with the initial values that you provided explicitly or that were assigned by the procedure. By default, PROC MCMC does not reset the parameters because the tuning phase usually moves the Markov chains to a more favorable place in the posterior distribution.
displays the model program and variable lists. The LIST option is a debugging feature and is not normally needed.
displays the compiled program code. The LISTCODE option is a debugging feature and is not normally needed.
specifies how the likelihood function is calculated. By default, PROC MCMC assumes that the observations in the data set are independent so that the joint loglikelihood function is the sum of the individual loglikelihood functions for the observations, where the individual loglikelihood function is specified in the MODEL statement. When your data are not independent, you can specify the JOINTMODEL option to modify the way that PROC MCMC computes the joint loglikelihood function. In this situation, PROC MCMC no longer steps through the input data set to sum the individual log likelihood.
To use this option correctly, you need to do the following two things:
create ARRAY symbols to store all data set variables that are used in the program. This can be accomplished with the BEGINCNST and ENDCNST statements.
program the joint loglikelihood function by using these ARRAY symbols only. The MODEL statement specifies the joint loglikelihood function for the entire data set. Typically, you use the function GENERAL in the MODEL statement.
See the sections BEGINCNST/ENDCNST Statement and Modeling Joint Likelihood for details.
specifies an upper limit for the number of proposal tuning loops. By default, MAXTUNE=24. See the section Covariance Tuning for more details.
produces a summary output for the tuning, burnin, and sampling history tables. The tables show the following when applicable:
“RWM Scale” shows the scale, or the range of the scales, used in each random walk Metropolis block that is normal or is based on a t distribution.
“Probability” shows the proposal probability parameter, or the range of the parameters, used in each random walk Metropolis block that is based on a geometric distribution.
“RWM Acceptance Rate” shows the acceptance rate, or the range of the acceptance rates, for each random walk Metropolis block.
“IM Acceptance Rate” shows the acceptance rate, or the range of the acceptance rates, for each independent Metropolis block.
produces detailed output of the tuning, burnin, and sampling history tables, including scale values, acceptance probabilities, blocking information, and so on. Use this option with caution, especially in randomeffects models that have a large number of randomeffects groups. This option can produce copious output.
produces none of the tuning history, burnin history, and sampling history tables.
The default is MCHISTORY=NONE.
specifies a lower limit for the number of proposal tuning loops. By default, MINTUNE=2. See the section Covariance Tuning for more details.
specifies how missing values are handled (see the section Handling of Missing Data for more details). The default is MISSING=COMPLETECASE.
gives you the option to model the missing values in an allcase analysis. You can use any techniques that you see fit, for example, fully Bayesian or multiple imputation.
assumes a complete case analysis, so all observations with missing variable values are discarded prior to the simulation.
outputs analysis for selected symbols of interest in the program. The symbols can be any of the following: model parameters (symbols in the PARMS statement), secondary parameters (assigned using the operator "="), the log of the posterior density (LOGPOST), the log of the prior density (LOGPRIOR), the log of the hyperprior density (LOGHYPER) if the HYPER statement is used, or the log of the likelihood function (LOGLIKE). You can use the keyword _PARMS_ as a shorthand for all of the model parameters. PROC MCMC performs only posterior analyses (such as plotting, diagnostics, and summaries) on the symbols selected with the MONITOR= option. You can also choose to monitor an entire array by specifying the name of the array. By default MONITOR=_PARMS_.
Posterior samples of any secondary parameters listed in the MONITOR= option are saved in the OUTPOST= data set. Posterior samples of model parameters are always saved to the OUTPOST= data set, regardless of whether they appear in the MONITOR= option.
specifies the number of burnin iterations to perform before beginning to save parameter estimate chains. By default, NBI=1000. See the section Burnin, Thinning, and Markov Chain Samples for more details.
specifies the number of iterations in the main simulation loop. This is the MCMC sample size if THIN=1. By default, NMC=1000.
specifies the number of iterations to use in each proposal tuning phase. By default, NTU=500.
specifies an output data set that contains the posterior samples of all model parameters, the iteration numbers (variable name ITERATION), the log of the posterior density (LOGPOST), the log of the prior density (LOGPRIOR), the log of the hyperprior density (LOGHYPER), if the HYPER statement is used, and the log likelihood (LOGLIKE). Any secondary parameters (assigned using the operator "=") listed in the MONITOR= option are saved to this data set. By default, no OUTPOST= data set is created.
controls the display of diagnostic plots. Three types of plots can be requested: trace plots, autocorrelation function plots, and kernel density plots. By default, the plots are displayed in panels unless the global plot option UNPACK is specified. Also when more than one type of plot is specified, the plots are grouped by parameter unless the global plot option GROUPBY=TYPE is specified. When you specify only one plot request, you can omit the parentheses around the plotrequest, as shown in the following example:
plots=none plots(unpack)=trace plots=(trace density)
ODS Graphics must be enabled before requesting plots. For example:
ods graphics on; proc mcmc data=exi seed=7 outpost=p1 plots=all; parm mu; prior mu ~ normal(0, sd=10); model y ~ normal(mu, sd=1); run; ods graphics off;
For more information about enabling and disabling ODS Graphics, see the section Enabling and Disabling ODS Graphics in Chapter 21, Statistical Graphics Using ODS.
If ODS Graphics is enabled but do not specify the PLOTS= option, then PROC MCMC produces, for each parameter, a panel that contains the trace plot, the autocorrelation function plot, and the density plot. This is equivalent to specifying PLOTS=(TRACE AUTOCORR DENSITY).
The globalplotoptions include the following:
adds a fringe plot to the horizontal axis of the density plot.
specifies that the plots are grouped by type.
specifies that the plots are grouped by parameter.
specifies the number of autocorrelation lags used in plotting the ACF graph. By default, LAGS=50.
smoothes the trace plot with a fitted penalized Bspline curve (Eilers and Marx; 1996).
specifies that all paneled plots are to be unpacked, so that each plot in a panel is displayed separately.
requests all types of plots. PLOTS=ALL is equivalent to specifying PLOTS=(TRACE AUTOCORR DENSITY).
displays the autocorrelation function plots for the parameters.
displays the kernel density plots for the parameters.
suppresses the display of all plots.
displays the trace plots for the parameters.
Consider a model with four parameters, X1–X4. Displays for various specifications are depicted as follows.
PLOTS=(TRACE AUTOCORR) displays the trace and autocorrelation plots for each parameter side by side with two parameters per panel:
Display 1 
Trace(X1) 
Autocorr(X1) 
Trace(X2) 
Autocorr(X2) 

Display 2 
Trace(X3) 
Autocorr(X3) 
Trace(X4) 
Autocorr(X4) 
PLOTS(GROUPBY=TYPE)=(TRACE AUTOCORR) displays all the paneled trace plots, followed by panels of autocorrelation plots:
Display 1 
Trace(X1) 

Trace(X2) 

Display 2 
Trace(X3) 

Trace(X4) 

Display 3 
Autocorr(X1) 
Autocorr(X2) 
Autocorr(X3) 
Autocorr(X4) 
PLOTS(UNPACK)=(TRACE AUTOCORR) displays a separate trace plot and a separate correlation plot, parameter by parameter:
Display 1 
Trace(X1) 
Display 2 
Autocorr(X1) 
Display 3 
Trace(X2) 
Display 4 
Autocorr(X2) 
Display 5 
Trace(X3) 
Display 6 
Autocorr(X3) 
Display 7 
Trace(X4) 
Display 8 
Autocorr(X4) 
PLOTS(UNPACK GROUPBY=TYPE)=(TRACE AUTOCORR) displays all the separate trace plots followed by the separate autocorrelation plots:
Display 1 
Trace(X1) 
Display 2 
Trace(X2) 
Display 3 
Trace(X3) 
Display 4 
Trace(X4) 
Display 5 
Autocorr(X1) 
Display 6 
Autocorr(X2) 
Display 7 
Autocorr(X3) 
Display 8 
Autocorr(X4) 
specifies the method used in constructing the initial covariance matrix for the MetropolisHastings algorithm. The QUANEW and NMSIMP methods find numerically approximated covariance matrices at the optimum of the posterior density function with respect to all continuous parameters. The optimization does not apply to discrete parameters. The tuning phase starts at the optimized values; in some problems, this can greatly increase convergence performance. If the approximated covariance matrix is not positive definite, then an identity matrix is used instead. Valid values are as follows:
uses the identity covariance matrix. This is the default. See the section Tuning the Proposal Distribution.
performs a conjugategradient optimization.
performs a doubledogleg optimization.
performs a quasiNewton optimization.
performs a NelderMead simplex optimization.
prints optimization iteration steps and results.
specifies a proposal distribution for the Metropolis algorithm. See the section Metropolis and MetropolisHastings Algorithms. You can also use PARMS statement option (see the section PARMS Statement) to change the proposal distribution for a particular block of parameters. Valid values are as follows:
specifies a normal distribution as the proposal distribution. This is the default.
specifies a t distribution with the degrees of freedom df. By default, df=3. If df , the normal distribution is used since the two distributions are almost identical.
controls the initial multiplicative scale to the covariance matrix of the proposal distribution. By default, SCALE=2.38. See the section Scale Tuning for more details.
specifies the random number seed. By default, SEED=0, and PROC MCMC gets a random number seed from the clock.
controls the number of times that PROC MCMC reports the expected run time of the simulation. This can be useful for monitoring the progress of CPUintensive programs. For example, with SIMREPORT=2, PROC MCMC reports the simulation progress twice. By default, SIMREPORT=0, and there is no reporting. The expected run times are displayed in the log file.
defines the singularity criterion in the procedure. By default, SINGDEN=1E11. The value indicates the exclusion of an endpoint in an interval. The mathematical notation "" is equivalent to "" in PROC MCMC—that is, is treated as in the procedure. The maximum SINGDEN allowed is .
specifies options for posterior statistics. By default, PROC MCMC computes the posterior mean, standard deviation, quantiles, and two credible intervals: equaltail and highest posterior density (HPD). Other available statistics include the posterior correlation and covariance. See the section Summary Statistics for more details. You can request all of the posterior statistics by specifying STATS=ALL. You can suppress all the calculations by specifying STATS=NONE.
specifies the level for the equaltail and HPD intervals. The value must be between and . By default, ALPHA=0.05.
calculates the posterior percentages. The numericlist contains values between and . By default, PERCENTAGE=(25 50 75).
computes all posterior statistics. You can combine the option ALL with any other options. For example STATS(ALPHA=(0.02 0.05 0.1))=ALL computes all statistics with the default settings and intervals at levels of 0.02, 0.05, and 0.1.
computes the posterior correlation matrix.
computes the posterior covariance matrix.
computes the posterior means, standard deviations, and percentile points for each variable. By default, the 25th, 50th, and 75th percentile points are produced, but you can use the global PERCENT= option to request specific percentile points.
computes the equaltail and HPD credible intervals for each variable. See the sections EqualTail Credible Interval and Highest Posterior Density (HPD) Interval for details. By default, ALPHA=0.05, but you can use the global ALPHA= option to request other intervals of any probabilities.
suppresses all of the statistics.
specifies the target acceptance rate for the random walk based Metropolis algorithm. See the section Metropolis and MetropolisHastings Algorithms. The numeric value must be between and . By default, TARGACCEPT=0.45 for models with 1 parameter; TARGACCEPT=0.35 for models with 2, 3, or 4 parameters; and TARGACCEPT=0.234 for models with more than 4 parameters (Roberts, Gelman, and Gilks; 1997; Roberts and Rosenthal; 2001).
specifies the target acceptance rate for the independence sampler algorithm. The independence sampler is used for blocks of binary parameters. See the section Independence Sampler for more details. The numeric value must be between and . By default, TARGACCEPTI=0.6.
controls the thinning rate of the simulation. PROC MCMC keeps every th simulation sample and discards the rest. All of the posterior statistics and diagnostics are calculated using the thinned samples. By default, THIN=1. See the section Burnin, Thinning, and Markov Chain Samples for more details.
displays the result of each operation in each statement in the model program as it is executed. This debugging option is very rarely needed, and it produces voluminous output. If you use this option, also use small NMC=, NBI=, MAXTUNE=, and NTU= numbers.
specifies the multiplicative weight used in updating the covariance matrix of the proposal distribution. The numeric value must be between 0 and 1. By default, TUNEWT=0.75. See the section Covariance Tuning for more details.