The BCHOICE Procedure

specifies a tolerance for acceptance probabilities for the random walk sampler. By default, ACCEPTTOL=0.075. You can specify this option for logit models.

specifies the algorithm to use to sample the posterior distribution for the regression coefficients. You can specify the following algorithms:

When possible, PROC BCHOICE samples directly from the full conditional distribution. Otherwise, the default sampling algorithm is the Gamerman algorithm for standard logit models, the random walk Metropolis algorithm for nested logit models, and the latent variables via the data augmentation method for probit models. Standard logit models can also use random walk Metropolis sampling algorithm if specified, while the other two types of models, nested logit models and probit models, have only their own default sampling algorithm.

specifies the input data set.

specifies options for convergence diagnostics. By default, PROC BCHOICE computes the effective sample sizes. The sample autocorrelations, Monte Carlo errors, Geweke test, Raftery-Lewis test, and Heidelberger-Welch test are also available. For more information about convergence diagnostics, see the section Assessing Markov Chain Convergence in Chapter 8, Introduction to Bayesian Analysis Procedures. You can request all the diagnostic tests by specifying DIAGNOSTICS=ALL. You can suppress all the diagnostic tests by specifying DIAGNOSTICS=NONE.

You can specify one or more of the following keyword-list options:

computes the deviance information criterion (DIC). DIC is calculated by using the posterior mean estimates of the parameters. For more information, see the section Deviance Information Criterion (DIC) in Chapter 8, Introduction to Bayesian Analysis Procedures.

calculates the average of estimated probabilities of all chosen alternatives in the input data set. You can use this average as a measure of goodness of fit.

specifies the numerical definition of infinity in PROC BCHOICE. By default, INF=1E15. For example, PROC BCHOICE considers 1E16 to be outside the support of the normal distribution and assigns a missing value to the log density evaluation. The minimum value that is allowed is 1E10, and the maximum value that is allowed is 1E27.

calculates the logarithm of the posterior density of the parameters and the likelihood at each iteration. As a result, the OUTPOST= data set will contain the LOGLIKE and LOGPOST variables. You can specify this option for logit models and nested logit models, but not probit models.

specifies an upper limit for the number of proposal tuning loops for the random walk sampler. You can specify this option for logit models; it is ignored for other models. By default, MAXTUNE=6.

controls the display of the Metropolis sampling history. This option is ignored for nested logit and probit models. You can specify the following values:

By default, MCHISTORY=BRIEF.

specifies a lower limit for the number of proposal tuning loops for the random walk sampler. You can specify this option for logit models; it is ignored for other models. By default, MINTUNE=2.

specifies the number of burn-in iterations to perform before beginning to save parameter estimate chains. By default, NBI=500. For more information, see the section Burn-In, Thinning, and Markov Chain Samples in Chapter 8, Introduction to Bayesian Analysis Procedures.

specifies the number of iterations in the main simulation loop. If you specify a data set in the OUTPOST= option, this is the number of posterior samples that will be saved for each parameter. This is the MCMC sample size if THIN=1. By default, NMC=5000.

suppresses the display of the "Class Level Information" table if you do not specify n. If you specify n, the values of the classification variables are displayed for only those variables whose number of levels is less than n. Specifying n helps reduce the size of the "Class Level Information" table if some classification variables have a large number of levels.

specifies the number of threads(CPUs) for analytic computations and simulation to run simultaneously on. Multi-threading is the concurrent execution of threads. When multi-threading is possible, you can realize substantial performance gains compared to the performance you get from sequential (single-threaded) execution. The more threads there are, the faster the computation runs. But do not specify a number that is more than the number of CPUs on the host where the analytic computations execute.

PROC BCHOICE performs two types of threading. In sampling fixed-effects parameters, PROC BCHOICE allocates data to different threads and accumulates values from each thread; in sampling of random-effects parameters, each thread generates a subset of these parameters simultaneously at each iteration. Most sampling algorithms are threaded. If you do not specify the NTHREADS= option or if you specify NTHREADS=0, the default number of threads is 1.

specifies the number of iterations to use in each proposal tuning phase for the random walk sampler. You can specify this option for logit models; it is ignored for other models. By default, NTU=500.

specifies an output data set to contain the posterior samples of all parameters and the iteration numbers. It contains the log of the posterior density (LOGPOST) and the log likelihood (LOGLIKE) if you specify the LOGPOST option. By default, no OUTPOST= data set is created.

controls the display of diagnostic plots. You can request three types of plots: trace plots, autocorrelation function plots, and kernel density plots. By default, the plots are displayed in panels unless you specify the global-plot-option UNPACK. Also, when you specify more than one type of plot, the plots are grouped by parameter unless you specify the global-plot-option GROUPBY=TYPE. When you specify only one plot-request, you can omit the parentheses around it, as shown in the following example:

   plots=none
   plots(unpack)=trace
   plots=(trace density)

If ODS Graphics is enabled but you do not specify the PLOTS= option, then PROC BCHOICE 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).

You can specify the following global-plot-options:

You can specify the following plot-requests:

Consider a model that has four parameters, X1–X4. The following list shows which plots are produced for various option settings:

controls the initial multiplicative scale to the covariance matrix of the proposal distribution for the random walk–based Metropolis algorithm for logit models. By default, SCALE=2.93. For more information, see the section Scale Tuning.

specifies the random number seed. By default, SEED=0, and PROC BCHOICE gets a random number seed from the system clock. Negative seed values are treated as the default. The largest possible value for the seed is . Analyses that use the same nonzero seed are reproducible. The seed value is reported in the "Model Information" table.

specifies options for posterior statistics. By default, PROC BCHOICE computes the posterior mean, standard deviation, quantiles, and two 95% credible intervals: equal-tail and highest posterior density (HPD). Other available statistics include the posterior correlation and covariance. For more information, see the section Summary Statistics in Chapter 8, Introduction to Bayesian Analysis Procedures. You can request all the posterior statistics by specifying STATS=ALL. You can suppress all the calculations by specifying STATS=NONE.

You can specify the following global-stats-options:

You can specify the following stats-requests:

specifies the target acceptance rate for the random walk–based Metropolis algorithm for logit models. For more information, see the section Metropolis and Metropolis-Hastings Algorithms in Chapter 8, Introduction to Bayesian Analysis Procedures. The numeric value must be between 0.01 and 0.99. By default, TARGACCEPT=0.45 for one parameter; TARGACCEPT=0.35 for two, three, or four parameters; and TARGACCEPT=0.234 for more than four parameters (Roberts, Gelman, and Gilks; 1997; Roberts and Rosenthal; 2001).

controls the thinning rate of the simulation. PROC BCHOICE keeps every nth simulation sample and discards the rest. All posterior statistics and diagnostics are calculated by using the thinned samples. By default, THIN=1. For more information, see the section Burn-In, Thinning, and Markov Chain Samples in Chapter 8, Introduction to Bayesian Analysis Procedures.

specifies the multiplicative weight used in updating the covariance matrix of the proposal distribution for the random walk sampler for logit models. The numeric value must be between 0 and 1. By default, TUNEWT=0.75. For more information, see the section Covariance Tuning.