Stata 15 help for bayes_meologit

[BAYES] bayes: meologit -- Bayesian multilevel ordered logistic regression


bayes [, bayesopts] : meologit depvar fe_equation [|| re_equation] [|| re_equation ...] [, options]

where the syntax of fe_equation is

[indepvars] [if] [in] [weight] [, fe_options]

and the syntax of re_equation is one of the following:

for random coefficients and intercepts

levelvar: [varlist] [, re_options]

for a random effect among the values of a factor variable

levelvar: R.varname

levelvar either is a variable identifying the group structure for the random effects at that level or is _all, representing one group comprising all observations.

fe_options Description ------------------------------------------------------------------------- Model offset(varname) include varname in model with coefficient constrained to 1 -------------------------------------------------------------------------

re_options Description ------------------------------------------------------------------------- Model covariance(vartype) variance-covariance structure of the random effects; only structures independent, identity, and unstructured supported noconstant suppress constant term from the random-effects equation -------------------------------------------------------------------------

options Description ------------------------------------------------------------------------- Model collinear keep collinear variables

Reporting or report odds ratios notable suppress coefficient table noheader suppress output header nogroup suppress table summarizing groups display_options control spacing, line width, and base and empty cells

level(#) set credible level; default is level(95) ------------------------------------------------------------------------- indepvars may contain factor variables; see fvvarlist. depvar, indepvars, and varlist may contain time-series operators; see tsvarlist. fweights are allowed; see weight. bayes: meologit, level() is equivalent to bayes, clevel(): meologit. For a detailed description of options, see Options in [ME] meologit.

bayesopts Description ------------------------------------------------------------------------- Priors * normalprior(#) specify standard deviation of default normal priors for regression coefficients; default is normalprior(100) * igammaprior(# #) specify shape and scale of default inverse-gamma prior for variance components; default is igammaprior(0.01 0.01) * iwishartprior(# [...]) specify degrees of freedom and, optionally, scale matrix of default inverse-Wishart prior for unstructured random-effects covariance

prior(priorspec) prior for model parameters; this option may be repeated dryrun show model summary without estimation

Simulation mcmcsize(#) MCMC sample size; default is mcmcsize(10000) burnin(#) burn-in period; default is burnin(2500) thinning(#) thinning interval; default is thinning(1) rseed(#) random-number seed exclude(paramref) specify model parameters to be excluded from the simulation results restubs(restub1 restub2 ...) specify stubs for random-effects parameters for all levels

Blocking * blocksize(#) maximum block size; default is blocksize(50) block(paramref[, blockopts]) specify a block of model parameters; this option may be repeated blocksummary display block summary * noblocking do not block parameters by default

Initialization initial(initspec) initial values for model parameters nomleinitial suppress the use of maximum likelihood estimates as starting values initrandom specify random initial values initsummary display initial values used for simulation * noisily display output from the estimation command during initialization

Adaptation adaptation(adaptopts) control the adaptive MCMC procedure scale(#) initial multiplier for scale factor; default is scale(2.38) covariance(cov) initial proposal covariance; default is the identity matrix


clevel(#) set credible interval level; default is clevel(95) hpd display HPD credible intervals instead of the default equal-tailed credible intervals * or report coefficients as odds ratios eform[(string)] report exponentiated coefficients and, optionally, label as string remargl compute log marginal likelihood batch(#) specify length of block for batch-means calculations; default is batch(0) saving(filename[, replace]) save simulation results to filename.dta nomodelsummary suppress model summary nomesummary suppress multilevel-structure summary [no]dots suppress dots or display dots every 100 iterations and iteration numbers every 1,000 iterations; default is dots dots(#[, every(#)]) display dots as simulation is performed [no]show(paramref) specify model parameters to be excluded from or included in the output showreffects[(reref)] specify that all or a subset of random-effects parameters be included in the output melabel display estimation table using the same row labels as meologit nogroup suppress table summarizing groups notable suppress estimation table noheader suppress output header title(string) display string as title above the table of parameter estimates display_options control spacing, line width, and base and empty cells

Advanced search(search_options) control the search for feasible initial values corrlag(#) specify maximum autocorrelation lag; default varies corrtol(#) specify autocorrelation tolerance; default is corrtol(0.01) ------------------------------------------------------------------------- * Starred options are specific to the bayes prefix; other options are common between bayes and bayesmh. Options prior() and block() can be repeated. priorspec and paramref are defined in [BAYES] bayesmh. paramref may contain factor variables; see fvvarlist. See [BAYES] bayesian postestimation for features available after estimation. Model parameters are regression coefficients {depvar:indepvars}, cutpoints {cut1}, {cut2}, and so on, random effects {rename}, and either variance components {rename:sigma2} or, if option covariance(unstructured) is specified, matrix parameter {restub:Sigma,matrix}; see Likelihood model in [BAYES] bayes for how renames and restub are defined. Use the dryrun option to see the definitions of model parameters prior to estimation. Flat priors, flat, are used by default for cutpoints. For a detailed description of bayesopts, see Options in [BAYES] bayes.


Statistics > Multilevel mixed-effects models > Bayesian regression > Ordered logistic regression


bayes: meologit fits a Bayesian multilevel ordered logistic regression to an ordinal outcome; see [BAYES] bayes and [ME] meologit for details.


Setup . webuse tvsfpors . keep if school < 500

Fit Bayesian two-level ordered logit regression using default priors . bayes: meologit thk prethk cc##tv || school:

Display odds ratios instead of coefficients . bayes, or

In addition to the main model parameters, display results for random effects corresponding to school codes 193 through 199 . bayes, showreffects({U0[(193/199).school]})

Check MCMC convergence for the main model parameters . bayesgraph diagnostics _all

Check MCMC convergence for the first, 10th, and 15th random effects . bayesgraph diagnostics {U0[1 10 15]}

Plot histograms of posterior distributions of the first 12 random effects on one graph . bayesgraph histogram {U0[1/12]}, byparm

Display estimation results using meologit's parameter labels and compute log marginal likelihood on replay . bayes, melabel remargl

Save MCMC results on replay . bayes, saving(mymcmc)

Fit Bayesian three-level ordered logit regression, specifying a standard deviation of 10 instead of 100 of the default normal priors for the regression coefficients and using a smaller MCMC size of 1,000 instead of the default of 10,000 . bayes, normalprior(10) mcmcsize(1000): meologit thk prethk cc##tv || school: || class:

Stored results

See Stored results in [BAYES] bayesmh.

© Copyright 1996–2018 StataCorp LLC   |   Terms of use   |   Privacy   |   Contact us   |   What's new   |   Site index