Stata 15 help for bayes_ologit

[BAYES] bayes: ologit -- Bayesian ordered logistic regression

Syntax

bayes [, bayesopts] : ologit depvar [indepvars] [if] [in] [weight] [, options]

options Description ------------------------------------------------------------------------- Model offset(varname) include varname in model with coefficient constrained to 1 collinear keep collinear variables

Reporting or report odds ratios 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 and indepvars may contain time-series operators; see tsvarlist. fweights are allowed; see weight. bayes: ologit, level() is equivalent to bayes, clevel(): ologit. For a detailed description of options, see Options in [R] ologit.

bayesopts Description ------------------------------------------------------------------------- Priors * normalprior(#) specify standard deviation of default normal priors for regression coefficients; default is normalprior(100)

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

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

Reporting clevel(#) set credible interval level; default is clevel(95) hpd display HPD credible intervals instead of the default equal-tailed credible intervals * or report odds ratios eform[(string)] report exponentiated coefficients and, optionally, label as string 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 dots display dots every 100 iterations and iteration numbers every 1,000 iterations dots(#[, every(#)]) display dots as simulation is performed [no]show(paramref) specify model parameters to be excluded from or included in the output 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} and cutpoints {cut1}, {cut2}, and so on. 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.

Menu

Statistics > Ordinal outcomes > Bayesian regression > Ordered logistic regression

Description

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

Examples

Setup . webuse fullauto

Fit Bayesian ordered logistic regression using default priors . bayes: ologit rep77 mpg foreign

Display odds ratios instead of coefficients . bayes, or

Increase the burn-in period to 5,000 from the default of 2,500 . bayes, burnin(5000): ologit rep77 mpg foreign

Same as above, but use standard deviation of 10 of the default normal prior for regression coefficients . bayes, normalprior(10) burnin(5000): ologit rep77 mpg foreign

Same as above, but also specify random-number seed for reproducibility . bayes, normalprior(10) burnin(5000) rseed(12345): ologit rep77 mpg foreign

Fit Bayesian ordered logistic regression using uniform priors for all regression coefficients and normal priors for cutpoints . bayes, prior({rep77:mpg foreign}, uniform(-5,5)) prior({cut1 cut2 cut3 cut4}, normal(0,10)): ologit rep77 mpg foreign

Same as above, but use a shortcut notation to refer to all regression coefficients . bayes, prior({rep77:}, uniform(-5,5)) prior({cut1 cut2 cut3 cut4}, normal(0,10)): ologit rep77 mpg foreign

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

Stored results

See Stored results in [BAYES] bayesmh.


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