Stata 15 help for bayes_poisson

[BAYES] bayes: poisson -- Bayesian Poisson regression

Syntax

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

options Description ------------------------------------------------------------------------- Model noconstant suppress constant term exposure(varname_e) include ln(varname_e) in model with coefficient constrained to 1 offset(varname_o) include varname_o in model with coefficient constrained to 1 collinear keep collinear variables

Reporting irr report incidence-rate 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, indepvars, varname_e, and varname_o may contain time-series operators; see tsvarlist. fweights are allowed; see weight. bayes: poisson, level() is equivalent to bayes, clevel(): poisson. For a detailed description of options, see Options in [R] poisson.

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 * irr report incidence-rate 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}. Use the dryrun option to see the definitions of model parameters prior to estimation. For a detailed description of bayesopts, see Options in [BAYES] bayes.

Menu

Statistics > Count outcomes > Bayesian regression > Poisson regression

Description

bayes: poisson fits a Bayesian Poisson regression to a nonnegative count outcome; see [BAYES] bayes and [R] poisson for details.

Examples

Setup . webuse dollhill3

Fit Bayesian Poisson regression using default priors . bayes: poisson deaths smokes i.agecat, exposure(pyears)

Replay results and report incidence-rate ratios . bayes, irr

Increase the burn-in period to 5,000 from the default of 2,500 . bayes, burnin(5000): poisson deaths smokes i.agecat, exposure(pyears)

Same as above, but use standard deviation of 10 of the default normal prior for regression coefficients . bayes, normalprior(10) burnin(5000): poisson deaths smokes i.agecat, exposure(pyears)

Same as above, but also specify random-number seed for reproducibility . bayes, normalprior(10) burnin(5000) rseed(12345): poisson deaths smokes i.agecat, exposure(pyears)

Fit Bayesian Poisson regression using uniform priors for all regression coefficients . bayes, prior({deaths:smokes i.agecat _cons}, uniform(-10,10)): poisson deaths smokes i.agecat, exposure(pyears)

Same as above, but use a shortcut notation to refer to all regression coefficients . bayes, prior({deaths:}, uniform(-10,10)): poisson deaths smokes i.agecat, exposure(pyears)

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

Stored results

See Stored results in [BAYES] bayesmh.


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