Stata 15 help for bayes_glm

[BAYES] bayes: glm -- Bayesian generalized linear models

Syntax

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

options Description ------------------------------------------------------------------------- Model family(familyname) distribution of depvar; default is family(gaussian) link(linkname) link function; default is canonical link for family() specified

Model 2 noconstant suppress constant term exposure(varname) include ln(varname) in model with coefficient constrained to 1 offset(varname) include varname in model with coefficient constrained to 1 collinear keep collinear variables asis retain perfect predictor variables mu(varname) use varname as the initial estimate for the mean of depvar init(varname) synonym for mu(varname)

Reporting eform report exponentiated coefficients 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: glm, level() is equivalent to bayes, clevel(): glm. For a detailed description of options, see Options in [R] glm.

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 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 [no]dots suppress dots or display dots every 100 iterations and iteration numbers every 1,000 iterations; default is nodots 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 > Generalized linear models > Bayesian generalized linear models (GLM)

Description

bayes: glm fits a Bayesian generalized linear model to outcomes of different types such as continuous, binary, count, and so on; see [BAYES] bayes and [R] glm for details.

Examples

Setup . webuse beetle

Fit Bayesian generalized linear model with a binomial family and a complementary log-log link using default priors . bayes: glm r i.beetle, family(binomial n) link(cloglog)

Replay results and report exponentiated coefficients . bayes, eform

Obtain highest posterior density intervals on replay . bayes, hpd

Obtain highest posterior density intervals at estimation time . bayes, hpd: glm r i.beetle, family(binomial n) link(cloglog)

Increase the burn-in period to 5,000 from the default of 2,500 and specify random-number seed for reproducibility . bayes, burnin(5000) rseed(12345): glm r i.beetle, family(binomial n) link(cloglog)

Same as above, but use standard deviation of 10 of the default normal prior for regression coefficients . bayes, normalprior(10) burnin(5000) rseed(12345): glm r i.beetle, family(binomial n) link(cloglog)

Use uniform priors for all regression coefficients . bayes, prior({r:i.beetle _cons}, uniform(-10,10)): glm r i.beetle, family(binomial n) link(cloglog)

Same as above, but use a shortcut notation to refer to all regression coefficients . bayes, prior({r:}, uniform(-10,10)): glm r i.beetle, family(binomial n) link(cloglog)

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

Stored results

See Stored results in [BAYES] bayesmh.


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