Stata 15 help for betareg

[R] betareg -- Beta regression

Syntax

betareg depvar indepvars [if] [in] [weight] [, options]

options Description ------------------------------------------------------------------------- Model noconstant suppress constant term scale(varlist [, noconstant) specify independent variables for scale link(linkname) specify link function for the conditional mean; default is link(logit) slink(slinkname) specify link function for the conditional scale; default is slink(log) constraints(constraints) apply specified linear constraints

SE/Robust vce(vcetype) vcetype may be oim, robust, cluster clustvar, bootstrap, or jackknife

Reporting level(#) set confidence level; default is level(95) nocnsreport do not display constraints display_options control columns and column formats, row spacing, line width, display of omitted variables and base and empty cells, and factor-variable labeling

Maximization maximize_options control the maximization process; seldom used

coeflegend display legend instead of statistics -------------------------------------------------------------------------

linkname Description ------------------------------------------------------------------------- logit logit probit probit cloglog complementary log-log loglog log-log -------------------------------------------------------------------------

slinkname Description ------------------------------------------------------------------------- log log root square root identity identity -------------------------------------------------------------------------

indepvars and varlist specified in scale() may contain factor variables; see fvvarlist. bayes, bootstrap, by, fmm, fp, jackknife, nestreg, rolling, statsby, stepwise, and svy are allowed; see prefix. For more details, see [BAYES] bayes: betareg and [FMM] fmm: betareg. Weights are not allowed with the bootstrap prefix. vce() and weights are not allowed with the svy prefix. fweights, iweights, and pweights are allowed; see weight. coeflegend does not appear in the dialog box. See [R] betareg postestimation for features available after estimation.

Menu

Statistics > Fractional outcomes > Beta regression

Description

betareg estimates the parameters of a beta regression model. This model accommodates dependent variables that are greater than 0 and less than 1, such as rates, proportions, and fractional data.

Options

+-------+ ----+ Model +------------------------------------------------------------

noconstant; see [R] estimation options.

scale(varlist [, noconstant]) specifies the independent variables used to model the scale.

noconstant suppresses the constant term in the scale model. A constant term is included by default.

link(linkname) specifies the link function used for the conditional mean. linkname may be logit, probit, cloglog, or loglog. The default is link(logit).

slink(slinkname) specifies the link function used for the conditional scale. slinkname may be log, root, or identity. The default is slink(log).

constraints(constraints); see [R] estimation options.

+-----------+ ----+ SE/Robust +--------------------------------------------------------

vce(vcetype) specifies the type of standard error reported, which includes types that are derived from asymptotic theory (oim), that are robust to some kinds of misspecification (robust), that allow for intragroup correlation (cluster clustvar), and that use bootstrap or jackknife methods (bootstrap, jackknife); see [R] vce_option.

+-----------+ ----+ Reporting +--------------------------------------------------------

level(#), nocnsreport; see [R] estimation options.

display_options: noci, nopvalues, noomitted, vsquish, noemptycells, baselevels, allbaselevels, nofvlabel, fvwrap(#), fvwrapon(style), cformat(%fmt), pformat(%fmt), sformat(%fmt), and nolstretch; see [R] estimation options.

+--------------+ ----+ Maximization +-----------------------------------------------------

maximize_options: difficult, technique(algorithm_spec), iterate(#), [no]log, trace, gradient, showstep, hessian, showtolerance, tolerance(#), ltolerance(#), nrtolerance(#), nonrtolerance, and from(init_specs); see [R] maximize. These options are seldom used.

The following option is available with betareg but is not shown in the dialog box:

coeflegend; see [R] estimation options.

Examples

Setup . webuse sprogram

Beta regression with default logit link for the conditional mean and log link for the conditional scale . betareg prate i.summer freemeals pdonations

Same as above, but with the scale parameter as a function of freemeals . betareg prate i.summer freemeals pdonations, scale(freemeals)

Stored results

betareg stores the following in e():

Scalars e(N) number of observations e(k) number of parameters e(k_eq) number of equations in e(b) e(k_eq_model) number of equations in overall model test e(k_dv) number of dependent variables e(df_m) model degrees of freedom e(ll) log likelihood e(ll_0) log likelihood, constant-only model e(N_clust) number of clusters e(chi2) chi-squared e(p) p-value for model test e(rank) rank of e(V) e(ic) number of iterations e(rc) return code e(converged) 1 if converged, 0 otherwise

Macros e(cmd) betareg e(cmdline) command as typed e(depvar) name of dependent variable e(wtype) weight type e(wexp) weight expression e(title) title in estimation output e(linkt) link title in the conditional mean equation e(linkf) link function in the conditional mean equation e(slinkt) link title in the conditional scale equation e(slinkf) link function in the conditional scale equation e(clustvar) name of cluster variable e(chi2type) Wald or LR; type of model chi-squared test e(vce) vcetype specified in vce() e(vcetype) title used to label Std. Err. e(opt) type of optimization e(which) max or min; whether optimizer is to perform maximization or minimization e(ml_method) type of ml method e(user) name of likelihood-evaluator program e(technique) maximization technique e(properties) b V e(predict) program used to implement predict e(marginsok) predictions allowed by margins e(marginsnotok) predictions disallowed by margins e(asbalanced) factor variables fvset as asbalanced e(asobserved) factor variables fvset as asobserved

Matrices e(b) coefficient vector e(Cns) constraints matrix e(ilog) iteration log (up to 20 iterations) e(gradient) gradient vector e(V) variance-covariance matrix of the estimators e(V_modelbased) model-based variance

Functions e(sample) marks estimation sample


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