Stata 15 help for fracreg

[R] fracreg -- Fractional response regression


Syntax for fractional probit regression

fracreg probit depvar [indepvars] [if] [in] [weight] [, options]

Syntax for fractional logistic regression

fracreg logit depvar [indepvars] [if] [in] [weight] [, options]

Syntax for fractional heteroskedastic probit regression

fracreg probit depvar [indepvars] [if] [in] [weight], het(varlist[, offset(varname_o)]) [options]

options Description ------------------------------------------------------------------------- Model noconstant suppress constant term offset(varname) include varname in model with coefficient constrained to 1 constraints(constraints) apply specified linear constraints collinear keep collinear variables * het(varlist[, offset(varname_o]) independent variables to model the variance and possible offset variable with fracreg probit

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

Reporting level(#) set confidence level; default is level(95) or report odds ratio; only valid with fracreg logit 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

nocoef do not display the coefficient table; seldom used coeflegend display legend instead of statistics ------------------------------------------------------------------------- * het() may be used only with fracreg probit to compute fractional heteroskedastic probit regression. indepvars may contain factor variables; see fvvarlist. depvar and indepvars may contain time-series operators; see tsvarlist. bayes, bootstrap, by, fp, jackknife, mi estimate, rolling, statsby, and svy are allowed; see prefix. For more details, see [BAYES] bayes: fracreg. vce(bootstrap) and vce(jackknife) are not allowed with the mi estimate prefix. Weights are not allowed with the bootstrap prefix. vce(), nocoef, and weights are not allowed with the svy prefix. fweights, iweights, and pweights are allowed; see weight. nocoef and coeflegend do not appear in the dialog box. See [R] fracreg postestimation for features available after estimation.


Statistics > Fractional outcomes > Fractional regression


fracreg fits a fractional response model for a dependent variable that is greater than or equal to 0 and less than or equal to 1. It uses a probit, logit, or heteroskedastic probit model for the conditional mean. These models are often used for outcomes such as rates, proportions, and fractional data.


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

noconstant, offset(varname), constraints(constraints), collinear; see [R] estimation options.

het(varlist[, offset(varname_o)]) specifies the independent variables and the offset variable, if there is one, in the variance function. het() may only be used with fracreg probit to compute fractional heteroskedastic probit regression.

offset(varname_o) specifies that selection offset varname_o be included in the model with the coefficient constrained to be 1.

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

vce(vcetype) specifies the type of standard error reported, which includes types 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(#); see [R] estimation options.

or reports the estimated coefficients transformed to odds ratios, that is, e^b rather than b. Standard errors and confidence intervals are similarly transformed. This option affects how results are displayed, not how they are estimated. or may be specified at estimation or when replaying previously estimated results. This option may only be used with fracreg logit.

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 options are available with fracreg but are not shown in the dialog box:

nocoef specifies that the coefficient table not be displayed. This option is sometimes used by programmers but is of no use interactively.

coeflegend; see [R] estimation options.


Setup . webuse 401k

Use fractional probit regression to obtain consistent estimates of the parameters of the conditional mean . fracreg probit prate mrate c.ltotemp##c.ltotemp c.age##c.age i.sole

Use fractional logistic regression to obtain consistent estimates of the parameters of the conditional mean . fracreg logit prate mrate c.ltotemp##c.ltotemp c.age##c.age i.sole

Obtain the odds ratios by specifying the option or . fracreg logit prate mrate c.ltotemp##c.ltotemp c.age##c.age i.sole, or

Stored results

fracreg 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(r2_p) pseudo-R-squared 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) fracreg e(cmdline) command as typed e(estimator) model for conditional mean; logit, probit, or hetprobit e(depvar) name of dependent variable e(wtype) weight type e(wexp) weight expression e(title) title in estimation output e(clustvar) name of cluster variable e(offset) offset e(chi2type) Wald; 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(estat_cmd) program used to implement estat e(predict) program used to implement predict 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(mns) vector of means of the independent variables 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

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