Stata 15 help for intreg

[R] intreg -- Interval regression


intreg depvar1 depvar2 [indepvars] [if] [in] [weight] [, options]

depvar1 and depvar2 should have the following form:

Type of data depvar1 depvar2 ---------------------------------------------- point data a = [a,a] a a interval data [a,b] a b left-censored data (-inf,b] . b right-censored data [a,inf) a . missing . . ----------------------------------------------

options Description ------------------------------------------------------------------------- Model noconstant suppress constant term het(varlist [, noconstant]) independent variables to model the variance; use noconstant to suppress constant term offset(varname) include varname in model with coefficient constrained to 1 constraints(constraints) apply specified linear constraints collinear keep collinear variables

SE/Robust vce(vcetype) vcetype may be oim, robust, cluster clustvar, opg, 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 ------------------------------------------------------------------------- indepvars and varlist may contain factor variables; see fvvarlist. depvar1, depvar2, indepvars, and varlist may contain time-series operators; see tsvarlist. bayes, bootstrap, by, fmm, fp, jackknife, mfp, nestreg, rolling, statsby, stepwise, and svy are allowed; see prefix. For more details, see [BAYES] bayes: intreg and [FMM] fmm: intreg. Weights are not allowed with the bootstrap prefix. aweights are not allowed with the jackknife prefix. vce() and weights are not allowed with the svy prefix. aweights, fweights, iweights, and pweights are allowed; see weight. coeflegend does not appear in the dialog box. See [R] intreg postestimation for features available after estimation.


Statistics > Linear models and related > Censored regression > Interval regression


intreg fits a linear model with an outcome measured as point data, interval data, left-censored data, or right-censored data. As such, it is a generalization of the models fit by tobit.


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

noconstant; see [R] estimation options.

het(varlist[, noconstant]) specifies that the logarithm of the standard deviation be modeled as a linear combination of varlist. The constant is included unless noconstant is specified.

offset(varname), constraints(constraints), collinear; 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, opg), 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.

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.

Setting the optimization type to technique(bhhh) resets the default vcetype to vce(opg).

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

coeflegend; see [R] estimation options.


We have a dataset containing wages, truncated and in categories. Some of the observations on wages are

wage1 wage2 20 25 meaning 20000 <= wages <= 25000 50 . meaning 50000 <= wages

Setup . webuse intregxmpl

Interval regression . intreg wage1 wage2 age c.age#c.age nev_mar rural school tenure

Same as above, but suppress constant term . intreg wage1 wage2 age c.age#c.age nev_mar rural school tenure, noconstant

Stored results

intreg stores the following in e():

Scalars e(N) number of observations e(N_unc) number of uncensored observations e(N_lc) number of left-censored observations e(N_rc) number of right-censored observations e(N_int) number of interval observations e(k) number of parameters e(k_aux) number of auxiliary 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 chi-squared test e(sigma) sigma e(se_sigma) standard error of sigma e(rank) rank of e(V) e(rank0) rank of e(V) for constant-only model e(ic) number of iterations e(rc) return code e(converged) 1 if converged, 0 otherwise

Macros e(cmd) intreg e(cmdline) command as typed e(depvar) names of dependent variables e(wtype) weight type e(wexp) weight expression e(title) title in estimation output e(clustvar) name of cluster variable e(offset) linear offset 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(het) heteroskedasticity, if het() specified e(ml_score) program used to implement scores 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(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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