Stata 15 help for mprobit

[R] mprobit -- Multinomial probit regression


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

options Description ------------------------------------------------------------------------- Model noconstant suppress constant terms baseoutcome(#|lbl) outcome used for normalizing location probitparam use the probit variance parameterization 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

Integration intpoints(#) number of quadrature points

Maximization maximize_options control the maximization process; seldom used

coeflegend display legend instead of statistics ------------------------------------------------------------------------- indepvars may contain factor variables; see fvvarlist. bayes, bootstrap, by, fp, jackknife, mi estimate, rolling, statsby, and svy are allowed; see prefix. For more details, see [BAYES] bayes: mprobit. vce(bootstrap) and vce(jackknife) are not allowed with the mi estimate prefix. 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] mprobit postestimation for features available after estimation.


Statistics > Categorical outcomes > Multinomial probit regression


mprobit fits a multinomial probit model for a categorical dependent variable with outcomes that have no natural ordering. The actual values taken by the dependent variable are irrelevant. The error terms are assumed to be independent, standard normal, random variables. asmprobit relaxes the independence of irrelevant alternatives assumption by specifying correlated latent-variable errors. asmprobit also allows heteroskedastic latent-variable errors and alternative-specific independent variables.


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

noconstant suppresses the J-1 constant terms.

baseoutcome(#|lbl) specifies the outcome used to normalize the location of the latent variable. The base outcome may be specified as a number or a label. The default is to use the most frequent outcome. The coefficients associated with the base outcome are zero.

probitparam specifies to use the probit variance parameterization by fixing the variance of the differenced latent errors between the scale and the base alternatives to be one. The default is to make the variance of the base and scale latent errors one, thereby making the variance of the difference to be two.

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.

If specifying vce(bootstrap) or vce(jackknife), you must also specify baseoutcome().

+-----------+ ----+ 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.

+-------------+ ----+ Integration +------------------------------------------------------

intpoints(#) specifies the number of Gaussian quadrature points to use in approximating the likelihood. The default is 15.

+--------------+ ----+ 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 mprobit but is not shown in the dialog box:

coeflegend; see [R] estimation options.


Setup . webuse sysdsn1

Fit multinomial probit model . mprobit insure age male nonwhite

Same as above, but use outcome 2 to normalize the location of the latent variable . mprobit insure age male nonwhite, baseoutcome(2)

Stored results

mprobit stores the following in e():

Scalars e(N) number of observations e(k_out) number of outcomes e(k_points) number of quadrature points 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_indvars) number of independent variables e(k_dv) number of dependent variables e(df_m) model degrees of freedom e(ll) log simulated-likelihood e(N_clust) number of clusters e(chi2) chi-squared e(p) p-value for model test e(k_eq_base) equation number of the base outcome e(baseout) the value of depvar to be treated as the base outcome e(ibaseout) index of the base outcome e(const) 0 if noconstant is specified, 1 otherwise e(probitparam) 1 if probitparam is specified, 0 otherwise 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) mprobit e(cmdline) command as typed e(depvar) name of dependent variable e(indvars) independent variables e(wtype) weight type e(wexp) weight expression e(title) title in estimation output e(clustvar) name of cluster variable e(chi2type) Wald, type of model chi-squared test e(vce) vcetype specified in vce() e(vcetype) title used to label Std. Err. e(outeqs) outcome equations e(out#) outcome labels, #=1,...,e(k_out) 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(marginsnotok) predictions disallowed by margins e(marginsdefault) default predict() specification for margins e(asbalanced) factor variables fvset as asbalanced e(asobserved) factor variables fvset as asobserved

Matrices e(b) coefficient vector e(outcomes) outcome values 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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