help mprobit dialogs: mprobit svy: mprobit
also see: mprobit postestimation
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Title
[R] mprobit -- Multinomial probit regression
Syntax
mprobit depvar [indepvars] [if] [in] [weight], [options]
options description
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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 spacing and display of omitted
variables and base and empty cells
Integration
intpoints(#) number of quadrature points
Maximization
maximize_options control the maximization process; seldom used
+ coeflegend display coefficients' legend instead of
coefficient table
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+ coeflegend does not appear in the dialog box.
indepvars may contain factor variables; see fvvarlist.
bootstrap, by, jackknife, mi estimate, rolling, statsby, and svy are
allowed; see prefix.
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.
See [R] mprobit postestimation for features available after estimation.
Menu
Statistics > Categorical outcomes > Independent multinomial probit
Description
mprobit fits multinomial probit (MNP) models via maximum likelihood.
depvar contains the outcome for each observation, and indepvars are the
associated covariates. The error terms are assumed to be independent,
standard normal, random variables. See [R] asmprobit for the case where
the latent-variable errors are correlated or heteroskedastic and you have
alternative-specific variables.
Options
+-------+
----+ 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, that are
robust to some kinds of misspecification, that allow for intragroup
correlation, and that use bootstrap or jackknife methods; 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: noomitted, vsquish, noemptycells, baselevels,
allbaselevels; 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,
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.
Examples
Setup
. webuse sysdsn1
Fit multinomial probit model
. mprobit insure age male nonwhite i.site
Same as above, but use outcome 2 to normalize the location of the latent
variable
. mprobit insure age male nonwhite i.site, baseoutcome(2)
Saved results
mprobit saves 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
e(k_eq_model) number of equations in model Wald test
e(k_indvars) number of independent variables
e(k_dv) number of dependent variables
e(k_autoCns) number of base, empty, and omitted constraints
e(df_m) model degrees of freedom
e(ll) log simulated-likelihood
e(N_clust) number of clusters
e(chi2) chi-squared
e(p) significance
e(i_base) base outcome index
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(cmd2) 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(singularHmethod) m-marquardt or hybrid; method used when Hessian is
singular
e(crittype) optimization criterion
e(properties) b V
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(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
Also see
Manual: [R] mprobit
Help: [R] mprobit postestimation;
[R] asmprobit, [R] clogit, [R] mlogit, [R] nlogit, [R] ologit,
[R] oprobit, [SVY] svy estimation
