help probit dialogs: probit svy: probit
also see: probit postestimation
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Title
[R] probit -- Probit regression
Syntax
probit depvar [indepvars] [if] [in] [weight] [, options]
options description
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Model
noconstant suppress constant term
offset(varname) include varname in model with coefficient
constrained to 1
asis retain perfect predictor variables
constraints(constraints) apply specified linear constraints
collinear keep collinear variables
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 spacing and display of omitted
variables and base and empty cells
Maximization
maximize_options control the maximization process; seldom
used
+ nocoef do not display the coefficient table;
seldom used
+ coeflegend display coefficients' legend instead of
coefficient table
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+ nocoef and coeflegend do not appear in the dialog box.
indepvars may contain factor variables; see fvvarlist.
depvar and indepvars may contain time-series operators; see tsvarlist.
bootstrap, by, fracpoly, jackknife, mfp, mi estimate, nestreg, rolling,
statsby, stepwise, 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.
aweights are not allowed with the jackknife prefix.
vce(), nocoef, and weights are not allowed with the svy prefix.
fweights, iweights, and pweights are allowed; see weight.
See [R] probit postestimation for features available after estimation.
Menu
Statistics > Binary outcomes > Probit regression
Description
probit fits a maximum-likelihood probit model.
If estimating on grouped data, see bprobit.
Several auxiliary command may be run after probit, logit, or logistic;
see [R] logistic postestimation for a description of these commands.
See logistic estimation commands for a list of related estimation
commands.
Options
+-------+
----+ Model +------------------------------------------------------------
noconstant, offset(varname), constraints(constraints), collinear; see [R]
estimation options.
asis specifies that all specified variables and observations be retained
in the maximization process. This option is typically not specified
and may introduce numerical instability. Normally probit drops
variables that perfectly predict success or failure in the dependent
variable along with their associated observations. In those cases,
the effective coefficient on the dropped variables is infinity
(negative infinity) for variables that completely determine a success
(failure). Dropping the variable and perfectly predicted
observations has no effect on the likelihood or estimates of the
remaining coefficients and increases the numerical stability of the
optimization process. Specifying this option forces retention of
perfect predictor variables and their associated observations.
+-----------+
----+ 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.
+-----------+
----+ Reporting +--------------------------------------------------------
level(#); see [R] estimation options.
nocnsreport; see [R] estimation options.
display_options: noomitted, vsquish, noemptycells, baselevels,
allbaselevels; see [R] estimation options.
+--------------+
----+ 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.
The following options are available with probit 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.
Examples
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Setup
. sysuse auto
Probit regression
. probit foreign weight mpg
Same as above, but with robust standard errors
. probit foreign weight mpg, vce(robust)
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Setup
. webuse union
Probit regression
. probit union age grade not_smsa south##c.year
Same as above, but adjust standard errors for clusters in id
. probit union age grade not_smsa south##c.year, vce(cluster id)
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Setup
. webuse nhanes2d, clear
. svyset
Probit regression using survey data
. svy: probit highbp height weight age female
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Saved results
probit saves the following in e():
Scalars
e(N) number of observations
e(N_cds) number of completely determined successes
e(N_cdf) number of completely determined failures
e(k) number of parameters
e(k_eq) number of equations in e(b)
e(k_eq_model) number of equations in model Wald test
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(r2_p) pseudo-R-squared
e(ll) log likelihood
e(ll_0) log likelihood, contant-only model
e(N_clust) number of clusters
e(chi2) chi-squared
e(p) significance
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) probit
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(clustvar) name of cluster variable
e(offset) offset
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(singularHmethod) m-marquardt or hybrid; method used when Hessian is
singular
e(crittype) optimization criterion
e(properties) b V
e(estat_cmd) program used to implement estat
e(predict) program used to implement predict
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(mns) vector of means of the independent variables
e(rules) information about perfect predictors
e(V) variance-covariance matrix of the estimators
e(V_modelbased) model-based variance
Functions
e(sample) marks estimation sample
Also see
Manual: [R] probit
Help: [R] probit postestimation;
[R] asmprobit, [R] biprobit, [R] brier, [R] glm, [R] hetprob,
[R] ivprobit, [R] logistic, [R] logit, [R] mprobit, [R] roc, [R]
scobit, [SVY] svy estimation, [XT] xtprobit
