Stata 15 help for dprobit

help dprobit dialog: dprobit also see: dprobit postestimation previously documented ------------------------------------------------------------------------------- dprobit continues to work but, as of Stata 11, is no longer an official part of Stata. This is the original help file, which we will no longer update, so some links may no longer work.

See margins for a recommended alternative to dprobit.


[R] dprobit -- Probit regression, reporting marginal effects


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

options Description ------------------------------------------------------------------------- Model offset(varname) include varname in model with coefficient constrained to 1 at(matname) point at which marginal effects are evaluated asis retain perfect predictor variables classic calculate mean effects for dummies like those for continuous variables

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

Reporting level(#) set confidence level; default is level(95)

Maximization maximize_options control the maximization process; seldom used

+ nocoef do not display the coefficient table; seldom used ------------------------------------------------------------------------- + nocoef does not appear in the dialog box. indepvars may contain factor variables; see fvvarlist. by, rolling, and statsby are allowed; see prefix. fweights and pweights are allowed; see weight. See dprobit postestimation for features available after estimation.


Statistics > Binary outcomes > Probit regression (reporting change in prob.)


dprobit fits maximum-likelihood probit models and is an alternative to probit. Rather than reporting the coefficients, dprobit reports the marginal effect, that is, the change in the probability for an infinitesimal change in each independent, continuous variable and, by default, reports the discrete change in the probability for dummy variables. probit may be typed without arguments after dprobit estimation to see the model in coefficient form.

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.


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

offset(varname); see [R] estimation options.

at(matname) specifies the point at which marginal effects are evaluated. The default is to evaluate at the mean of the independent variables. If there are k independent variables, matname may be 1 x k or 1 x (k + 1); that is, it may optionally include final element 1 reflecting the constant. at() may be specified when the model is fit or when results are redisplayed.

asis; see Options for probit above.

classic requests that the mean effects always be calculated using the formula f(xb)*b_i. If classic is not specified, f(xb)*b_i is used for continuous variables, but the mean effects for dummy variables are calculated as F(x_1*b) - F(x_0*b). Here x_1=x but with element i set to 1, and x_0=x but with element i set to 0, and x is the mean of the independent variables or the vector specified by at(). classic may be specified at estimation time or when results are redisplayed. Results calculated without classic may be redisplayed with classic and vice versa.

+-----------+ ----+ 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, and that allow for intragroup correlation; see [R] vce_option.

+-----------+ ----+ Reporting +--------------------------------------------------------

level(#); see [R] estimation options.

+--------------+ ----+ Maximization +-----------------------------------------------------

maximize_options: iterate(#), [no]log, trace, tolerance(#), ltolerance(#); see [R] maximize. These options are seldom used.

The following option is available with dprobit but is 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.


Setup . sysuse auto . generate goodplus = rep78 >= 4 if rep78 < .

Probit regression, reporting marginal effects . dprobit foreign mpg goodplus

Saved results

dprobit 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(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(pbar) fraction of successes observed in data e(xbar) average probit score e(offbar) average offset

Macros e(cmd) dprobit 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(at) predicted probability (at x) 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(dummy) string of blank-separated 0s and 1s; 0 means that the corresponding independent variable is not a dummy; 1 means that it is e(crittype) optimization criterion e(properties) b V e(estat_cmd) program used to implement estat e(predict) program used to implement predict

Matrices e(b) coefficient vector e(V) variance-covariance matrix of the estimators e(dfdx) marginal effects e(se_dfdx) standard errors of the marginal effects

Functions e(sample) marks estimation sample

Also see

Manual: previously documented

Help: [R] dprobit 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

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