**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**.

__Title__

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

__Syntax__

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

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

SE/Robust
**vce(***vcetype***)** *vcetype* may be **oim**, __r__**obust**, or __cl__**uster** *clustvar*

Reporting
__l__**evel(***#***)** set confidence level; default is **level(95)**

Maximization
*maximize_options* control the maximization process; seldom used

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

__Menu__

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

__Description__

**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.

__Options__

+-------+
----+ 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*: __iter__**ate(***#***)**, [__no__]__lo__**g**, __tr__**ace**, __tol__**erance(***#***)**,
__ltol__**erance(***#***)**; 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.

__Example__

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**