**[R] oprobit postestimation** -- Postestimation tools for oprobit

__Postestimation commands__

The following postestimation commands are available after **oprobit**:

Command Description
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**contrast** contrasts and ANOVA-style joint tests of estimates
**estat ic** Akaike's and Schwarz's Bayesian information criteria
(AIC and BIC)
**estat summarize** summary statistics for the estimation sample
**estat vce** variance-covariance matrix of the estimators (VCE)
**estat** (svy) postestimation statistics for survey data
**estimates** cataloging estimation results
* **forecast** dynamic forecasts and simulations
* **hausman** Hausman's specification test
**lincom** point estimates, standard errors, testing, and
inference for linear combinations of coefficients
**linktest** link test for model specification
* **lrtest** likelihood-ratio test
**margins** marginal means, predictive margins, marginal effects,
and average marginal effects
**marginsplot** graph the results from margins (profile plots,
interaction plots, etc.)
**nlcom** point estimates, standard errors, testing, and
inference for nonlinear combinations of coefficients
**predict** predictions, residuals, influence statistics, and
other diagnostic measures
**predictnl** point estimates, standard errors, testing, and
inference for generalized predictions
**pwcompare** pairwise comparisons of estimates
**suest** seemingly unrelated estimation
**test** Wald tests of simple and composite linear hypotheses
**testnl** Wald tests of nonlinear hypotheses
-------------------------------------------------------------------------
* **forecast**, **hausman**, and **lrtest** are not appropriate with **svy** estimation
results. **forecast** is also not appropriate with **mi** estimation results.

__Syntax for predict__

**predict** [*type*] {*stub****** | *newvar* | *newvarlist*} [*if*] [*in*] [**,** *statistic*
__o__**utcome(***outcome***)** __nooff__**set**]

**predict** [*type*] {*stub****** | *newvarlist*} [*if*] [*in*] **,** __sc__**ores**

*statistic* Description
-------------------------------------------------------------------------
Main
__p__**r** predicted probabilities; the default
**xb** linear prediction
**stdp** standard error of the linear prediction
-------------------------------------------------------------------------
If you do not specify **outcome()**, **pr** (with one new variable specified)
assumes **outcome(#1)**.
You specify one or k new variables with **pr**, where *k* is the number of
outcomes.
You specify one new variable with **xb** and **stdp**.
These statistics are available both in and out of sample; type **predict**
*...* **if e(sample)** *...* if wanted only for the estimation sample.

__Menu for predict__

**Statistics > Postestimation**

__Description for predict__

**predict** creates a new variable containing predictions such as
probabilities, linear predictions, and standard errors.

__Options for predict__

+------+
----+ Main +-------------------------------------------------------------

**pr**, the default, calculates the predicted probabilities. If you do not
also specify the **outcome()** option, you specify k new variables, where
k is the number of categories of the dependent variable. Say that
you fit a model by typing **oprobit result x1 x2**, and **result** takes on
three values. Then you could type **predict p1 p2 p3** to obtain all
three predicted probabilities. If you specify the **outcome()** option,
you must specify one new variable. Say that **result** takes on the
values 1, 2, and 3. Typing **predict p1, outcome(1)** would produce the
same **p1**.

**xb** calculates the linear prediction. You specify one new variable, for
example, **predict linear, xb**. The linear prediction is defined,
ignoring the contribution of the estimated cutpoints.

**stdp** calculates the standard error of the linear prediction. You specify
one new variable, for example, **predict se, stdp**.

**outcome(***outcome***)** specifies for which outcome the predicted probabilities
are to be calculated. **outcome()** should contain either one value of
the dependent variable or one of **#1**, **#2**, *...*, with **#1** meaning the
first category of the dependent variable, **#2** meaning the second
category, etc.

**nooffset** is relevant only if you specified **offset(***varname***)** for **oprobit**.
It modifies the calculations made by **predict** so that they ignore the
offset variable; the linear prediction is treated as xb rather than
as xb + offset.

**scores** calculates equation-level score variables. The number of score
variables created will equal the number of outcomes in the model. If
the number of outcomes in the model was k, then

The first new variable will contain the derivative of the log
likelihood with respect to the regression equation.

The other new variables will contain the derivative of the log
likelihood with respect to the cutpoints.

__Syntax for margins__

**margins** [*marginlist*] [**,** *options*]

**margins** [*marginlist*] **,** __pr__**edict(***statistic *...**)** [__pr__**edict(***statistic *...**)**
...] [*options*]

*statistic* Description
-------------------------------------------------------------------------
default probabilities for each outcome
__p__**r** probability for a specified outcome
**xb** linear prediction
**stdp** not allowed with **margins**
-------------------------------------------------------------------------
**pr** defaults to the first outcome.

Statistics not allowed with **margins** are functions of stochastic
quantities other than **e(b)**.

For the full syntax, see **[R] margins**.

__Menu for margins__

**Statistics > Postestimation**

__Description for margins__

**margins** estimates margins of response for probabilities and linear
predictions.

__Examples__

Setup
**. webuse fullauto**
**. oprobit rep77 i.foreign length mpg**

Predicted probabilities of an excellent repair record
**. predict exc if e(sample), outcome(5)**

Histogram of predicted probabilities
**. histogram exc**

Linear prediction
**. predict pscore, xb**

Average marginal effects on the probability of the worst repair record
**. margins, dydx(*) predict(outcome(1))**