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#### Highlights

• Easily find and use any postestimation analysis tool

• List of all postestimation features available for your model

• Automatically updates as new models are estimated

• Available after every Stata estimation command

As you fit models, Stata's Postestimation Selector shows you postestimation statistics, tests, and predictions that you could use right now.

### Let's see it work

Suppose we have just fit a linear regression of systolic blood pressure on age, weight, and an indicator for females.

. webuse nhanes2, clear

. regress bpsystol age weight i.sex

Source         SS           df       MS     Number of obs   =    10,351
F(3, 10347)     =   1501.75
Model    1709209.9         3  569736.633   Prob > F        =    0.0000
Residual   3925460.13    10,347  379.381476   R-squared       =    0.3033
Total   5634670.03    10,350  544.412563   Root MSE        =    19.478

bpsystol   Coefficient  Std. err.      t    P>|t|     [95% conf. interval]

age    .6374325   .0111334    57.25   0.000     .6156088    .6592562
weight    .4170339    .013474    30.95   0.000     .3906221    .4434456

sex
Female     .8244702   .4140342     1.99   0.046     .0128832    1.636057
_cons    70.13615   1.187299    59.07   0.000     67.80881    72.46348



What's next? How can we check to see whether any assumptions of the model have been violated? Can we compare the model we just fit to the more complex model we may have fit previously? Can we save our estimation results so that we can use them again later? How do we know which of Stata's hundreds, if not thousands, of postestimation features are available after the model we just fit?

The Postestimation Selector provides a list of all postestimation tools available after fitting our model and provides two-click access to the corresponding dialog boxes.

We have selected Residual-versus-predictor plot. We click on Launch, the dialog box opens, and we create our graph. The Postestimation Selector will remain open so that we can perform the rest of our analysis. For instance, we can select Tests for heteroskedasticity and open the dialog box to perform a Breusch–Pagan test.

We recommend that you leave the Postestimation Selector open at all times.

Next, we decide to fit a logistic regression model for highbp, a variable that is one when the subject's blood pressure is clinically high and is zero otherwise.

. logistic highbp age weight i.sex

Logistic regression                                    Number of obs =  10,351
LR chi2(3)    = 2326.44
Prob > chi2   =  0.0000
Log likelihood = -5887.5446                            Pseudo R2     =  0.1650

highbp   Odds ratio   Std. err.      z    P>|z|     [95% conf. interval]

age    1.052054   .0014852    35.95   0.000     1.049147    1.054969
weight    1.044683    .001759    25.96   0.000     1.041242    1.048137

sex
Female     1.036659   .0498306     0.75   0.454     .9434528    1.139074
_cons     .002525   .0004077   -37.05   0.000     .0018401     .003465

Note: _cons estimates baseline odds.

As soon as our new model is estimated, the Postestimation Selector updates and shows us the postestimation tools that are now available.

While we could have found a list of available postestimation commands from the help file for logistic postestimation, the Postestimation Selector is more specialized. It lists only postestimation features available for the exact model that was fit, adjusting for any options or prefixes that were specified. Notice what happens when we take into account the complex survey nature of this dataset by specifying the svy prefix with our command.

. svy: logistic highbp age weight i.sex
(running logistic on estimation sample)

Survey: Logistic regression

Number of strata = 31                            Number of obs   =      10,351
Number of PSUs   = 62                            Population size = 117,157,513
Design df       =          31
F(3, 29)        =      449.71
Prob > F        =      0.0000

Linearized
highbp  Odds ratio   std. err.      t    P>|t|     [95% conf. interval]

age    1.054031   .0017933    30.93   0.000      1.05038    1.057695
weight    1.046507   .0021333    22.30   0.000     1.042165    1.050867

sex
Female     .9388421    .055388    -1.07   0.293      .832409    1.058884
_cons    .0021719   .0004316   -30.86   0.000     .0014482    .0032572

Note: _cons estimates baseline odds.

We find that some of the diagnostics and goodness-of-fit statistics that were available previously are no longer listed and that there is a new list of postestimation features that are available only when fitting models to complex survey data.

We can use the Postestimation Selector to guide us to the postestimation tools that are available after any model that we fit, and with any combinations of options or prefixes.