What’s this about?

As you fit models, Stata’s new 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.

 

. regress bpsystol age weight i.female

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
			Adj R-squared = 0.3031
Total		5634670.03             10,350                544.412563	Root MSE = 19.478

 

bpsystol		Coef.                       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
1.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.female


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
1.female		1.036659                  .0498306         0.75               0.454                    .9434528            1.139074
_cons		.002525                    .0004077        -37.05            0.000                   .0018401            .003465

 

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.female
(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
1.female		.9388421                    .055388                -1.07                 0.293                 .832409                      1.058884
_cons		.0021719                    .0004316             -30.86               0.000               .0014482                     .0032572

 

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.