What's this about?
As you fit models, Stata's new Postestimation Selector shows you
postestimation statistics, tests, and predictions that you could use
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
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
. 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.
Tell me more
For more information, see the manual entry on the Postestimation Selector.
Also read the overview from the Stata News.