## Stata 15 help for logit_postestimation

```
[R] logit postestimation -- Postestimation tools for logit

Postestimation commands

The following postestimation commands are of special interest after
logit:

Command               Description
-------------------------------------------------------------------------
estat classification  report various summary statistics, including the
classification table
estat gof             Pearson or Hosmer-Lemeshow goodness-of-fit test
lroc                  compute area under ROC curve and graph the curve
lsens                 graph sensitivity and specificity versus
probability cutoff
-------------------------------------------------------------------------
These commands are not appropriate after the svy prefix.

The following standard postestimation commands are also available:

Command               Description
-------------------------------------------------------------------------
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
* forecast            dynamic forecasts and simulations
* hausman             Hausman's specification test
lincom              point estimates, standard errors, testing, and
inference for linear combinations of coefficients
* 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] newvar [if] [in] [, statistic nooffset rules asif]

statistic             Description
-------------------------------------------------------------------------
Main
pr                  probability of a positive outcome; the default
xb                  linear prediction
stdp                standard error of the prediction
* dbeta               Pregibon (1981) Delta-Beta influence statistic
* deviance            deviance residual
* dx2                 Hosmer, Lemeshow, and Sturdivant (2013) Delta
chi-squared influence statistic
* ddeviance           Hosmer, Lemeshow, and Sturdivant (2013) Delta-D
influence statistic
* hat                 Pregibon (1981) leverage
* number              sequential number of the covariate pattern
* residuals           Pearson residuals; adjusted for number sharing
covariate pattern
* rstandard           standardized Pearson residuals; adjusted for number
sharing covariate pattern
score               first derivative of the log likelihood with respect
to xb
-------------------------------------------------------------------------
Unstarred statistics are available both in and out of sample; type
predict ... if e(sample) ... if wanted only for the estimation sample.
Starred statistics are calculated only for the estimation sample, even
when if e(sample) is not specified.
pr, xb, stdp, and score are the only options allowed with svy estimation
results.

Statistics > Postestimation

Description for predict

predict creates a new variable containing predictions such as
probabilities, linear predictions, standard errors, influence statistics,
deviance residuals, leverages, sequential numbers, Pearson residuals, and
equation-level scores.

Options for predict

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

pr, the default, calculates the probability of a positive outcome.

xb calculates the linear prediction.

stdp calculates the standard error of the linear prediction.

dbeta calculates the Pregibon (1981) Delta-Beta influence statistic, a
standardized measure of the difference in the coefficient vector that
is due to deletion of the observation along with all others that
share the same covariate pattern.  In Hosmer, Lemeshow, and
Sturdivant (2013, 154-155) jargon, this statistic is M-asymptotic;
that is, it is adjusted for the number of observations that share the
same covariate pattern.

deviance calculates the deviance residual.

dx2 calculates the Hosmer, Lemeshow, and Sturdivant (2013, 191) Delta
chi-squared influence statistic, reflecting the decrease in the
Pearson chi-squared that is due to deletion of the observation and
all others that share the same covariate pattern.

ddeviance calculates the Hosmer, Lemeshow, and Sturdivant (2013, 191)
Delta-D influence statistic, which is the change in the deviance
residual that is due to deletion of the observation and all others
that share the same covariate pattern.

hat calculates the Pregibon (1981) leverage or the diagonal elements of
the hat matrix adjusted for the number of observations that share the
same covariate pattern.

number numbers the covariate patterns -- observations with the same
covariate pattern have the same number.  Observations not used in
estimation have number set to missing.  The first covariate pattern
is numbered 1, the second 2, and so on.

residuals calculates the Pearson residual as given by Hosmer, Lemeshow,
and Sturdivant (2013, 155) and adjusted for the number of
observations that share the same covariate pattern.

rstandard calculates the standardized Pearson residual as given by
Hosmer, Lemeshow, and Sturdivant (2013, 191) and adjusted for the
number of observations that share the same covariate pattern.

score calculates the equation-level score, the first derivative of the
log likelihood with respect to the linear prediction.

+---------+
----+ Options +----------------------------------------------------------

nooffset is relevant only if you specified offset(varname) for logit.  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.

rules requests that Stata use any rules that were used to identify the
model when making the prediction.  By default, Stata calculates
missing for excluded observations.

asif requests that Stata ignore the rules and the exclusion criteria and
calculate predictions for all observations possible by using the
estimated parameter from the model.

Syntax for margins

margins [marginlist] [, options]

margins [marginlist] , predict(statistic ...) [predict(statistic ...)
...] [options]

statistic          Description
-------------------------------------------------------------------------
pr                 probability of a positive outcome; the default
xb                 linear prediction
stdp               not allowed with margins
dbeta              not allowed with margins
deviance           not allowed with margins
dx2                not allowed with margins
ddeviance          not allowed with margins
hat                not allowed with margins
number             not allowed with margins
residuals          not allowed with margins
rstandard          not allowed with margins
score              not allowed with margins
-------------------------------------------------------------------------

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

For the full syntax, see [R] margins.

Statistics > Postestimation

Description for margins

margins estimates margins of response for probabilities and linear
predictions.

Examples

---------------------------------------------------------------------------
Setup
. webuse lbw

Fit logistic regression to predict low birth weight
. logit low age lwt i.race smoke ptl ht ui

Calculate fitted probabilities
. predict p

Report classification table and summary statistics
. estat classification

Perform goodness-of-fit test
. estat gof

---------------------------------------------------------------------------
Setup
. webuse hospital, clear
. logistic satisfied hospital##illness

ANOVA-style table of tests for main effects and interaction effects
. contrast hospital##illness

Test differences between illnesses at each hospital
. margins illness, over(hospital) contrast

---------------------------------------------------------------------------

References

Hosmer, D. W., Jr., S. A. Lemeshow, and R. X. Sturdivant. 2013.  Applied
Logistic Regression. 3rd ed.  Hoboken, NJ: Wiley.

Pregibon, D. 1981. Logistic regression diagnostics.  Annals of Statistics
9: 705-724.

```