Stata 15 help for mlogit_postestimation

[R] mlogit postestimation -- Postestimation tools for mlogit

Postestimation commands

The following postestimation commands are available after mlogit:

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 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 * 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 outcome(outcome)]

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

statistic Description ------------------------------------------------------------------------- Main pr predicted probabilities; the default xb linear prediction stdp standard error of the linear prediction stddp standard error of the difference in two linear predictions ------------------------------------------------------------------------- If you do not specify outcome(), pr (with one new variable specified), xb, and stdp assume outcome(#1). You must specify outcome() with the stddp option. You specify one or k new variables with pr, where k is the number of outcomes. You specify one new variable with xb, stdp, and stddp. 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 mlogit 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 must also specify the outcome(outcome) option.

stdp calculates the standard error of the linear prediction. You must also specify the outcome(outcome) option.

stddp calculates the standard error of the difference in two linear predictions. You must specify the outcome(outcome) option, and here you specify the two particular outcomes of interest inside the parentheses, for example, predict sed, stdp outcome(1,3).

outcome(outcome) specifies the outcome for which the statistic is to be calculated. equation() is a synonym for outcome(): it does not matter which you use. outcome() or equation() can be specified using

#1, #2, ..., where #1 means the first category of the dependent variable, #2 means the second category, etc.;

the values of the dependent variable; or

the value labels of the dependent variable if they exist.

scores calculates equation-level score variables. The number of score variables created will be one less than the number of outcomes in the model. If the number of outcomes in the model were k, then

the first new variable will contain the first derivative of the log likelihood with respect to the first equation;

the second new variable will contain the first derivative of the log likelihood with respect to the second equation;

...

the (k-1)th new variable will contain the first derivative of the log likelihood with respect to the (k-1)st equation.

Syntax for margins

margins [marginlist] [, options]

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

statistic Description ------------------------------------------------------------------------- default probabilities for each outcome pr probability for a specified outcome xb linear prediction for a specified outcome stdp not allowed with margins stddp not allowed with margins ------------------------------------------------------------------------- pr and xb default 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 sysdsn1 . mlogit insure age male nonwhite i.site

Test joint significance of 2.site and 3.site in all equations . test 2.site 3.site

Test joint significance of coefficients in Prepaid equation . test [Prepaid]

Test joint significance of 2.site and 3.site in Uninsure equation . test [Uninsure]: 2.site 3.site

Test if coefficients in Prepaid and Uninsure equations are equal . test [Prepaid=Uninsure]

Predict probabilities of outcome 1 for estimation sample . predict p1 if e(sample), outcome(1)

Display summary statistics of p1 . summarize p1

Compute linear prediction for Indemnity equation . predict idx1, outcome(Indemnity) xb

--------------------------------------------------------------------------- Setup . sysuse auto, clear . mlogit rep78 mpg displ

Compute the predicted probability at the regressors' means for each outcome . margins, atmeans

Compute the average marginal effect of each regressor on the probability of each of the outcomes 1-3 . margins, dydx(*) predict(outcome(1)) predict(outcome(2)) predict(outcome(3)) ---------------------------------------------------------------------------


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