[R] ologit postestimation -- Postestimation tools for ologit
Description
The following postestimation commands are available after ologit:
Command Description
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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
(1) forecast dynamic forecasts and simulations
lincom point estimates, standard errors, testing, and inference for linear
combinations of coefficients
linktest link test for model specification
(2) 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
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(1) forecast is not appropriate with mi or svy estimation results.
(2) lrtest is not appropriate with svy estimation results.
statistic Description
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Main
pr predicted probabilities; the default
xb linear prediction
stdp standard error of the linear prediction
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If you do not specify outcome(), pr (with one new variable specified) assumes outcome(#1).
You specify one or k new variables with pr, where k is the number of outcomes.
You specify one new variable with xb and stdp.
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 > Predictions, residuals, etc.
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 ologit 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 specify one new variable, for example, predict
linear, xb. The linear prediction is defined, ignoring the contribution of the estimated
cutpoints.
stdp calculates the standard error of the linear prediction. You specify one new variable,
for example, predict se, stdp.
outcome(outcome) specifies for which outcome the predicted probabilities are to be
calculated. outcome() should contain either one value of the dependent variable or one
of #1, #2, ..., with #1 meaning the first category of the dependent variable, #2 meaning
the second category, etc.
nooffset is relevant only if you specified offset(varname) for ologit. 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.
scores calculates equation-level score variables. The number of score variables created will
equal the number of outcomes in the model. If the number of outcomes in the model was k,
then
The first new variable will contain the derivative of the log likelihood with respect to
the regression equation.
The other new variables will contain the derivative of the log likelihood with respect to
the cutpoints.
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