Stata 15 help for irt_hybrid_postestimation

[IRT] irt hybrid postestimation -- Postestimation tools for irt hybrid

Postestimation commands

The following postestimation commands are of special interest after irt:

Command Description ------------------------------------------------------------------------- estat report report estimated IRT parameters irtgraph icc plot item characteristic curve (ICC) irtgraph iif plot item information function (IIF) irtgraph tcc plot test characteristic curve (TCC) irtgraph tif plot test information function (TIF) -------------------------------------------------------------------------

The following standard postestimation commands are also available:

Command Description ------------------------------------------------------------------------- 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 lincom point estimates, standard errors, testing, and inference for linear combinations of coefficients * lrtest likelihood-ratio test nlcom point estimates, standard errors, testing, and inference for nonlinear combinations of coefficients predict predictions predictnl point estimates, standard errors, testing, and inference for generalized predictions test Wald tests of simple and composite linear hypotheses testnl Wald tests of nonlinear hypotheses ------------------------------------------------------------------------- * lrtest is not appropriate with svy estimation results.

Syntax for predict

Syntax for obtaining predictions of item probabilities and other statistics

predict [type] newvarsspec [if] [in] [, statistic item_options]

Syntax for obtaining estimated latent variables and their standard errors

predict [type] newvarsspec [if] [in], latent [latent_options]

Syntax for obtaining parameter-level scores

predict [type] newvarsspec [if] [in], scores

newvarsspec is stub* or newvarlist.

statistic Description ------------------------------------------------------------------------- Main pr probabilities; the default xb linear prediction -------------------------------------------------------------------------

item_options Description ------------------------------------------------------------------------- Main * outcome(item [#]) specify item variable; default is all variables conditional(ctype) compute statistic conditional on estimated latent variables; default is conditional(ebmeans) marginal compute statistic marginally with respect to the latent variables

Integration int_options integration options ------------------------------------------------------------------------- * outcome(item #) may also be specified as outcome(#.item) or outcome(item ##). outcome(item #3) means the third outcome value. outcome(item #3) would mean the same as outcome(item 4) if outcomes were 1, 3, and 4.

ctype Description ------------------------------------------------------------------------- ebmeans empirical Bayes means of latent variables; the default ebmodes empirical Bayes modes of latent variables fixedonly prediction for the fixed portion of the model only -------------------------------------------------------------------------

latent_options Description ------------------------------------------------------------------------- Main ebmeans use empirical Bayes means of latent trait; the default ebmodes use empirical Bayes modes of latent trait se(newvar) calculate standard errors

Integration int_options integration options -------------------------------------------------------------------------

int_options Description ------------------------------------------------------------------------- intpoints(#) use # quadrature points to compute marginal predictions and empirical Bayes means iterate(#) set maximum number of iterations in computing statistics involving empirical Bayes estimators tolerance(#) set convergence tolerance for computing statistics involving empirical Bayes estimators -------------------------------------------------------------------------

Menu for predict

Statistics > Postestimation

Description for predict

predict creates a new variable containing predictions such as probabilities, linear predictions, and parameter-level scores.

Options for predict

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

pr, the default, calculates the predicted probability.

xb specifies that the linear predictor be calculated.

outcome(item [#]) specifies that predictions for item be calculated. Use # to specify which outcome level to predict. Predictions for all observed response variables are computed by default.

conditional(ctype) and marginal specify how latent variables are handled in computing statistic.

conditional() specifies that statistic will be computed conditional on specified or estimated latent variables.

conditional(ebmeans), the default, specifies that empirical Bayes means be used as the estimates of the latent variables. These estimates are also known as posterior mean estimates of the latent variables.

conditional(ebmodes) specifies that empirical Bayes modes be used as the estimates of the latent variables. These estimates are also known as posterior mode estimates of the latent variables.

conditional(fixedonly) specifies that all latent variables be set to zero, equivalent to using only the fixed portion of the model.

marginal specifies that the predicted statistic be computed marginally with respect to the latent variables, which means that statistic is calculated by integrating the prediction function with respect to all the latent variables over their entire support.

Although this is not the default, marginal predictions are often very useful in applied analysis. They produce what are commonly called population-averaged estimates.

latent specifies that the latent trait is predicted using an empirical Bayes estimator; see options ebmeans and ebmodes.

ebmeans specifies that empirical Bayes means are used to predict the latent variables.

ebmodes specifies that empirical Bayes modes are used to predict the latent variables.

se(newvar) calculates standard errors of the empirical Bayes estimator and stores the result in newvar. This option requires the latent option.

scores calculates the scores for each coefficient in e(b). This option requires a new variable list of the length equal to the number of columns in e(b). Otherwise, use stub* to have predict generate enumerated variables with prefix stub.

+-------------+ ----+ Integration +------------------------------------------------------

intpoints(#) specifies the number of quadrature points used to compute marginal predictions and the empirical Bayes means; the default is the value from estimation.

iterate(#) specifies the maximum number of iterations when computing statistics involving empirical Bayes estimators; the default is the value from estimation.

tolerance(#) specifies convergence tolerance when computing statistics involving empirical Bayes estimators; the default is the value from estimation.

Examples

Setup . webuse science

Fit an NRM to items q1-q3 and a PCM to item q4 . irt hybrid (nrm q1-q3) (pcm q4)

Replay the table of estimated IRT parameters, sorting the output by parameter instead of by item and in ascending order of difficulty . estat report, byparm sort(b)

Use the NRM parameters to plot the test characteristic curves . irtgraph tcc, thetalines(-1.96 0 1.96)


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