Stata 15 help for gsem_predict

[SEM] predict after gsem -- Generalized linear predictions, etc.

Syntax for predict

Syntax for predicting observed endogenous outcomes and other statistics

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

Syntax for obtaining estimated continuous latent variables and their standard errors

predict [type] newvarsspec [if] [in], lstatistic [loptions]

Syntax for obtaining ML scores

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

newvarsspec is stub* or newvarlist.

The default is to predict observed endogenous variables with empirical Bayes means predictions of the continuous latent variables. If the model includes a categorical latent variable, the default is class-specific predictions of the observed endogenous variables.

statistic Description ------------------------------------------------------------------------- Main mu expected value of depvar; the default pr probability (synonym for mu when mu is a probability) eta expected value of linear prediction of depvar density density function at depvar distribution distribution function at depvar survival survivor function at depvar expression(exp) calculate prediction using exp classpr latent class probability classposteriorpr posterior latent class probability -------------------------------------------------------------------------

options Description ------------------------------------------------------------------------- Main conditional(ctype) compute statistic conditional on estimated continuous latent variables; default is conditional(ebmeans) marginal compute statistic marginally with respect to the latent variables pmarginal compute mu marginally with respect to the posterior latent class probabilities nooffset make calculation ignoring offset or exposure + outcome(depvar [#]) specify observed response variable (default all) * class(lclspec) specify latent class (default all)

Integration int_options integration options ------------------------------------------------------------------------- + outcome(depvar #) is allowed only if depvar has family multinomial, ordinal, or bernoulli. Predicting other generalized responses requires specifying only outcome(depvar). outcome(depvar #) may also be specified as outcome(#.depvar) or outcome(depvar ##). outcome(depvar #3) means the third outcome value. outcome(depvar #3) would mean the same as outcome(depvar 4) if outcomes were 1, 3, and 4. * class(lclspec) is allowed only for models with categorical latent variables. For models with one categorical latent variable, lclspec can be a class value, such as class(2) or its equivalent factor-variable notation class(2.C), assuming the categorical latent variable is C. For models with two or more categorical latent variables, lclspec may only be in factor-variable notation, such as class(2.C#1.D) for categorical latent variables C and D.

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

lstatistic Description ------------------------------------------------------------------------- Main latent empirical Bayes prediction of all latent variables latent(varlist) empirical Bayes prediction of specified latent variables -------------------------------------------------------------------------

loptions Description ------------------------------------------------------------------------- Main ebmeans empirical Bayes means of latent variables; the default ebmodes empirical Bayes modes of latent variables se(stub*|newvarlist) standard errors of empirical Bayes estimates

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

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Statistics > SEM (structural equation modeling) > Predictions

Description

predict is a standard postestimation command of Stata. This entry concerns use of predict after gsem. See [SEM] predict after sem if you fit your model with sem.

predict after gsem creates new variables containing observation-by-observation values of estimated observed response variables, linear predictions of observed response variables, latent class probabilities, or endogenous or exogenous continuous latent variables.

Options

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

mu, the default, calculates the expected value of the outcomes.

pr calculates predicted probabilities and is a synonym for mu. This option is available only for multinomial, ordinal, and Bernoulli outcomes.

eta calculates the fitted linear prediction.

density calculates the density function. This prediction is computed using the current values of the observed variables, including the dependent variable.

distribution calculates the distribution function. This prediction is computed using the current values of the observed variables, including the dependent variable. This option is not allowed for multinomial outcomes.

survival calculates the survivor function. This prediction is computed using the current values of the observed variables, including the dependent variable. This option is only allowed for exponential, gamma, loglogistic, lognormal, and Weibull outcomes.

expression(exp) specifies the prediction as an expression. exp is any valid Stata expression, but the expression must contain a call to one of the two special functions unique to this option:

1. mu(outcome): The mu() function specifies the calculation of the mean prediction for outcome. If mu() is specified without outcome, the mean prediction for the first outcome is implied.

pr(outcome): The pr() function is a synonym for mu(outcome) when outcome identifies a multinomial, ordinal, or Bernoulli outcome.

2. eta(outcome): The eta() function specifies the calculation of the linear prediction for outcome. If eta() is specified without outcome, the linear predictor for the first outcome is implied.

When you specify exp, both of these functions may be used repeatedly, in combination, and in combination with other Stata functions and expressions.

classpr calculates predicted probabilities for each latent class.

classposteriorpr calculates predicted posterior probabilities for each latent class. The posterior probabilities are a function of the latent class predictors and the fitted outcome densities.

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

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

Although this is not the default, marginal predictions are often very useful in applied analysis. They produce what are commonly called population-averaged estimates. They are also required by margins for models with continuous latent variables.

For models with continuous latent variables, the statistic is calculated by integrating the prediction function with respect to all the latent variables over their entire support.

For models with categorical latent variables, mu is the only supported statistic. The overall expected value of each outcome is predicted by combining the class-specific expected values using the latent class probabilities.

pmarginal specifies that the overall expected value of each outcome be predicted by combining the class-specific expected values using the posterior latent class probabilities. This option is allowed only with the default statistic, mu.

nooffset is relevant only if option offset() or exposure() was specified at estimation time. nooffset specifies that offset() or exposure() be ignored, which produces predictions as if all subjects had equal exposure.

outcome(depvar [#]) specifies that predictions for depvar be calculated. Predictions for all observed response variables are computed by default. If depvar is a multinomial or an ordinal outcome, then # optionally specifies which outcome level to predict.

class(lclspec) specifies that predictions for latent class lclspec be calculated. Predictions for all latent classes are computed by default. For models with one categorical latent variable, such as C, lclspec can be a class value, such as class(2) or its equivalent factor-variable notation, class(2.C). For models with two or more categorical latent variables, such as C and D, lclspec may only be in factor-variable notation, such as class(2.C) or class(2.C#1.D).

latent and latent(varlist) specify that the continuous latent variables be estimated using empirical Bayes predictions. By default or if the ebmeans option is specified, empirical Bayes means are computed. With the ebmodes option, empirical Bayes modes are computed.

latent requests empirical Bayes estimates for all latent variables.

latent(varlist) requests empirical Bayes estimates for the specified latent variables.

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

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

se(stub*|newvarlist) calculates standard errors of the empirical Bayes estimators and stores the result in newvarlist. This option requires the latent or latent() option.

scores calculates the scores for each coefficient in e(b). This option requires a new variable list of 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.

Remarks

Out-of-sample prediction is allowed for all predict options except scores.

predict has two ways of specifying the names of the variables to be created:

. predict stub*, ...

or

. predict firstname secondname ..., ...

The first creates variables named stub1, stub2, .... The second creates variables with names that you specify. We strongly recommend using the stub* syntax when creating multiple variables because you have no way of knowing the order in which to specify the individual variable names to correspond to the order in which predict will make the calculations. If you use stub*, the variables will be labeled and you can rename them.

The second syntax is useful when you create one variable and specify outcome(), expression(), class(), or latent().

See [SEM] intro 7, [SEM] example 28g, [SEM] example 29g, [SEM] example 50g, and [SEM] example 52g.

Examples

Setup . webuse gsem_cfa . gsem (MathAb -> (q1-q8)@b), logit var(MathAb@1)

Predicted probability of success for all observed response variables . predict pr*, pr

Empirical Bayes mean prediction of the latent variable . predict ability, latent(MathAb)


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