meglm postestimation - Stata

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2 meglm postestimation — Postestimation tools for meglm Syntax for predict Syntax for obtaining predictions of random effects and their standard errors predict [ type ] newvarsspec [ if ] [ in ] , { remeans | remodes } [ reses(newvarsspec) ] Syntax for obtaining other predictions predict [ type ] newvarsspec [ if ] [ in ] [ , statistic options ] newvarsspec is stub* or newvarlist. statistic Main mu pr fitted xb stdp residuals pearson deviance anscombe Description mean response; the default synonym for mu for ordinal and binary response models fitted linear predictor linear predictor for the fixed portion of the model only standard error of the fixed-portion linear prediction raw residuals; available only with the Gaussian family Pearson residuals deviance residuals Anscombe residuals These statistics are available both in and out of sample; type predict . . . if e(sample) . . . if wanted only for the estimation sample.
meglm postestimation — Postestimation tools for meglm 3 options Main means modes nooffset fixedonly outcome(outcome) Integration intpoints(#) iterate(#) tolerance(#) Description compute statistic using empirical Bayes means; the default compute statistic using empirical Bayes modes ignore the offset or exposure variable in calculating predictions; relevant only if you specified offset() or exposure() when you fit the model prediction for the fixed portion of the model only outcome category for predicted probabilities for ordinal models use # quadrature points to compute empirical Bayes means set maximum number of iterations in computing statistics involving empirical Bayes estimators set convergence tolerance for computing statistics involving empirical Bayes estimators For ordinal outcomes, you specify one or k new variables in newvarlist with mu and pr, where k is the number of outcomes. If you do not specify outcome(), those options assume outcome(#1). Menu for predict Statistics > Postestimation > Predictions, residuals, etc. Options for predict ✄ ✄ Main remeans calculates posterior means of the random effects, also known as empirical Bayes means. You must specify q new variables, where q is the number of random-effects terms in the model. However, it is much easier to just specify stub* and let Stata name the variables stub1, stub2, . . . , stubq for you. remodes calculates posterior modes of the random effects, also known as empirical Bayes modes. You must specify q new variables, where q is the number of random-effects terms in the model. However, it is much easier to just specify stub* and let Stata name the variables stub1, stub2, . . . , stubq for you. reses(stub* | newvarlist) calculates standard errors of the empirical Bayes estimators and stores the result in newvarlist. This option requires the remeans or the remodes option. You must specify q new variables, where q is the number of random-effects terms in the model. However, it is much easier to just specify stub* and let Stata name the variables stub1, stub2, . . . , stubq for you. The remeans, remodes, and reses() options often generate multiple new variables at once. When this occurs, the random effects (and standard errors) contained in the generated variables correspond to the order in which the variance components are listed in the output of meglm. Still, examining the variable labels of the generated variables (by using the describe command, for instance) can be useful in deciphering which variables correspond to which terms in the model. mu, the default, calculates the predicted mean, that is, the inverse link function applied to the linear prediction. By default, this is based on a linear predictor that includes both the fixed effects and the random effects, and the predicted mean is conditional on the values of the random effects. Use the fixedonly option if you want predictions that include only the fixed portion of the model, that is, if you want random effects set to 0.
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meglm postestimation - Stata

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