Stata 15 help for meqrlogit_postestimation

[ME] meqrlogit postestimation -- Postestimation tools for meqrlogit

Postestimation commands

The following postestimation commands are of special interest after meqrlogit:

Command Description ------------------------------------------------------------------------- estat group summarize the composition of the nested groups estat icc estimate intraclass correlations estat recovariance display the estimated random-effects covariance matrices estat sd display variance components as standard deviations and correlations -------------------------------------------------------------------------

The following standard postestimation commands are also available:

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) estimates cataloging estimation results 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 test Wald tests of simple and composite linear hypotheses testnl Wald tests of nonlinear hypotheses -------------------------------------------------------------------------

Syntax for predict

Syntax for obtaining estimated random effects and their standard errors

predict [type] newvarsspec [if] [in] , reffects [reses(newvarsspec) relevel(levelvar)]

Syntax for obtaining other predictions

predict [type] newvar [if] [in] [, statistic nooffset fixedonly]

newvarsspec is stub* or newvarlist.

statistic Description ------------------------------------------------------------------------- Main mu mean response; the default xb linear predictor for the fixed portion of the model only stdp standard error of the fixed-portion linear prediction pearson Pearson residuals deviance deviance residuals anscombe Anscombe residuals ------------------------------------------------------------------------- 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 mean responses; linear predictions; standard errors; and Pearson, deviance, and Anscombe residuals.

Options for predict

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

reffects calculates posterior modal estimates of the random effects. By default, estimates for all random effects in the model are calculated. However, if the relevel(levelvar) option is specified, then estimates for only level levelvar in the model are calculated. For example, if classes are nested within schools, then typing

. predict b*, reffects relevel(school)

would yield random-effects estimates at the school level. You must specify q new variables, where q is the number of random-effects terms in the model (or level). However, it is much easier to just specify stub* and let Stata name the variables stub1, stub2, ..., stubq for you.

reses(newvarsspec) calculates standard errors for the random-effects estimates. By default, standard errors for all random effects in the model are calculated. However, if the relevel(levelvar) option is specified, then standard errors for only level levelvar in the model are calculated; see the reffects option.

You must specify q new variables, where q is the number of random-effects terms in the model (or level). However, it is much easier to just specify stub* and let Stata name the variables stub1, stub2, ..., stubq for you. The new variables will have the same storage type as the corresponding random-effects variables.

The reffects and reses() options often generate multiple new variables at once. When this occurs, the random effects (or standard errors) contained in the generated variables correspond to the order in which the variance components are listed in the output of meqrlogit. Still, examining the variable labels of the generated variables (with the describe command, for instance) can be useful in deciphering which variables correspond to which terms in the model.

relevel(levelvar) specifies the level in the model at which predictions for random effects and their standard errors are to be obtained. levelvar is the name of the model level and is either the name of the variable describing the grouping at that level or is _all, a special designation for a group comprising all the estimation data.

mu, the default, calculates the predicted mean. 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 (see below) if you want predictions that include only the fixed portion of the model, that is, if you want random effects set to 0.

xb calculates the linear prediction based on the estimated fixed effects (coefficients) in the model. This is equivalent to fixing all random effects in the model to their theoretical (prior) mean value of 0.

stdp calculates the standard error of the fixed-effects linear predictor.

pearson calculates Pearson residuals. Pearson residuals large in absolute value may indicate a lack of fit. By default, residuals include both the fixed portion and the random portion of the model. The fixedonly option modifies the calculation to include the fixed portion only.

deviance calculates deviance residuals. Deviance residuals are recommended by McCullagh and Nelder (1989) as having the best properties for examining the goodness of fit of a GLM. They are approximately normally distributed if the model is correctly specified. They may be plotted against the fitted values or against a covariate to inspect the model's fit. By default, residuals include both the fixed portion and the random portion of the model. The fixedonly option modifies the calculation to include the fixed portion only.

anscombe calculates Anscombe residuals, which are designed to closely follow a normal distribution. By default, residuals include both the fixed portion and the random portion of the model. The fixedonly option modifies the calculation to include the fixed portion only.

nooffset is relevant only if you specified offset(varname) with meqrlogit. It modifies the calculations made by predict so that they ignore the offset variable; the linear prediction is treated as xb rather than xb + offset.

fixedonly modifies predictions to include only the fixed portion of the model, equivalent to setting all random effects equal to 0; see the mu option.

Syntax for margins

margins [marginlist] [, options]

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

statistic Description ------------------------------------------------------------------------- xb linear predictor for the fixed portion of the model only; the default reffects not allowed with margins reses not allowed with margins mu not allowed with margins stdp not allowed with margins pearson not allowed with margins deviance not allowed with margins anscombe not allowed with margins -------------------------------------------------------------------------

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 linear predictions.

Examples

--------------------------------------------------------------------------- Setup . webuse bangladesh . meqrlogit c_use urban age child* || district: urban, covariance(unstructured)

Random-effects covariance matrix for level district . estat recovariance

Random-effects correlation matrix for level district . estat recovariance, correlation

Predictions of random effects . predict re_urban re_cons, reffects

Compute conditional intraclass correlation . estat icc

--------------------------------------------------------------------------- Setup . webuse towerlondon, clear . meqrlogit dtlm difficulty i.group || family: || subject:

Summarize composition of nested groups . estat group

Predicted probabilities, incorporating random effects . predict p

Predicted probabilities, ignoring subject and family effects . predict p_fixed, fixedonly

Compute residual intraclass correlations . estat icc

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

Reference

McCullagh, P., and J. A. Nelder. 1989. Generalized Linear Models. 2nd ed. London: Chapman & Hall/CRC.


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