Stata 15 help for xtmixed_postestimation

xtmixed has been renamed to mixed; see [ME] mixed postestimation. xtmixed continues to work but, as of Stata 13, is no longer an official part of Stata. This is the original help file, which we will no longer update, so some links may no longer work.

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

Title

[XT] xtmixed postestimation -- Postestimation tools for xtmixed

Description

The following postestimation commands are of special interest after xtmixed:

Command Description ------------------------------------------------------------------------- estat group summarize the composition of the nested groups estat recovariance display the estimated random-effects covariance matrix estat icc estimate intraclass correlations -------------------------------------------------------------------------

The following standard postestimation commands are also available:

Command Description ------------------------------------------------------------------------- contrast contrasts and ANOVA-style joint tests of estimates estat AIC, BIC, VCE, and estimation sample summary estimates cataloging estimation results 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 -------------------------------------------------------------------------

Special-interest postestimation commands

estat group reports number of groups and minimum, average, and maximum group sizes for each level of the model. Model levels are identified by the corresponding group variable in the data. Because groups are treated as nested, the information in this summary may differ from what you would get had you tabulated each group variable yourself.

estat recovariance displays the estimated variance-covariance matrix of the random effects for each level. Random effects can be either random intercepts, in which case the corresponding rows and columns of the matrix are labeled as _cons, or random coefficients, in which case the label is the name of the associated variable in the data.

estat icc displays the intraclass correlation for pairs of responses at each nested level of the model. Intraclass correlations are available for random-intercept models or for random-coefficient models conditional on random-effects covariates being equal to zero. They are not available for crossed-effects models or with residual error structures other than independent structures.

Syntax for predict

Syntax for obtaining best linear unbiased predictions (BLUPs) of random effects, or the BLUPs' standard errors

predict [type] {stub*|newvarlist} [if] [in] , {reffects | reses} [level(levelvar)]

Syntax for obtaining scores after ML estimation

predict [type] {stub*|newvarlist} [if] [in] , scores

Syntax for obtaining other predictions

predict [type] newvar [if] [in] [, statistic level(levelvar)]

statistic Description ------------------------------------------------------------------------- Main xb xb, linear predictor for the fixed portion of the model stdp standard error of the fixed-portion linear prediction xb fitted fitted values, linear predictor of the fixed portion plus contributions based on predicted random effects residuals residuals, response minus fitted values * rstandard standardized residuals ------------------------------------------------------------------------- Unstarred statistics are available both in and out of sample; type predict ... if e(sample) ... if wanted only for the estimation sample. Starred statistics are calculated only for the estimation sample, even when if e(sample) is not specified.

Menu for predict

Statistics > Postestimation

Options for predict

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

xb calculates the linear prediction for the fixed portion of the model.

stdp calculates the standard error of the fixed-portion linear prediction.

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

reffects calculates best linear unbiased predictions (BLUPs) of the random effects. By default, BLUPs for all random effects in the model are calculated. However, if the level(levelvar) option is specified, then BLUPs for only level levelvar in the model are calculated. For example, if classes are nested within schools, then

. predict b*, reffects level(school)

would be used to obtain BLUPs 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...stubq for you.

reses calculates the standard errors of the best linear unbiased predictions (BLUPs) of the random effects. By default, standard errors for all BLUPs in the model are calculated. However, if the level(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...stubq for you.

Option reses is not available after estimation with robust/cluster variances.

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 xtmixed. Still, examining the variable labels of the generated variables (using the describe command, for instance) can be useful in deciphering which variables correspond to which terms in the model.

scores calculates the parameter-level scores, one for each parameter in the model including regression coefficients and variance components. The score for a parameter is the first derivative of the log likelihood (or log pseudolikelihood) with respect to that parameter. One score per highest-level group is calculated, and it is placed on the last record within that group. Scores are calculated in the estimation metric as stored in e(b).

scores is not available after restricted maximum-likelihood (REML) estimation.

fitted calculates fitted values, which are equal to the fixed-portion linear predictor plus contributions based on predicted random effects, or in mixed-model notation, xb + Zu. By default, the fitted values take into account random effects from all levels in the model, however, if the level(levelvar) option is specified, then the fitted values are fit beginning at the top-most level down to and including level levelvar. For example, if classes are nested within schools, then

. predict yhat1, fitted level(school)

would produce school-level predictions. That is, the predictions would incorporate school-specific random effects, but not those for each class nested within each school.

residuals calculates residuals, equal to the responses minus fitted values. By default, the fitted values take into account random effects from all levels in the model, however, if the level(levelvar) option is specified, then the fitted values are fit beginning at the top-most level down to and including level levelvar.

rstandard calculates standardized residuals, equal to the residuals multiplied by the inverse square root of the estimated error covariance matrix.

Syntax for estat group

estat group

Menu for estat

Statistics > Postestimation

Syntax for estat recovariance

estat recovariance [, level(levelvar) correlation matlist_options]

Menu for estat

Statistics > Postestimation

Options for estat recovariance

level(levelvar) specifies the level in the model for which the random-effects covariance matrix is to be displayed. By default, the covariance matrices for all levels in the model are displayed. levelvar is the name of the model level and is either the name of variable describing the grouping at that level or _all, a special designation for a group comprising all the estimation data.

correlation displays the covariance matrix as a correlation matrix.

matlist_options control how the matrix (or matrices) are displayed. See [P] matlist for details.

Syntax for estat icc

estat icc [, level(#)]

Menu for estat

Statistics > Postestimation

Option for estat icc

level(#) specifies the confidence level, as a percentage, for confidence intervals. The default is level(95) or as set by set level.

Examples

--------------------------------------------------------------------------- Setup . webuse pig . xtmixed weight week || id: week, covariance(unstructured) variance

Random-effects covariance matrix for level id . estat recovariance

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

BLUPS of random effects . predict u1 u0, reffects

Standard errors of BLUPs . predict s1 s0, reses

--------------------------------------------------------------------------- Setup . webuse productivity, clear . xtmixed gsp private emp hwy water other unemp || region: || state:

Summarize composition of nested groups . estat group

Fitted values at region level . predict gsp_region, fitted level(region)

Log likelihood scores . predict double sc*, scores

Compute residual intraclass correlations . estat icc

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

Saved results

estat recovariance saves the last-displayed random-effects covariance matrix in r(cov) or in r(corr) if it is displayed as a correlation matrix.

estat icc saves the following in r():

Scalars r(icc#) level-# intraclass correlation r(se#) standard errors of level-# intraclass correlation r(level) confidence level of confidence intervals

Macros r(label#) label for level #

Matrices r(ci#) vector of confidence intervals (lower and upper) for level-# intraclass correlation

For a G-level nested model, # can be any integer between 2 and G.


© Copyright 1996–2018 StataCorp LLC   |   Terms of use   |   Privacy   |   Contact us   |   What's new   |   Site index