Stata 15 help for me estat sd

[ME] estat sd -- Display variance components as standard deviations and correlations


estat sd [, variance verbose post coeflegend]

Menu for estat

Statistics > Postestimation


estat sd displays the random-effects and within-group error parameter estimates as standard deviations and correlations.


variance specifies that estat sd display the random-effects and within-group error parameter estimates as variances and covariances. If the post option is specified, the estimated variances and covariances and their respective standard errors are posted to e(). variance is allowed only after menl.

verbose specifies that the full estimation table be displayed. By default, only the random-effects and within-group error parameters are displayed. This option is implied when post is specified.

post causes estat sd to behave like a Stata estimation (e-class) command. estat sd posts the vector of calculated standard deviation and correlation parameters along with the corresponding variance-covariance matrix to e(), so that you can treat the estimated parameters just as you would results from any other estimation command. For example, you could use test to perform simultaneous tests of hypotheses on the parameters, or you could use lincom to create linear combinations.

The following option is not shown in the dialog box:

coeflegend specifies that the legend of the coefficients and how to specify them in an expression be displayed rather than displaying the statistics for the coefficients. This option is not supported by mixed.


Setup . webuse pig . mixed weight week || id: week, covariance(unstructured)

Display the estimated variance components as correlations and standard deviations . estat sd

Same as above, but display the full estimation table . estat sd, verbose

Stored results

estat sd stores the following in r():

Matrices r(b) coefficient vector r(V) variance-covariance matrix of the estimators

If post() is specified, estat sd also stores the following in e():

Matrices e(b) coefficient vector e(V) variance-covariance matrix of the estimators

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