mixed - Stata

mixed — Multilevel mixed-effects linear regression 7
organize them by model level. Residual-variance parameter estimates are also displayed in their
original estimation metric.
noheader suppresses the output header, either at estimation or upon replay.
nogroup suppresses the display of group summary information (number of groups, average group
size, minimum, and maximum) from the output header.
nostderr prevents mixed from calculating standard errors for the estimated random-effects parameters,
although standard errors are still provided for the fixed-effects parameters. Specifying this option
will speed up computation times. nostderr is available only when residuals are modeled as
independent with constant variance.
nolrtest prevents mixed from fitting a reference linear regression model and using this model to
calculate a likelihood-ratio test comparing the mixed model to ordinary regression. This option
may also be specified on replay to suppress this test from the output.
display options: noomitted, vsquish, noemptycells, baselevels, allbaselevels, nofvlabel,
fvwrap(#), fvwrapon(style), cformat(% fmt), pformat(% fmt), sformat(% fmt), and
nolstretch; see [R] estimation options.
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EM options
These options control the expectation-maximization (EM) iterations that take place before estimation
switches to a gradient-based method. When residuals are modeled as independent with constant
variance, EM will either converge to the solution or bring parameter estimates close to the solution.
For other residual structures or for weighted estimation, EM is used to obtain starting values.
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emiterate(#) specifies the number of EM iterations to perform. The default is emiterate(20).
emtolerance(#) specifies the convergence tolerance for the EM algorithm. The default is
emtolerance(1e-10). EM iterations will be halted once the log (restricted) likelihood changes
by a relative amount less than #. At that point, optimization switches to a gradient-based method,
unless emonly is specified, in which case maximization stops.
emonly specifies that the likelihood be maximized exclusively using EM. The advantage of specifying
emonly is that EM iterations are typically much faster than those for gradient-based methods.
The disadvantages are that EM iterations can be slow to converge (if at all) and that EM provides
no facility for estimating standard errors for the random-effects parameters. emonly is available
only with unweighted estimation and when residuals are modeled as independent with constant
variance.
emlog specifies that the EM iteration log be shown. The EM iteration log is, by default, not
displayed unless the emonly option is specified.
emdots specifies that the EM iterations be shown as dots. This option can be convenient because
the EM algorithm may require many iterations to converge.
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Maximization
maximize options: difficult, technique(algorithm spec), iterate(#), [ no ] log, trace,
gradient, showstep, hessian, showtolerance, tolerance(#), ltolerance(#),
nrtolerance(#), and nonrtolerance; see [R] maximize. Those that require special mention
for mixed are listed below.
For the technique() option, the default is technique(nr). The bhhh algorithm may not be
specified.
matsqrt (the default), during optimization, parameterizes variance components by using the matrix
square roots of the variance–covariance matrices formed by these components at each model level.