SAS/STAT 922 User's Guide: The MIXED Procedure (Book Excerpt)

4560 ✦ Chapter 56: The MIXED Procedure
This documentation refers to analyses when n > 0 simply as iterative influence analysis,
even if final covariance parameter estimates can be updated in a single step (for example,
when METHOD=MIVQUE0 or METHOD=TYPE3). This nomenclature reflects the
fact that only if n > 0 are all model parameters updated, which can require additional
iterations. If n > 0 and METHOD=REML (default) or METHOD=ML, the procedure
updates fixed effects and variance-covariance parameters after removing the selected
observations with additional Newton-Raphson iterations, starting from the converged
estimates for the entire data. The process stops for each observation or set of observations
if the convergence criterion is satisfied or the number of further iterations exceeds n. If
n > 0 and METHOD=TYPE1, TYPE2, or TYPE3, ANOVA estimates of the covariance
parameters are recomputed in a single step.
Compared to noniterative updates, the computations are more involved. In particular
for large data sets and/or a large number of random effects, iterative updates require
considerably more resources. A one-step (ITER=1) or two-step update might be a good
compromise. The output includes the number of iterations performed, which is less than
n if the iteration converges. If the process does not converge in n iterations, you should
be careful in interpreting the results, especially if n is fairly large.
Bounds and other restrictions on the covariance parameters carry over from the full-data
model. Covariance parameters that are not iterated in the model fit to the full data
(the NOITER or HOLD= option in the PARMS statement) are likewise not updated
in the refit. In certain models, such as random-effects models, the ratios between the
covariance parameters and the residual variance are maintained rather than the actual
value of the covariance parameter estimate (see the section “Influence Diagnostics” on
page 4613).
KEEP=n
determines how many observations are retained for display and in the output data set or
how many tuples if you specify SIZE=. The output is sorted by an influence statistic as
discussed for the SIZE= suboption.
SELECT=value-list
specifies which observations or effect levels are chosen for influence calculations. If the
SELECT= suboption is not specified, diagnostics are computed as follows:
for all observations, if EFFECT= or SIZE= are not given
for all levels of the specified effect, if EFFECT= is specified
for all tuples of size k formed from the observations in value-list, if SIZE=k is
specified
When you specify an effect with the EFFECT= option, the values in value-list represent
indices of the levels in the order in which PROC MIXED builds classification effects.
Which observations in the data set correspond to this index depends on the order of the
variables in the CLASS statement, not the order in which the variables appear in the
interaction effect. See the section “Parameterization of Mixed Models” on page 4606
to understand precisely how the procedure indexes nested and crossed effects and how
levels of classification variables are ordered. The actual values of the classification
variables involved in the effect are shown in the output so you can determine which
observations were removed.