Basic Features F 6535
The first scenario can be generalized to include one set of clusters nested within another. For example,
if students are the experimental unit, they can be clustered into classes, which in turn can be clustered
into schools. Each level of this hierarchy can introduce an additional source of variability and correlation.
The second scenario occurs in longitudinal studies, where repeated measurements are taken over time.
Alternatively, the repeated measures could be spatial or multivariate in nature.
PROC MIXED provides a variety of covariance structures to handle the previous two scenarios. The most
common of these structures arises from the use of random-effects parameters, which are additional unknown
random variables assumed to affect the variability of the data. The variances of the random-effects parameters,
commonly known as variance components, become the covariance parameters for this particular structure.
Traditional mixed linear models contain both fixed- and random-effects parameters, and, in fact, it is the
combination of these two types of effects that led to the name mixed model. PROC MIXED fits not only
these traditional variance component models but numerous other covariance structures as well.
PROC MIXED fits the structure you select to the data by using the method of restricted maximum likelihood
(REML), also known as residual maximum likelihood. It is here that the Gaussian assumption for the data is
exploited. Other estimation methods are also available, including maximum likelihood and MIVQUE0. The
details behind these estimation methods are discussed in subsequent sections.
After a model has been fit to your data, you can use it to draw statistical inferences via both the fixed-effects
and covariance parameters. PROC MIXED computes several different statistics suitable for generating
hypothesis tests and confidence intervals. The validity of these statistics depends upon the mean and variance-
covariance model you select, so it is important to choose the model carefully. Some of the output from PROC
MIXED helps you assess your model and compare it with others.
Basic Features
PROC MIXED provides easy accessibility to numerous mixed linear models that are useful in many common
statistical analyses. In the style of the GLM procedure, PROC MIXED fits the specified mixed linear model
and produces appropriate statistics.
Here are some basic features of PROC MIXED:
covariance structures, including variance components, compound symmetry, unstructured, AR(1),
Toeplitz, spatial, general linear, and factor analytic
GLM-type grammar, by using MODEL, RANDOM, and REPEATED statements for model specifica-
tion and CONTRAST, ESTIMATE, and LSMEANS statements for inferences
appropriate standard errors for all specified estimable linear combinations of fixed and random effects,
and corresponding t and F tests
subject and group effects that enable blocking and heterogeneity, respectively
REML and ML estimation methods implemented with a Newton-Raphson algorithm
capacity to handle unbalanced data
ability to create a SAS data set corresponding to any table
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