The MIXED Procedure

Syntax: MIXED Procedure

The following statements are available in PROC MIXED.

PROC MIXED <options> ;: BY variables ;; CLASS variables ;; ID variables ;; MODEL dependent = <fixed-effects> </ options> ;; RANDOM random-effects </ options> ;; REPEATED <repeated-effect></ options> ;; PARMS (value-list) ...</ options> ;; PRIOR <distribution >< / options> ;; CONTRAST ’label’ <fixed-effect values ...>
<| random-effect values ...>, ...</ options> ;; ESTIMATE ’label’ <fixed-effect values ...>
<| random-effect values ...></ options> ;; LSMEANS fixed-effects </ options> ;; WEIGHT variable ;

Items within angle brackets ( < > ) are optional. The CONTRAST, ESTIMATE, LSMEANS, and RANDOM statements can appear multiple times; all other statements can appear only once.

The PROC MIXED and MODEL statements are required, and the MODEL statement must appear after the CLASS statement if a CLASS statement is included. The CONTRAST, ESTIMATE, LSMEANS, RANDOM, and REPEATED statements must follow the MODEL statement. The CONTRAST and ESTIMATE statements must also follow any RANDOM statements.

Table 56.1 summarizes the basic functions and important options of each PROC MIXED statement. The syntax of each statement in Table 56.1 is described in the following sections in alphabetical order after the description of the PROC MIXED statement.

Table 56.1 Summary of PROC MIXED Statements
Statement	Description	Important Options
PROC MIXED	invokes the procedure	DATA= specifies input data set, METHOD= specifies estimation method
BY	performs multiple PROC MIXED analyses in one invocation	none
CLASS	declares qualitative variables that create indicator variables in design matrices	none
ID	lists additional variables to be included in predicted values tables	none
MODEL	specifies dependent variable and fixed effects, setting up $\text{[math]}$	S requests solution for fixed-effects parameters, DDFM= specifies denominator degrees of freedom method, OUTP= outputs predicted values to a data set, INFLUENCE computes influence diagnostics
RANDOM	specifies random effects, setting up $\text{[math]}$ and $\text{[math]}$	SUBJECT= creates block-diagonality, TYPE= specifies covariance structure, S requests solution for random-effects parameters, G displays estimated $\text{[math]}$
REPEATED	sets up $\text{[math]}$	SUBJECT= creates block-diagonality, TYPE= specifies covariance structure, R displays estimated blocks of $\text{[math]}$ , GROUP= enables between-subject heterogeneity, LOCAL adds a diagonal matrix to $\text{[math]}$
PARMS	specifies a grid of initial values for the covariance parameters	HOLD= and NOITER hold the covariance parameters or their ratios constant, PARMSDATA= reads the initial values from a SAS data set
PRIOR	performs a sampling-based Bayesian analysis for variance component models	NSAMPLE= specifies the sample size, SEED= specifies the starting seed
CONTRAST	constructs custom hypothesis tests	E displays the $\text{[math]}$ matrix coefficients
ESTIMATE	constructs custom scalar estimates	CL produces confidence limits
LSMEANS	computes least squares means for classification fixed effects	DIFF computes differences of the least squares means, ADJUST= performs multiple comparisons adjustments, AT changes covariates, OM changes weighting, CL produces confidence limits, SLICE= tests simple effects
WEIGHT	specifies a variable by which to weight $\text{[math]}$	none

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