The following statements are available in PROC MIXED.
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. The LSMESTIMATE, SLICE, and STORE statements are shared with many procedures. Summary descriptions of functionality and syntax for these statements are also given after the PROC MIXED statement in alphabetical order, but you can find full documentation on them in Chapter 19, Shared Concepts and Topics.
Table 58.1 summarizes the basic functions and important options of each PROC MIXED statement. The syntax of each statement in Table 58.1 is described in the following sections in alphabetical order after the description of the PROC MIXED statement.
Statement 
Description 
Important Options 

Invokes the procedure 
DATA= specifies input data set, METHOD= specifies estimation method 

Performs multiple PROC MIXED analyses in one invocation 
None 

Declares qualitative variables that create indicator variables in design matrices 
None 

Lists additional variables to be included in predicted values tables 
None 

Specifies dependent variable and fixed effects, setting up 
S requests solution for fixedeffects parameters, DDFM= specifies denominator degrees of freedom method, OUTP= outputs predicted values to a data set, INFLUENCE computes influence diagnostics 

Specifies random effects, setting up and 
SUBJECT= creates blockdiagonality, TYPE= specifies covariance structure, S requests solution for randomeffects parameters, G displays estimated 

Sets up 
SUBJECT= creates blockdiagonality, TYPE= specifies covariance structure, R displays estimated blocks of , GROUP= enables betweensubject heterogeneity, LOCAL adds a diagonal matrix to 

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 

Performs a samplingbased Bayesian analysis for variance component models 
NSAMPLE= specifies the sample size, SEED= specifies the starting seed 

Constructs custom hypothesis tests 
E displays the matrix coefficients 

Constructs custom scalar estimates 
CL produces confidence limits 

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 

Provides custom hypothesis tests among the least squares means 
ADJUST= determines the method for multiple comparison adjustment of LSmean differences, JOINT requests a joint F or chisquare test for the rows of the estimate 

Performs a partitioned analysis of LS–means for an interaction 
ADJUST= determines the method for multiple comparison adjustment of LSmean differences, DIFF requests differences of LSmeans 

Saves the context and results of the analysis 
LABEL= adds a custom label 

Specifies a variable by which to weight 
None 