The following statements are available in PROC HPMIXED:
Items within angle brackets ( < > ) are optional. The CONTRAST, ESTIMATE, LSMEANS, RANDOM, and TEST statements can appear multiple times; all other statements can appear only once.
The PROC HPMIXED and MODEL statements are required, and the MODEL statement must appear after the CLASS statement if these statements are included. The BY, CLASS, MODEL, ID, OUTPUT, TEST, RANDOM, REPEATED and WEIGHT statements are described in full after the PROC HPMIXED statement in alphabetical order. The EFFECT, is shared with many other procedures. Summary descriptions of functionality and syntax for this statement is also given after the PROC HPMIXED statement in alphabetical order, but you can find full documentation on it in Chapter 19, Shared Concepts and Topics.
Table 45.1 summarizes the basic functions and important options of each PROC HPMIXED statement.
Statement |
Description |
Important Options |
---|---|---|
Invokes the procedure |
DATA= specifies input data set, METHOD= specifies estimation method |
|
Performs multiple PROC HPMIXED 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 fixed-effects parameters, DDFM= specifies denominator degrees of freedom method |
|
Specifies random effects, setting up and |
SUBJECT= creates block-diagonality, TYPE= specifies covariance structure, S requests solution for random-effects parameters |
|
Sets up |
SUBJECT= creates block-diagonality, TYPE= specifies covariance structure, R= displays estimated blocks of , GROUP= enables between-subject heterogeneity |
|
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 |
|
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, CL produces confidence limits, SLICE= tests simple effects |
|
Specifies a variable by which to weight |
None |