LMIXED Procedure
REPEATED Statement
REPEATED repeated-effect </ options>;
The REPEATED statement defines the repeated effect and the residual covariance structure in the mixed model. The residual variance-covariance matrix is denoted as 
. The repeated-effect is required and consists entirely of classification variables. The levels of the repeated-effect must be different for each observation within a subject in order to avoid the singular matrix.
Table 15 summarizes the options available in the REPEATED statement.
Table 15: Summary of REPEATED Statement Options
| Option | Description |
|---|---|
| Construction of Covariance Structure | |
| GROUP= | Defines an effect that specifies heterogeneity in the residual covariance structure |
| NOCONVERT | Prevents the covariance structure from being internally converted |
| SUBJECT= | Identifies the subjects in the residual covariance structure |
| TYPE= | Specifies the residual covariance structure (the default is VC) |
| Statistical Output | |
| R= | Displays blocks of the estimated matrix |
| RC= | Display the Cholesky root (lower) of blocks of the estimated matrix |
| RCI= | Displays the inverse Cholesky root (lower) of blocks of the estimated matrix |
| RCORR= | Displays the correlation matrix that corresponds to blocks of the estimated matrix |
| RI= | Displays the inverse of blocks of the estimated matrix |
In the current release, the REPEATED statement is supported only when DMMETHOD=SPARSE.
You must specify one or both of the following options after a slash (/):
-
GROUP=effect
GRP=effect defines an effect that specifies heterogeneity in the residual covariance structure. All observations that have the same level of the effect have the same covariance parameters. Each new level of the effect produces a new set of covariance parameters that have the same structure as the original group. You should exercise caution in defining the effect, because strange covariance patterns can result when it is misused. Also, the effect can greatly increase the number of estimated covariance parameters, which can adversely affect the optimization process.
- NOCONVERT
prevents the covariance structure from being internally converted to an equivalent one that leads to efficient computation.
-
SUBJECT=effect
SUB=effect identifies the subjects in your mixed model. Complete independence is assumed across subjects; therefore, this option produces a block-diagonal structure in
that has identical blocks. The data set is not required to be grouped by the effect. When the effect consists entirely of classification variables, the blocks of correspond to observations that share the same level of that effect. These blocks are sorted according to this effect as well.
You can also specify the following options after a slash (/):
- R<=value-list>
-
displays blocks of the estimated
matrix. The first block that is determined by the SUBJECT= effect is the default displayed block. The value-list indicates the subjects for which blocks of
are displayed. For example, the following statement displays block matrices for the first, third, and fifth persons: repeated time / type=un subject=person r=1,3,5;See the PARMS statement for the possible forms of value-list.
- RC<=value-list>
displays the Cholesky root of blocks of the estimated
matrix. The value-list specification is the same as for the R= option. - RCI<=value-list>
displays the inverse Cholesky root of blocks of the estimated
matrix. The value-list specification is the same as for the R= option. - RCORR<=value-list>
displays the correlation matrix that corresponds to blocks of the estimated
matrix. The value-list specification is the same as for the R= option. - RI<=value-list>
produces the inverse of blocks of the estimated
matrix. The value-list specification is the same as for the R= option. - TYPE=covariance-structure
-
specifies the structure of the residual variance-covariance matrix
. The SUBJECT= option defines the blocks of , and the TYPE= option specifies the structure of these blocks. In the current release, the valid values of covariance-structure are AR(1), CHOL, CS, CSH, UN, and VC. For more information about these covariance structure types, see the description in the section RANDOM Statement.