The PLM Procedure
| LSMEANS Statement |
- LSMEANS <model-effects> </ options> ;
The LSMEANS statement computes and compares least squares means (LS-means) of fixed effects. LS-means are predicted population margins—that is, they estimate the marginal means over a balanced population. In a sense, LS-means are to unbalanced designs as class and subclass arithmetic means are to balanced designs.
Table 68.5 summarizes important options in the LSMEANS statement.
Option |
Description |
|---|---|
Construction and Computation of LS-Means |
|
Modifies the covariate value in computing LS-means |
|
Computes separate margins |
|
Requests differences of LS-means |
|
Specifies the weighting scheme for LS-means computation as determined by the input data set |
|
Tunes estimability checking |
|
Degrees of Freedom and p-values |
|
Determines the method for multiple comparison adjustment of LS-means differences |
|
Determines the confidence level ( |
|
Adjusts multiple comparison p-values further in a step-down fashion |
|
Statistical Output |
|
Constructs confidence limits for means and mean differences |
|
Displays the correlation matrix of LS-means |
|
Displays the covariance matrix of LS-means |
|
Prints the |
|
Produces a "Lines" display for pairwise LS-means differences |
|
Prints the LS-means |
|
Requests ODS statistical graphics of means and mean comparisons |
|
Specifies the seed for computations that depend on random numbers |
|
Generalized Linear Modeling |
|
Exponentiates and displays estimates of LS-means or LS-means differences |
|
Computes and displays estimates and standard errors of LS-means (but not differences) on the inverse linked scale |
|
Reports (simple) differences of least squares means in terms of odds ratios if permitted by the link function |
|
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matrix For details about the syntax of the LSMEANS statement, see the section LSMEANS Statement of Chapter 19, Shared Concepts and Topics.