The LSMEANS statement computes and compares least squares means (LSmeans) of fixed effects. LSmeans are predicted population margins—that is, they estimate the marginal means over a balanced population. In a sense, LSmeans are to unbalanced designs as class and subclass arithmetic means are to balanced designs.
Table 60.6 summarizes the options available in the LSMEANS statement.
Table 60.6: LSMEANS Statement Options
Option 
Description 

Construction and Computation of LSMeans 

Modifies the covariate value in computing LSmeans 

Computes separate margins 

Requests differences of LSmeans 

Specifies the weighting scheme for LSmeans computation as determined by the input data set 

Tunes estimability checking 

Degrees of Freedom and pvalues 

Determines the method for multiplecomparison adjustment of LSmeans differences 

Determines the confidence level () 

Adjusts multiplecomparison pvalues further in a stepdown fashion 

Statistical Output 

Constructs confidence limits for means and mean differences 

Displays the correlation matrix of LSmeans 

Displays the covariance matrix of LSmeans 

Prints the matrix 

Produces a "Lines" display for pairwise LSmeans differences 

Prints the LSmeans 

Requests graphs of means and mean comparisons 

Specifies the seed for computations that depend on random numbers 

Generalized Linear Modeling 

Exponentiates and displays estimates of LSmeans or LSmeans differences 

Computes and displays estimates and standard errors of LSmeans (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 
For details about the syntax of the LSMEANS statement, see the section LSMEANS Statement in Chapter 19: Shared Concepts and Topics.
Note: If you have classification variables in your model, then the LSMEANS statement is allowed only if you also specify the PARAM=GLM option.