The LSMESTIMATE statement provides a mechanism for obtaining custom hypothesis tests among least squares means.
Table 60.7 summarizes the options available in the LSMESTIMATE statement.
Table 60.7: LSMESTIMATE Statement Options
Option 
Description 

Construction and Computation of LSMeans 

Modifies covariate values in computing LSmeans 

Computes separate margins 

Specifies a list of values to divide the coefficients 

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

Tunes estimability checking 

Degrees of Freedom and pvalues 

Determines the method for multiplecomparison adjustment of LSmeans differences 

Determines the confidence level () 

Performs onesided, lowertailed inference 

Adjusts multiplecomparison pvalues further in a stepdown fashion 

Specifies values under the null hypothesis for tests 

Performs onesided, uppertailed inference 

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 

Prints the matrix 

Produces a joint F or chisquare test for the LSmeans and LSmeans differences 

Specifies the seed for computations that depend on random numbers 

Generalized Linear Modeling 

Specifies how to construct estimable functions with multinomial data 

Exponentiates and displays LSmeans estimates 

Computes and displays estimates and standard errors of LSmeans (but not differences) on the inverse linked scale 
For details about the syntax of the LSMESTIMATE statement, see the section LSMESTIMATE Statement in Chapter 19: Shared Concepts and Topics.
Note: If you have classification variables in your model, then the LSMESTIMATE statement is allowed only if you also specify the PARAM=GLM option.