LOGSELECT Procedure

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.

LS-means can be computed for any effect in the procedure’s MODEL statement that involves only classification variables. You can specify multiple model-effects in one or more LSMEANS statements that appear after the MODEL statement. When you restore a model from an item store, you must specify the desired model-effects in the EFFECT= option before the slash (/).

Table 6 summarizes the options available in the LSMEANS statement.

Table 6: LSMEANS Statement Options

Option	Description
Construction and Computation of LS-Means
AT	Modifies covariate values used to compute LS-means
ATVAR	Modifies covariate values for restored models
DIFF	Computes differences of LS-means
SINGULAR=	Tunes estimability checking
p-Values
ADJUST=	Specifies the multiple comparison adjustment method for LS-means differences
ALPHA=	Specifies the confidence level ()
Statistical Output
CL	Constructs confidence limits for means and mean differences
CORR	Displays the correlation matrix of LS-means
COV	Displays the covariance matrix of LS-means
E	Displays the matrix

For more information about the syntax of the LSMEANS statement, see the section LSMEANS Statement in Chapter 2, Shared Concepts.

LS-means analysis is not available if you use LASSO or elastic net selection.