The PROBIT 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 81.29 summarizes the options available in the LSMEANS statement.

Table 81.29: LSMEANS Statement Options

Option

Description

Construction and Computation of LS-Means

AT

Modifies the covariate value in computing LS-means

BYLEVEL

Computes separate margins

DIFF

Requests differences of LS-means

OM=

Specifies the weighting scheme for LS-means computation as determined by the input data set

SINGULAR=

Tunes estimability checking

Degrees of Freedom and p-values

ADJUST=

Determines the method for multiple-comparison adjustment of LS-means differences

ALPHA= $\alpha $

Determines the confidence level ($1-\alpha $)

STEPDOWN

Adjusts multiple-comparison p-values further in a step-down fashion

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

Prints the $\mb{L}$ matrix

LINES

Produces a "Lines" display for pairwise LS-means differences

MEANS

Prints the LS-means

PLOTS=

Requests graphs of means and mean comparisons

SEED=

Specifies the seed for computations that depend on random numbers


For details about the syntax of the LSMEANS statement, see the section LSMEANS Statement in Chapter 19: Shared Concepts and Topics.