The LOGISTIC Procedure |
LSMESTIMATE Statement |
The LSMESTIMATE statement provides a mechanism for obtaining custom hypothesis tests among least squares means.
Table 51.6 summarizes important options in the LSMESTIMATE statement.
Option |
Description |
---|---|
Construction and Computation of LS-Means |
|
Modifies covariate values in computing LS-means |
|
Computes separate margins |
|
Specifies a list of values to divide the coefficients |
|
Specifies the weighting scheme for LS-means computation as determined by a 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 () |
|
Performs one-sided, lower-tailed inference |
|
Adjusts multiple comparison p-values further in a step-down fashion |
|
Specifies values under the null hypothesis for tests |
|
Performs one-sided, upper-tailed inference |
|
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 matrix |
|
Prints the matrix |
|
Produces a joint or chi-square test for the LS-means and LS-means 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 LS-means estimates |
|
Computes and displays estimates and standard errors of LS-means (but not differences) on the inverse linked scale |
For details about the syntax of the LSMESTIMATE statement, see the section LSMESTIMATE Statement of 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.
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