What is the coefficient of the lsmeans statement?
For the first two LSMEANS statements, the LS-means coefficient for X1 is (the mean of X1) and for X2 is (the mean of X2 ). However, for the first LSMEANS statement, the coefficient for X1 * X2 is , but for the second LSMEANS statement, the coefficient is .
How to interpret SE for lsmeans-SAS support?
The default is the denominator degrees of freedom taken from the “Type III Tests of Fixed Effects” table corresponding to the LS-means effect, 350 is the denominator degrees of freedom for the tests of fixed effects resulting from the MODEL. The documentation points out –
How to specify the difftype control in lsmeans?
The difftype CONTROL requests the differences with a control, which, by default, is the first level of each of the specified LSMEANS effects. To specify which levels of the effects are the controls, list the quoted formatted values in parentheses after the keyword CONTROL.
How is the approximate standard error for the LS-mean computed?
The approximate standard errors for the LS-mean is computed as the square root of . LS-means can be computed for any effect in the MODEL statement that involves CLASS variables. You can specify multiple effects in one LSMEANS statement or in multiple LSMEANS statements,…
How is the estimate statement tested in proc mixed?
As in the ESTIMATE statement, the matrix is tested for estimability, and if this test fails, PROC MIXED displays “Non-est” for the LS-means entries. Assuming the LS-mean is estimable, PROC MIXED constructs an approximate t test to test the null hypothesis that the associated population quantity equals zero.
Which is version of lmertest calculates least squares?
lmerTest (version 2.0-36) lsmeans: Calculates Least Squares Means and Confidence Intervals for the factors of a fixed part of mixed effects model of lmer object.
How is the lsmeans statement related to the GLM procedure?
The LSMEANS statement computes least squares means (LS-means) of fixed effects. As in the GLM procedure, 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.