Use the drop-down menu to specify the estimation method to be used for
Binary
,
Nominal
, or
Ordinal
trait
types and a
model
that includes
random effects
. PROC GLIMMIX is used to perform the estimation.
|
Residual
Pseudo-Likelihood method in which the expansion locus of the expansion is the vector of random effects solutions.
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|
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Residual Pseudo-Likelihood method in which the expansion locus of the expansion is the
mean
of the random effects.
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This method works best with only a few
observations
per level of random effect because the calculations can be very slow.
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Pseudo-likelihood methods for generalized linear
mixed models
can be cast in terms of Taylor series expansions (linearizations) of the GLMM.
Residual methods account for the
fixed effects
in the construction of the objective function, which reduces the bias in
covariance
parameter estimates. Estimation methods involving Taylor series create pseudo-data for each optimization. Those data are transformed to have zero mean in a residual method. Although the covariance parameter estimates in a residual method are the maximum likelihood estimates for the transformed problem, the fixed-effects estimates are (estimated) generalized least squares estimates. In a likelihood method that is not residual based, both the covariance parameters and the fixed-effects estimates are maximum likelihood estimates, but the former are known to have greater bias. In some problems, residual likelihood estimates of covariance parameters are unbiased.
Refer to the
SAS PROC GLIMMIX documentation
for additional details.
