|
ASYCORR
|
|
•
|
Displays asymptotic correlation matrix of covariance parameter estimates. It is computed from the corresponding asymptotic covariance matrix (see the description of the ASYCOV option, below) |
|
|
ASYCOV
|
|
•
|
This option requests that the asymptotic covariance matrix of the covariance parameters be displayed. By default, this matrix is the observed inverse Fisher information matrix, which equals 2H-1, where H is the Hessian (second derivative) matrix of the objective function. |
|
|
EMPIRICAL
|
|
•
|
Computes the estimated variance-covariance matrix of the fixed-effects parameters by using the asymptotically consistent (or sandwich) estimator. |
|
|
IC=None
|
|
•
|
The GLIMMIX procedure normally computes various IC that typically apply a penalty to the (possibly restricted) log likelihood, log pseudo-likelihood, or log quasi-likelihood that depends on the number of parameters and/or the sample size. |
|
•
|
Select this option to suppress computation of information criteria (IC) in the "Fit Statistics" table. This is the default for models based on pseudo-likelihoods. |
|
|
IC=PQ
|
|
•
|
The GLIMMIX procedure normally computes various IC that typically apply a penalty to the (possibly restricted) log likelihood, log pseudo-likelihood, or log quasi-likelihood that depends on the number of parameters and/or the sample size. |
|
•
|
Select this option to request that the penalties include the number of fixed-effects parameters, when estimation in models with random effects is based on a residual (restricted) likelihood. |
Note: For METHOD=MSPL, METHOD=MMPL, METHOD=LAPLACE, and METHOD=QUAD, the IC=Q and IC=PQ options produce the same results.
|
|
IC=Q
|
|
•
|
The GLIMMIX procedure normally computes various IC that typically apply a penalty to the (possibly restricted) log likelihood, log pseudo-likelihood, or log quasi-likelihood that depends on the number of parameters and/or the sample size. |
|
•
|
This is the default option for linear mixed model with normal errors, and the resulting information criteria are identical to the IC option specified using PROC MIXED Options. |
Note: For METHOD=MSPL, METHOD=MMPL, METHOD=LAPLACE, and METHOD=QUAD, the IC=Q and IC=PQ options produce the same results.
|
|
INITGLM
|
|
•
|
Requests that the estimates from a generalized linear model fit (a model without random effects) be used as the starting values for the generalized linear mixed model. This option is the default for METHOD=LAPLACE and METHOD=QUAD |
|
|
ITDETAILS
|
|
•
|
Displays the parameter values at each iteration and enables the writing of notes to the SAS log pertaining to infinite likelihood and singularities during Newton-Raphson iterations. |
|
|
LOGNOTE
|
|
•
|
Writes periodic notes to the SAS log describing the current status of computations. |
|
•
|
Note: This option was designed for use with analyses requiring extensive CPU resources. |
|
|
METHOD=RSPL
|
|
•
|
Specifies the estimation method in a generalized linear mixed model (GLMM). |
|
•
|
The RSPL option specifies that the estimation is based on a Residual likelihood with a Subject-specific expansion locus. The PL abbreviation identifies the method as a pseudo-likelihood technique. |
|
•
|
This is the default option. |
|
|
METHOD=MSPL
|
|
•
|
Specifies the estimation method in a generalized linear mixed model (GLMM). |
|
•
|
The MSPL option specifies that the estimation is based on a Maximum likelihood (R) with a Subject-specific expansion locus. The PL abbreviation identifies the method as a pseudo-likelihood technique. |
|
|
METHOD=RMPL
|
|
•
|
Specifies the estimation method in a generalized linear mixed model (GLMM). |
|
•
|
The RMPL option specifies that the estimation is based on a Residual likelihood with a Marginal-specific expansion locus. The PL abbreviation identifies the method as a pseudo-likelihood technique. |
|
|
METHOD=MMPL
|
|
•
|
Specifies the estimation method in a generalized linear mixed model (GLMM). |
|
•
|
The MMPL option specifies that the estimation is based on a Maximum likelihood with a Marginal-specific expansion locus. The PL abbreviation identifies the method as a pseudo-likelihood technique. |
|
|
METHOD=LAPLACE
|
|
•
|
Approximates the marginal likelihood by using Laplace’s method. |
|
•
|
Twice the negative of the resulting log-likelihood approximation is the objective function that the procedure minimizes to determine parameter estimates. Laplace estimates typically exhibit better asymptotic behavior and less small-sample bias than pseudo-likelihood estimators. On the other hand, the class of models for which a Laplace approximation of the marginal log likelihood is available is much smaller compared to the class of models to which PL estimation can be applied. |
|
|
METHOD=QUAD
|
|
•
|
Approximates the marginal log likelihood with an adaptive Gauss-Hermite quadrature. |
|
•
|
Compared to METHOD=LAPLACE, the models for which parameters can be estimated by quadrature are further restricted. |
|
|
NOBOUND
|
|
•
|
Requests the removal of boundary constraints on covariance parameters. |
|
•
|
For example, variance components have a default lower boundary constraint of 0, and the NOBOUND option allows their estimates to be negative. |
|
|
NOBSDETAIL
|
|
•
|
Adds detailed information to the "Number of Observations" table to reflect how manySuzanne Fields were excluded from the analysis and for which reason. |
|
|
NOCLPRINT
|
|
•
|
Suppresses the display of the Class Level Information table if you do not specify number. |
|
•
|
If you do specify number, only levels with totals that are less than number are listed in the table. |
|
|
NOITPRINT
|
|
•
|
Suppresses the display of the Iteration History table. |
|
|
NOFIT
|
|
•
|
Suppresses fitting of the model. |
|
|
NOINITGLM
|
|
•
|
Requests that the starting values for the fixed effects not be obtained by first fitting a generalized linear model. |
|
•
|
This option is the default for the pseudo-likelihood estimation methods and for the linear mixed model. For the pseudo-likelihood methods, starting values can be implicitly defined based on an initial pseudo-data set derived from the data and the link function. For linear mixed models, starting values for the fixed effects are not necessary. |
|
|
NOPROFILE
|
|
•
|
Includes the residual variance as part of the Newton-Raphson iterations. |
|
•
|
This option applies only to models that have a residual variance parameter. |
|
•
|
By default, this parameter is profiled out of the likelihood calculations. |
|
|
NOREML
|
|
•
|
Determines the denominator for the computation of the scale parameter in a GLM for normal data and for overdispersion parameters. |
|
•
|
In GLMM models fit by pseudo-likelihood methods, the NOREML option changes the estimation method to the nonresidual form. |
|
|
OR
|
|
•
|
Requests that odds ratios be added to the output when applicable. |
|
|
ORDER=DATA
|
|
•
|
Specifies that the levels of the classification variables are sorted in the order in which they appear in the input data set. |
|
|
ORDER=FORMATTED
|
|
•
|
Specifies that the levels of the classification variables are sorted in the order specified by an external formatted variable. |
|
|
ORDER=FREQ
|
|
•
|
Specifies that the levels of the classification variables are sorted in the order of descending frequency count. |
|
|
ORDER=INTERNAL
|
|
•
|
Specifies that the levels of the classification variables are sorted in the order specified by an unformatted variable. |
|
|
PROFILE
|
|
•
|
Requests that scale parameters be profiled from the optimization, if possible. |
|
•
|
This is the default for generalized linear mixed models. |
Note: In generalized linear models with normally distributed data, you can use the PROFILE option to request profiling of the residual variance.
|
|
SCOREMOD
|
|
•
|
Requests that the Hessian matrix in GLMMs be based on a modified scoring algorithm, provided that PROC GLIMMIX is in scoring mode when the Hessian is evaluated. |
|
