When to stop convergence in a mixed model?
WARNING: Stopped because of infinite likelihood. NOTE: The above message was for the following BY group: SampleID=1 NOTE: Convergence criteria met. NOTE: The above message was for the following BY group: SampleID=2 NOTE: Convergence criteria met. NOTE: Estimated G matrix is not positive definite.
Why does the optimization not converge in a mixed model?
The optimization might not converge, either because the initial guess is poor or because the model is not a good fit to the data. SAS regression procedures for which this might happen include PROC LOGISTIC, GENMOD, MIXED, GLMMIX, and NLMIXED. For mixed models, several problems can occur if you have a misspecified model.
Can a G lmer fit produce a convergence warning?
[g]lmer fits may produce convergence warnings; these do not necessarily mean the fit is incorrect (see “Theoretical details” below). The following steps are recommended assessing and resolving convergence warnings (also see examples below):
Where do I find convergence criteria and error tolerances?
The “Convergence criteria and error tolerances” specified on the “Nonlinear Setup > Advanced Settings” dialog (Figure 2). Unless you have a reason to change these values, they are normally left at the default values as shown below. Figure 1.
What’s the difference between G and your in a mixed model?
The variance-covariance matrix G is often used to specify subject-specific effects, whereas R specifies residual effects. A goal of mixed models is to specify the structure of the G and/or R matrices and estimate the variance-covariance parameters. Because G is a covariance matrix, G must be positive semidefinite.
What is the goal of a mixed model?
A goal of mixed models is to specify the structure of the G and/or R matrices and estimate the variance-covariance parameters. Because G is a covariance matrix, G must be positive semidefinite. A nondegenerate covariance matrix will be fully positive definite.