Why are you using Glimmix, if the data (i.e., the residuals) appear normally distributed? I know you can use Glimmix for such data, but why not Mixed, which probably gives you more control over the structure of the residuals and less convergence and infinite-likelihood problems. And I always use ddfm=Sat in Mixed, which seems to give sensible degrees of freedom for everything. As for negative variance, yeah, I don't have any problem with it. When a true variance is small and the uncertainty is large, sampling uncertainty can easily result in an observed negative variance, and even if the sample variance is positive, the only way you can get sensible confidence limits is to allow them (i.e., the lower limit) to be negative.
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