Douglas Bates wrote:
I'm not sure what model you want to fit here. To specify a random effect in lme you need both a grouping factor and a model matrix. The error message indicates that lme is unable to determine a grouping factor. It would be correct syntax if you added a single level factor to the data frame and used that but then the model fit would fail because you would be trying to estimate a variance in a model where there is no variation in the term.
O.k. I see and think I understand it.
It seems to me that you are trying to estimate parameters in a mixed-effects model without any random effects and lme can't do that.
Yes, what I want is a model without any random effects to be tested against a model with random effects. I want to show, that the random effects are negligible but that we account for pseudo replicates and have tested this explicitely.
I'm not sure what is better: to leave the random effects in the model or simply an LR test against a linear model fitted by lm. I've never seen such an example in the books. Or have I missed a global alternative here?
Thomas P.
I would recommend the likelihood ratio test against a linear model fit by lm. The p-value returned from this test will be conservative because you are testing on the boundary of the parameter space.
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