Comments (3)
I am so sorry to bother you with another question, but I just looked at the variance-covariance of the random effects from the first model call given above, one with the MQL method, and one with the PQL method. They appear to be very different, and I have reprinted them below. Anything you can do to help me would be very much appreciated.
MQL:
(Co-)Variances:
Grouping level: 1
Estimate Std.Err.
For~1 2.7604 0.8721
HO~1 -0.6779 8.9009 1.1230 2.7437
PQL:
(Co-)Variances:
Grouping level: 1
Estimate Std.Err.
For~1 1.9713 0.6556
HO~1 0.3991 3.7709 0.5324 0.7616
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Please direct support question to me via email instead of using the issues interface (which is for bug reports).
Usually one should be careful when interpreting the results of anova()
when approximate likelihood methods are used for estimation. This applies in particular to PQL and MQL estimates, which are known to be biased in particularly when clusters are small. In the case of your result, the conclusion is pretty unequivocal, however, the data do not lead support to rejecting the null hypothesis. But anyway, if you want to be sure, I would suggest using simulation-based p-values.
The difference between the PQL and MQL estimates may be sampling fluctuations of the biases of PQL and MQL going into different direction. This is of course difficult to decide unless one knows the true parameter values.
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