Mixed models, did someone figure out lme? [🇷 for BE/BA]

posted by ElMaestro  – Denmark, 2020-08-10 16:16 (1350 d 01:10 ago) – Posting: # 21850
Views: 3,463

Hi all,

Oh, how life throws you a curve ball every now and then....

As you know I am writing optimisers and algos for REML fits for replicated and semireplicated BE designs.
For replicated (2x2x4) I'd like to have something to compare with. As I am neither rich nor well connected I can't get access to SAS or WinNonlin, but lucky me, there are free packages and functions in R for mixed models.
I gather lmer does not allow for different within-variances, so I am looking at lme.

Is lme the most hostile and poorly documented function ever in existence??
I have no idea how to use it after having read the docs approximately 50 million times; all I want is to fit a model with five variance components:
Within Ref, within Test, between Ref, between Test, and their covariance.

I once had the book by Pinheiro & Bates and it is a terrible document for anyone trying to get familiar with mixed models. I think it went into the burner a cold November night 10 years ago. You need to know and understand all structures and syntax examples before anything in that book makes sense. So nothing makes sense to me, obviously. A bit like real life, you might say.

So, I was thinking the bebac forum must come to my rescue and I read with equal amounts of joy and equal amounts of horror this old post written by one of the greatest of the truly great. :-D:party::crying:

Does it really have to be that hard??
I googled around but I have not really seen much about lme for BE. Where do I start, does anyone have suggestions on how to use lme for a 224BE design and be able to extract meaningful variance components? Did anyone since then figure out easier and possibly more meaningful ways to work with lme such that the model is clearly defined in an understandable way and such that extraction of swr and swt is not plain meaningless torture?

Mixed models are no good for my mental health.
Many thanks. :cool:

Pass or fail!

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