The `svylme`

package does maximum (weighted pairwise) likelihood. Linear mixed model software tends to also provide REML, either as an option or as the default. Why not here?

I don’t think it would be too hard to take the definition of the REML criterion and weight and pairwise it. The question is whether that’s actually what is wanted.

REML deals with the bias in variance components caused by using up degrees of freedom on the fixed effects. It’s a generalisation of using \(n-p\) instead of \(n-1\) or \(n\) in the denominator of the residual variance. It’s most important for highly structured experimental designs where \(p/n\) is not negligible. If you have that sort of design, you wouldn’t just be doing random sampling from the data. Random sampling would mess up all your painstakingly-designed orthogonalities. It might even end up with a lot of parameters being unidentifiable: if \(p/n\) is not negligible, you can’t hope to estimate \(p\) things from a small subsample of the \(n\).

There might well be situations where you would follow up a complex experimental design with measurements made on a subsample, but good design and analysis isn’t going to just fall out of bolting on sampling to the end of the design. The problem will need some combination of two-phase experimental design and two-phase sampling.

Until I have a better idea of what it might be useful for, I’m not planning to add ‘REML’ as an option to `svy2lme`

.