[R-sig-ME] Random-effects variance-covariance matrix in lmer?

[Phillip Alday]

This is a frequent question for lme4 and MixedModels.jl and the answer
is....

it's not going to be supported in any mainstream release of either
package in the foreseeable future.

You can however still get some of these structures by clever
specification of the random effects.

For example:

data(Machines,package="nlme")
library("lme4")

m <- lmer(score ~ 1 + Machine + (0+Machine|Worker), Machines)

# with compound symmetry
m_cs <- lmer(score ~ 1 + Machine + (1|Worker) + (1|Worker:Machine),
Machines)

# pdDiag -- for continuous variables, you could just use the || syntax

m_diag <- lmer(score ~ 1 + Machine + (0+dummy(Machine,"A")|Worker) +
(0+dummy(Machine,"B")|Worker) + (0+dummy(Machine,"C")|Worker), Machines)

I'll leave pdIdent as an exercise for the OP. ;)

Best,
Phillip

On 02/10/2019 15:50, matthias.suter using agroscope.admin.ch wrote:
> Hi all
> 
> I'm looking for a way to specify a more complex variance-covariance matrix for the random effects in lmer(). For example, in lme() (nlme package), there are the pdMat classes to specify e.g. a compound symmetry (pdCompSym) or a multiple of an identity (pdIdent) structure for the variance-covariance matrix of random effects. Is there a possibility to code an equivalent for lmer()? I'm specifically interested in "compound symmetry" and "multiple of an identity".
> 
> I assume that Douglas Bates or Ben Bolker have a distinct answer on that.
> 
> Thanks in advance,
> Matthias
> 
> 
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