[R-sig-ME] lme: several slopes, same variance, no correlation

Thierry Onkelinx thierry.onkelinx at inbo.be
Fri Jul 15 11:44:25 CEST 2016

Dear Ben,

Crossed random effects are doable but not easy in nlme. It is described
somewhere in Pinheiro and Bates (2000).

However, correlation structures in nlme work only on the residuals within
the same level of the random effects. In case of nested random effects the
most detailed level is used. The residuals of observations from different
random effect levels are assumed to be independent! I'm not sure how it
works with crossed random effects but it won't surprise me if it would use
the levels of id1:id2:id3:id4. That is something you may, or may not, want.

I'd suggest that you think on how the correlation structure should work
before you try the crossed random effects in nlme. If the correlation
structure doesn't make sense, then you don't have to bother the switch from
lme4 to nlme.

Another option would be to switch to INLA (www.rinla.org) which allows for
correlated random effects.

Best regards,

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey

2016-07-15 11:16 GMT+02:00 Ben Pelzer <b.pelzer op maw.ru.nl>:

> Dear list,
> I am trying to fit a model with 4 crossed (no hierarchy) random effects
> which all four are considered to be independent draws from one and the same
> normal distribution. So, all four have the same variance and the
> correlations are zero. In lmer I could specify something like:
> lmer(y ~ 1+ (x1|id1) + (x2|id2) + (x3|id3) + (id4|id4))
> but then four different variances would be estimated and also all the
> covariances.
> Would it be possible to estimate such a model in lme, where one can have
> all kinds of correlation structures? Thanks for any help!
> Ben.
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> R-sig-mixed-models op r-project.org mailing list
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