[R-sig-ME] lmer code for multiple random slopes

[Peter R Law]

Many thanks Wolfgang.


Peter

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‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐
On Thursday, March 4, 2021 5:45 AM, Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:

> Hi Peter,
>
> In:
>
> response ~ x + (1|G2/G1) + (x|G2)?
>
> the first () term adds random intercepts for each level of G2 and random intercepts for each level of G1 within G2. The second () term actually stands for (1+x|G2), so it again adds random intercepts for each level of G2, and random slopes for x for each level of G2 (and allows these random intercepts and slopes to be correlated). So, you are in essence adding random intercepts for G2 twice; probably not what you intended. Maybe you want:
>
> response ~ x + (1|G2/G1) + (0+x|G2)?
>
> but this implies that the random intercepts and slopes at the G2 level are uncorrelated. Or you could use:
>
> response ~ x + (1|G1:G2) + (x|G2)?
>
> which gives you correlated random intercepts and slopes for G2 and also adds random intercepts for the various G1-G2 level combinations (i.e., random intercepts for each level of G1 within G2).
>
> Best,
> Wolfgang
>
> > -----Original Message-----
> > From: Peter R Law [mailto:prldb using protonmail.com]
> > Sent: Thursday, 04 March, 2021 0:08
> > To: Viechtbauer, Wolfgang (SP); r-sig-mixed-models using r-project.org
> > Subject: RE: [R-sig-ME] lmer code for multiple random slopes
> > Many thanks Wolfgang! That does clear up my confusion. That interpretation of
> > 'random effects' didn't occur to me.
> > Thanks also for pointing out that the differences in the estimates provided by lme
> > and lmer occur because they handle singular models differently and won't occur for
> > non-singular examples.
> > When I have collected enough data, the model I am actually interested in will have
> > two nested random factors, G1 within G2, and I only want random slopes (say for a
> > covariate x) with respect to G2, but random intercepts for both factors. Am I
> > right in thinking the code will take the form
> > response ~ x + (1|G2/G1) + (x|G2)?
> > Much appreciated,
> > Peter
> > Peter R Law
> > Research Associate
> > Center for African Conservation Ecology
> > Nelson Mandela University
> > South Africa
> > ‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐
> > On Wednesday, March 3, 2021 7:43 AM, Viechtbauer, Wolfgang (SP)
> > wolfgang.viechtbauer using maastrichtuniversity.nl wrote:
> >
> > > Dear Peter, Dear All,
> > > The error doesn't say 78 variance components, it says 78 random effects. There
> > > are 26 groups in this dataset and you are estimating random intercepts and two
> > > sets of random slopes. Hence, there are 3*26 = 78 random effects that are in this
> > > model. But the dataset has only 74 rows. This is a check built into lmer() to
> > > avoid fitting overly complex models for a given dataset. You can disable this
> > > check with (also, use REML=FALSE, not "False"):
> > > M2 <- lmer(Response ~ P + A + (P + A | Group), REML=FALSE, data=Trial,
> > > control=lmerControl(check.nobs.vs.nRE="ignore"))
> > > Then it will run and this is the same model as fitted with:
> > > M2n <- lme( Response ~ P + A, random = ~ P + A | Group, data=Trial, method="ML")
> > > The results differ somewhat though, because both packages handle such ill-
> > > defined problems in different ways.
> > > Best,
> > > Wolfgang