[R-sig-ME] Meaning of Corr of random-effects with a cross-level interaction

[Thierry Onkelinx]

Dear Simon,

A perfect correlation between random effect parameters indicates a problem.
Note the failed convergence warning.
Standardising ses makes things even worse: it yields a singular fit error.

Removing the random slope of ses or the sector interaction solves the
problem. i.e. the model runs and yields sensible output.

Looking at the data, it seems like both math and ses have bounds. Ses
even seems to have some data above its upper bound.
The model assumes no bounds in the response variable. Maybe this is the
cause of the problem.

ggplot(hsb, aes(x = ses, y = math, colour = factor(sector))) +
  geom_point()

Best regards,

Thierry


ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx using inbo.be
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www.inbo.be

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Op do 24 sep. 2020 om 18:39 schreef Simon Harmel <sim.harmel using gmail.com>:

> Dear All,
>
> I had a quick question. I have a cross-level interaction in my model below
> (ses*sector). My cluster-level predictor "sector" is a binary variable
> (0=Public, 1=Private). My level-1 predictor is numeric.
>
> QUESTION:  The `Corr = 1` is indicating the correlation between
> intercepts and slopes across BOTH public & private sectors (like their
> average) OR something else?
>
> hsb <- read.csv('
> https://raw.githubusercontent.com/rnorouzian/e/master/hsb.csv')
>
> summary(lmer(math ~ ses*sector + (ses|sch.id), data = hsb))
>
>
> Random effects:
>  Groups   Name        Variance Std.Dev.     Corr
>  sch.id   (Intercept)  3.82107    1.9548
>           ses                0.07587     0.2754        1.00
>  Residual             36.78760 6.0653
>
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>
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