Thank you Thierry! Would you please clarify one of your sentences: "both
math and ses have bounds. Ses even seems to have some data above its
upper bound."
Specifically, would please clarify what you mean by "ses has some data
above its upper bound"?(you mean the couple of outlying ses values in red
as shown in your plot?)
Of course, real world data always have some lower and upper bound based on
the instrument (e.g., a math test) used to collect the data. But my
question is what are the relative required lower and upper bounds on
NUMERIC OUTCOME & NUMERIC PREDICTORS so we don't face convergence issues
of the type I have shown in my question?
Thank you,
Simon
On Fri, Sep 25, 2020 at 3:03 AM Thierry Onkelinx <thierry.onkelinx at inbo.be>
wrote:
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 at inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be
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Op do 24 sep. 2020 om 18:39 schreef Simon Harmel <sim.harmel at 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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