[R-sig-ME] random intercept or nested random?

[Thierry Onkelinx]

Dear Julia,

I wrote a blogpost on this: https://www.muscardinus.be/2017/08/fixed-and-random/

Best regards,

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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2018-02-17 11:54 GMT+01:00 Julia Chacon Labella <juliachacon at gmail.com>:
> Hello to everybody,
>
> I have read that in some cases the same variable could be consider either a
> random or a fixed effect at the same time. But do not have any example for
> this. Could someone explain me when is this the case? When there is a
> justification to considered a variable in fixed effect term of the model
> and also in the random part? I am quite interested on that.
>
> I am thinking in the next example:
>
> Imagine we are interested in knowing if the exam marks of pupils depends on
> their Sex. So, you choose two different schools to check for this, but
> School 1 is only for boys and School 2 is only for girls. Within each
> school you sample many classes.
>
> So, we would have ExamMarks as response variable.
>
> SchoolSex as explanatory variable (in this given example would be the same
> School or Sex), with two levels SchoolBoys and SchoolGirls
>
> Class as explanatory variable, with many levels (8 per school), coded as
> Class1, Class 2....up to Class16.
>
> The model would be (lme4):
>
> lmer(exammarks ~ Schoolsex + (1|Class), data=data)
>
> So, we treat class as a random factor.
>
> Should we consider in this case, the random part of the model as a nested
> random and specify  it as (1|Class|Schoolsex) ?
>
> In that case the model would be:
> lmer(ExamMarks ~ Schoolsex + (1|Class|Schoolsex), data=data)
>
> Thank you very much,
> Julia
>
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>
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