[R-sig-ME] Why || doesn't zero out the correlations in lmer

[Phillip Alday]

On 10/8/21 17:07, Simon Harmel wrote:
> Thank you, this is extremely helpful to know. You mentioned it's well
> documented, any possible links to share?

The lme4 documentation, e.g.

https://www.rdocumentation.org/packages/lme4/versions/1.1-27.1/topics/lmer

> (Because of the way it is implemented, the ||||-syntax /works only for
> design matrices containing numeric (continuous) predictors/; to fit
> models with independent categorical effects, see |dummy
> <https://www.rdocumentation.org/link/dummy?package=lme4&version=1.1-27.1>|
> or the |lmer_alt| function from the afex package.)

> Also, does nlme::lme() behave in the same manner in this regard?

I am unaware of nlme supporting the double-bar syntax at all, but
specifing the correlation structure to be diagonal (pdDiagonal? it's
been a while) will force all correlations to zero.


>
> On Fri, Oct 8, 2021 at 4:46 PM Phillip Alday <me using phillipalday.com> wrote:
>> This is a well-documented issue: || doesn't zero correlations between a
>> categorical variable's levels. As far as I know, there are
>> software-development/technical reasons for this, not statistical ones.
>>
>> The afex package has an implementation that zeroes everything out.
>>
>> On 8/10/21 4:32 pm, Simon Harmel wrote:
>>> Dear Colleagues,
>>>
>>> I have a 'factor' predictor called 'type' (with 4 levels). In the
>>> random part, I have used `||` so the levels of 'type' can't correlate
>>> with each other.
>>>
>>> But I wonder why still correlations are reported in the output?
>>> Thanks, Simon
>>>
>>> lmer(y~type + (type || ID), data = data)
>>>
>>> Random effects:
>>>  Groups   Name  Std.Dev. Corr
>>>  ID     type0 0.4276
>>>           type1 0.7012   0.81
>>>           type2 0.7115   0.72 0.97
>>>           type3 0.7655   0.83 1.00 0.98
>>>
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