[R-sig-ME] lmer - individual slopes
bbo|ker @end|ng |rom gm@||@com
Thu Jan 6 02:23:21 CET 2022
On 1/5/22 8:10 PM, Cátia Ferreira De Oliveira via R-sig-mixed-models wrote:
> I hope you had a lovely winter break.
> I am interested in extracting the individual slopes for an experimental
> design where individuals are asked to do a task with two conditions -
> congruent and incongruent. These participants are also divided into groups
> - sleep or awake. Should I consider group allocation in the model? The
> individual slopes are extracted so that we can check whether the rate of
> learning is stable across sessions.
> Should we use:
> a) lmer(Response times ~ Congruency + Group + (Congruency| Participant)
> Or is it ok to just have it as:
> b) lmer(Response times ~ Congruency + (Congruency| Participant)
> [[alternative HTML version deleted]]
> R-sig-mixed-models using r-project.org mailing list
I don't see why you wouldn't use Congruency*Group as your fixed
effect? The more accurately you can estimate the population-level
effects in the fixed-effect part of the model, the less is left for the
random effects component to explain, which in turn makes it more likely
that the distribution will conform to the assumption of normality.
For example, suppose (retreating from a random-slopes model to a
random-intercept model) that you had a treatment, ttt, with two levels
and a large effect, with subjects allocated to one treatment or the
other (a nested design). If you fitted
lmer(response ~ 1 + (1|subject)
the distribution of subject-level effects would be strongly bimodal,
since it would have to account for the treatment effect as well, whereas
lmer(response ~ ttt + (1|subject)
would move the signal into the fixed effect and allow the random effect
to capture the smaller (and possibly Normal) subject-level effects.
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