I have data of an experiment where subjects (
ID) have to perform 10
trials of a go/no-go task. I want to study the influence of the
decision (go/no-go) on some physiological measure (e.g.
pupil.diameter) during trial. I want to study that alongside other variables, some dependant on the subject (e.g.
age), and other dependant on the trial (e.g.
My data looks like this:
ID trial decision pupil.diameter age difficulty 0 1 1 go 3.2 47 easy 1 1 2 go 2.4 47 hard 2 1 3 no-go 5.6 47 hard 3 1 4 go 5.1 47 hard . . . . . . . 9 1 10 go 3.4 47 easy 10 2 1 no-go 3.6 29 easy 11 2 2 go 4.2 29 hard . . . . . . .
I would originally perform (under
R) linear model/anova analyses like this:
pupil.diameter ~ age + difficulty + decision
with possibly interaction effects.
The problem is that some of my variables are dependant either on the
ID or the
trial (and thus their values repeated across multiple lines).
My search led me to consider a mixed-effects model and to use
lmer, but I am still confused on how to do it correctly.
Is a mixed-effects model suitable here?
How to specify the random effects correctly?
Should I declare my model like this?
pupil.diameter ~ age + difficulty + decision + (1|ID)
Or like this???
pupil.diameter ~ age + difficulty + decision + (1|trial)
pupil.diameter ~ age + difficulty + decision + (1|ID) + (1|trial)
pupil.diameter ~ age + difficulty + decision + (age|ID) + (difficulty|trial)
NB: To generalise the example:
subject-dependant variables could also be categorical (e.g.
trial-dependant variables could also be continuous (e.g.
difficultyon a scale of 1 to 10);
variable to explain could also be categorical (e.g.
blinked.during.trial?) and the model would be adapted to a logistic regression;