Fitting a model using glmmTMB

Question

Let's say I've got 3 scaled quantitative features, and 1 target categorical feature. Each quantitative feature is independent from each other. If I use glmmTMB to try and fit a model for this I could do something like this:

myModel<-glmmTMB(TargetFeature~FeatureA + FeatureB + FeatureC)

But if these features are from a collection of different experimental participants, recorded during different experiments should they be included also as random variables in the model definition?

something like :

myModel2<-glmmTMB(TargetFeature~FeatureA + FeatureB + FeatureC 
              + (1 | Participant) + (1 | ExperimentGroup/Experiment))

The data would look like this:

data[1:5,c('Participant','ExperimentGroup', 'Experiment', 'FeatureA', 'FeatureB','FeatureC')]
  Participant ExperimentGroup Experiment FeatureA     FeatureB        FeatureC
1      AC-001             E01      E01-A -0.5940626 -0.10808170       0.5198040
2      AC-001             E01      E01-A -0.5940626 -0.18172495       0.5198040
3      AC-001             E01      E01-A -0.5940626 -0.18172495       0.5198040
4      AC-001             E01      E01-A -0.5940626  0.21481427       0.5099361
5      AC-001             E01      E01-A -0.5940626  0.09152672       0.5090794

I'm having trouble understanding what would be necessary to model this, taking into account whether or not I'd need to use * or + for the second set of feature terms. (or even if I'm writing the model correctly at all) Any advice would be greatly appreciated!

Answer 1

Let's assume a simpler setting, where the end part of your model would look like (1|Participant) + (1|Experiment). That would be consistent with the fact that Participant and Experiment are fully/partially crossed so that every participant participated in every experiment/some of the experiments.

If the end part of your model would look like (1|Experiment/Participant), that would be consistent with the fact that you had multiple experiments, each with different participants.

In either case, the experiments you conducted would represent a (representative) subset of all the possible experiments you could have conducted and the subjects included in each experiment would represent a (representative) subset of all the subjects you could have included in the experiment.

How you write the model therefore has to reflect whether (i) Participant and Experiment are crossed (either fully or partially) or (ii) Participant is nested in Experiment.

The model you end up writing will help you learn something about a typical subject in a typical experiment.