I have fitted a hierarchical regression model with brms.
My data structure is: ~380 human participants, each of which provides ~600 measurements (one of two button presses). I have different factors (e.g. ‘Cost’ or ‘OfferValue’) to predict those choices. I have z-scored the regressors across the data from all participants (rather than scored within single participants)
Here is an example of the model (I’m new to brms/ lme4 formulas…)
fit <- brm(formula = ForageChoice ~ 1+OfferValue+Cost+(1+OfferValue+Cost | ID), data = df, family = 'bernoulli', warmup = 1000, iter = 2000, chains = 4,cores=4)
The model fits (all Rhat<1.1 - I have not yet checked other measures)
But looking at the correlations, the regression weights are very correlated (some > 0.9). The regressors that I put in are not correlated. I think this might be because some participants basically don’t do the task well and then don’t take any of the factors that should affect their choices into account. So in a way, each person should have a factor that says how well they do the task (their own ‘noisiness’), independent from how much (relatively) they use each factor.
I don’t know whether there is anyway to code this in a regression (in brms)?