What is the best way in brms() to add group-level predictors to explain group-level parameters? In my case the group-level parameter is the intercept which varies across year groups, say.
I am following the Gelman and Hill route and simply adding in a group-level predictor outside of the group-level code block. For example, adding in a predictor to explain the intercept which varies between year groups, by creating a group-predictor -
meanCF - for the group-varying intercept. This predictor - by definition - only varies between year to year in my dataset:
y ~ X + meanCF + (1 | year)
reg_data <- data %>% group_by(year) %>% mutate(medianCF = median(cashflow_rf), meanCF = sum(cashflow)/sum(fixed_c_gross))
Is this the best approach?