Testing custom contrasts with suppressed intercept in brms::hypothesis()

Interfaces brms
,
1

Hi there,

I have 3 questions about testing specific contrasts with sum-zero coding style and suppressing the intercept and I appreciate your comments. I am planning to do a pairwise comparison between levels of a factor with 4 levels. Here I load and prepare the data and then set the contrast to sum-zero:

# load libraries
library(brms)
library(tidyverse)


# load data
library(afex)
data(sk2011.1)

data <- sk2011.1 %>%
  select(id, instruction, inference, response) %>%
  as_tibble()


# set the contrast to sum-zero
options(contrasts = c('contr.sum', 'contr.poly'))
contrasts(data$inference) # 4 levels
contrasts(data$instruction) # 2 levels
  1. With a regular formula, I am not able to sample from intercept and I cannot use Intercept (which is the grand mean) in the brms::hypothesis() to recover the fourth level of my predictor. Thus, I suppress the intercept. Is the intercept still my grand mean considering my deviation coding? For example, is this equation still true? inference4 = inference1 + inference2 + inference3 
get_prior(bf(response ~ 1 + inference), 
          data= data)
# prior     class       coef group resp dpar nlpar bound       source
# (flat)         b                                             default
# (flat)         b inference1                             (vectorized)
# (flat)         b inference2                             (vectorized)
# (flat)         b inference3                             (vectorized)
# student_t        Intercept                               default

get_prior(bf(response ~ 0 + Intercept + inference), 
          data= data)
# prior class       coef group resp dpar nlpar bound       source
# (flat)     b                                             default
# (flat)     b inference1                             (vectorized)
# (flat)     b inference2                             (vectorized)
# (flat)     b inference3                             (vectorized)
# (flat)     b  Intercept                             (vectorized)
  1. Is 0 + Intercept + inference different from 0 + inference? With the second formula, I have direct estimates for all 4 levels of the predictor.
get_prior(bf(response ~ 0 + inference), 
          data= data)

# prior class        coef group resp dpar nlpar bound       source
# (flat)     b                                              default
# (flat)     b inferenceAC                             (vectorized)
# (flat)     b inferenceDA                             (vectorized)
# (flat)     b inferenceMP                             (vectorized)
# (flat)     b inferenceMT                             (vectorized)
  1. Since 0 + inference is easier to deal with, if I decide to use this formula, what happens when I add another predictor? Here I added a 2-level predictor but I only have the estimate for one of its levels. How can I recover its second level considering that there is no intercept here?
get_prior(bf(response ~ 0 + inference + instruction), 
          data= data)
# prior class         coef group resp dpar nlpar bound       source
# (flat)     b                                               default
# (flat)     b  inferenceAC                             (vectorized)
# (flat)     b  inferenceDA                             (vectorized)
# (flat)     b  inferenceMP                             (vectorized)
# (flat)     b  inferenceMT                             (vectorized)
# (flat)     b instruction1                             (vectorized)

I am aware that it is easy to recover the draws for all levels using tidybayes, emmeans, and other tools. I am interested to calculate BF for different contrasts using the hypothesis() function.

Thank you.