Livecoding of the first question

Q1P1

pacman::p_load(tidyverse, brms)

# Prepare the data

d <- Ass3
#summary(d)

d$Diagnosis <- plyr::revalue(as.character(d$Diagnosis), 
                             c("0"="Controls", "1"="Schizophrenia"))

d <- d %>%
  mutate(
    ID = as.factor(ID),
    Diagnosis = as.factor(Diagnosis)
  )

# Define the formula

AltercentricDiagnosis_f0 <- bf(
  AltercentricIntrusion ~ 1 + Diagnosis
)

AltercentricDiagnosis_f <- bf(
  AltercentricIntrusion ~ 0 + Diagnosis
)

# Design the priors

get_prior(AltercentricDiagnosis_f0, family = gaussian, d)
get_prior(AltercentricDiagnosis_f, family = gaussian, d)

priorDiagnosis <- c(
  prior(normal(4, 1), class = b),
  prior(normal(1, 2), class = sigma)
) 

# Test the priors

AltercentricDiagnosis_PriorCheck_m <- brm(
  formula = AltercentricDiagnosis_f,
  data = d,
  family = gaussian,
  prior = priorDiagnosis,
  sample_prior = "only"
)

pp_check(AltercentricDiagnosis_PriorCheck_m, nsamples = 100)

## Fitting the model
AltercentricDiagnosis_m <- brm(
  formula = AltercentricDiagnosis_f,
  data = d,
  family = gaussian,
  prior = priorDiagnosis,
  sample_prior = T
)

# Posterior predictive check
pp_check(AltercentricDiagnosis_m, nsamples = 100)

## Check the model for warnings
AltercentricDiagnosis_m

# Hypothesis testing + updating check
plot(hypothesis(AltercentricDiagnosis_m,
           "DiagnosisSchizophrenia > DiagnosisControls"))

hypothesis(AltercentricDiagnosis_m,
           "DiagnosisSchizophrenia > DiagnosisControls")

conditional_effects(AltercentricDiagnosis_m)

plot(conditional_effects(AltercentricDiagnosis_m), points=T)