simulations with BayesFactors

sim_mean_BF.R

library(BayesFactor)
library(future.apply)
library(future.batchtools)

# for the simulations, I used our cluster that runs PBS Torque 
plan(batchtools_torque, resources = list(walltime = '01:00:00', memory = '2Gb')) # specify that the PBS Torque cluster is used and the resources we want

# create conditions combinations
conditions <- expand.grid(d = seq(0, 0.36, 0.04), N = seq(5, 65, 4))

# a function that gets BFs nrep times based on the conditions
get_bf <- function (curr_conditions, nrep = 10000){
  # in each replication, we generate a random normal sample with a mean d and SD = 1 and compute the BF using default settings for one-sample t-test
  bf <- replicate(nrep,
                  ttestBF(rnorm(curr_conditions[['N']], curr_conditions[['d']]))@bayesFactor$bf)
  data.frame(N = curr_conditions[['N']], d = curr_conditions[['d']], bf = bf)
}

# run the simulations and combine the results into data.table
res <- rbindlist(future_apply(conditions, 1, get_bf, future.seed = T, 
                              future.packages = c('data.table','BayesFactor')))

mean_bf <- res[,.(mean_log_bf = mean(bf), mean_bf = mean(exp(bf))), by = .(N,d)] 

color_cuts <-  c(0,.01,0.1,0.33,3,10,100,Inf)
ggplot(mean_bf, aes(x = d, y = N, fill = mean_bf))+geom_tile()+scale_fill_viridis_c(trans='log')+labs(fill = 'Mean logBF')
ggplot(mean_bf, aes(x = d, y = N, fill= cut2(mean_bf, color_cuts)))+geom_tile()+scale_fill_viridis_d()+labs(fill = 'Mean BF')
ggplot(mean_bf, aes(x = d, y = N, fill = mean_log_bf))+geom_tile()+scale_fill_viridis_c()+labs(fill = 'Mean logBF')
ggplot(mean_bf, aes(x = d, y = N, fill = cut2(mean_log_bf, cuts = log(color_cuts))))+geom_tile()+scale_fill_viridis_d()+labs(fill = 'Mean logBF')