JimGrange

library(tidyverse)

# Set the effect size
eff_size <- 0.2

# Function to simulate data, run regression, and check if individual predictors are significant
simulate_and_regress <- function(n) {
  data <- tibble(
    a = rnorm(n), 
    b = rnorm(n),

JimGrange

library(tidyverse)
library(faux)
library(lme4)
library(afex)
options(dplyr.summarise.inform = FALSE)
set.seed(123)


sim_data <- function(n_subj, n_trials){
  

JimGrange

library(tidyverse)
library(flankr)

# get example data from flankr
# ONLY SHOWING FOR CONGRUENT DATA.
d <- flankr::exampleData %>% 
  filter(congruency == "congruent")

# how many subjects?
n_subjects <- length(unique(d$subject))

JimGrange

# load packages -----------------------------------------------------------
library(tidyverse)
library(brms)
library(tidybayes)
library(bayesplot)
library(emmeans)
library(bayestestR)

JimGrange

library(brms)
library(tidyverse)

set.seed(123)  

# generate some subject-averaged data
n_subjects <- 100

data <- tibble(
  id = 1:n_subjects, 

JimGrange

library(tidyverse)
library(faux)
library(lme4)
library(afex)
options(dplyr.summarise.inform = FALSE)
set.seed(123)


sim_data <- function(n_subj, n_trials){
  

JimGrange

library(tidyverse)
library(BayesFactor)

# set seed for reproducibility
set.seed(234)

# define population-level parameters
p_text_yes <- 0.5
p_video_yes <- 0.5

JimGrange

library(tidyverse)
library(Superpower)

# set means & standard deviations for idealised data
# (note experiment 1 data comes from the pilot experiment)
exp_1_means <- c(1334, 1594, 1588, 1725)
exp_1_sds <- c(308, 295, 338, 343)
exp_2_means <- c(1030, 1080, 1120, 1150)
exp_2_sds <- c(200, 200, 200, 200)

JimGrange

# gaussian kernel function
gaussian_kernel <- function(u){
  (1 / sqrt(2 * pi)) * exp(-0.5 * u ^ 2)
}


# kernel density estimate function
kde <- function(n, data, x_limit, y_limit, h_x, h_y){
  
  x <- seq(from = x_limit[1], 

JimGrange

mod_1 <- brm(value ~ questionnaire * condition, 
             data = idealised_data,
             seed = 42,
             cores = 4)

mod_2 <- brm(value ~ questionnaire + condition, 
             data = idealised_data,
             seed = 42,
             cores = 4)