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)

JimGrange

library(tidyverse)
library(rstan)
rstan_options(auto_write = TRUE)

#--- declare the data
# n_c = total number of people who drink caffeine
# n_nc = total number of people who do not drink caffeine
# p_c = observed proportion of people who drink caffeine that have a favourite mug
# p_nc = observed proportion of people who drink caffeine that DO NOT have a favourite mug
stan_data <- list(

JimGrange

# sample size
n <- 500

# simulate data for each predictor 
# (note predictor b is categorical)
predictor_a <- rnorm(n, 0, 1) 
predictor_b <- rbinom(n, 1, .5)

# population-level beta values
b_predictor_a <- -0.20