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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)) |
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# load packages ----------------------------------------------------------- | |
library(tidyverse) | |
library(brms) | |
library(tidybayes) | |
library(bayesplot) | |
library(emmeans) | |
library(bayestestR) | |
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library(brms) | |
library(tidyverse) | |
set.seed(123) | |
# generate some subject-averaged data | |
n_subjects <- 100 | |
data <- tibble( | |
id = 1:n_subjects, |
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library(tidyverse) | |
library(faux) | |
library(lme4) | |
library(afex) | |
options(dplyr.summarise.inform = FALSE) | |
set.seed(123) | |
sim_data <- function(n_subj, n_trials){ | |
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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 |
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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) |
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# 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], |
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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) |
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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( |
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# 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 |
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