JimGrange

#------------------------------------------------------------------------------
# generate data from a multivariate normal distribution with known 
# correlations between values
mvrnorm <- function(n, nValues, meanValues, sdValues, corMatrix) {
  Z <- matrix(rnorm(n * nValues), nValues, n)
  t(meanValues + sdValues * t(chol(corMatrix)) %*% Z)
}
#------------------------------------------------------------------------------

JimGrange

### plots of prior robustness check

# gif created from PNG plots using http://gifmaker.me/

# clear R's memory
rm(list = ls())

# load the Bayes factor package
library(BayesFactor)

JimGrange

library(ggplot2)
library(emoGG)

ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
  geom_emoji(emoji = "2744") + add_emoji(emoji = "26c4") 

JimGrange

#------------------------------------------------------------------------------
### set up

# clear workspace
rm(list = ls())

# set working directory
setwd("D:/Work/Blog_YouTube code/Blog/Olympic Medals")

# load relevant packages

JimGrange

setwd("C:/Users/psa33/Dropbox/Work/Blog/gifs/increasingN")

binWidth <- 0.005

# define parameter space
x <- seq(from = binWidth / 2, to = 1 - binWidth / 2, by = binWidth)

# what rate of fairness is the coin?
rate <- 0.5

JimGrange

#------------------------------------------------------------------------------
rm(list = ls())

set.seed(50)

# function for generating random draws from multivariate distribution
# n = number of draws; p = number of variables
# u = mean of each variable; s = SD of each variable
# corMat = correlation matrix
mvrnorm <- function(n, p, u, s, corMat) {

JimGrange

#------------------------------------------------------------------------------
### initial set up
rm(list = ls())

# set working directory
setwd("D:/Work/Other/Blog_YouTube code/Blog/Bayesian Partial Correlation")

# load libraries
library(ppcor)
library(Hmisc)

JimGrange

library(metafor)
library(dplyr)

# sample size per study
n <- 10

# population effect size
true_d <- 0.2

# generate studies

JimGrange

library(rtdists)

set.seed(42)

# declare correct & error drift rate
correct_drift <- 2.4
error_drift <- 1 - correct_drift

# generate RTs with the diffusion model
rt1 <- rLBA(500, A = 0.5, b = 1, t0 = 0.5, 

JimGrange

# https://nceas.github.io/oss-lessons/parallel-computing-in-r/parallel-computing-in-r.html

library(parallel)
library(foreach)
library(doParallel)
library(snow)
set.seed(42)

n_subs <- 100000
n_trials <- 5000