## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----setup--------------------------------------------------------------------
# Load libraries
library(RColorBrewer)
library(ggplot2)
library(dplyr)
library(reshape2)
library(latex2exp)
library(lddmm)

theme_set(theme_bw(base_size = 14))
cols = brewer.pal(9, "Set1")

## ----eval = FALSE, results = 'hide', fig.show = 'hide', warning = FALSE, message = FALSE----
#  # Load the data
#  data('data')
#  
#  # Descriptive plots
#  plot_accuracy(data)
#  plot_RT(data)
#  
#  # Run the model
#  hypers = NULL
#  hypers$s_sigma_mu = hypers$s_sigma_b = 0.1
#  
#  # Change the number of iterations when running the model
#  # Here the number is small so that the code can run in less than 1 minute
#  Niter = 25
#  burnin = 15
#  thin = 1
#  samp_size = (Niter - burnin) / thin
#  
#  set.seed(123)
#  fit = LDDMM(data = data,
#               hypers = hypers,
#               Niter = Niter,
#               burnin = burnin,
#               thin = thin)
#  
#  # Plot the results
#  plot_post_pars(data, fit, par = 'drift')
#  plot_post_pars(data, fit, par = 'boundary')
#  
#  # Compute the WAIC to compare models
#  compute_WAIC(fit)