utilities for Harada and Kagaya, https://doi.org/10.1101/330787

utility.R

library(tidyverse)
library(forcats)
library(rstan)
library(shinystan)

waic <- function(log_likelihood) {
  # from https://gist.github.com/MatsuuraKentaro/3f6ae5863e700f5039c19e36a9bdf646
  training_error <- - mean(log(colMeans(exp(log_likelihood))))
  functional_variance_div_N <- mean(colMeans(log_likelihood^2) - colMeans(log_likelihood)^2)
  waic <- training_error + functional_variance_div_N
  return(waic)
}

df_quantile_mcmc <- function(x, y_mcmc,
                             probs=c(2.5, 25, 50, 75, 97.5)/100){
    qua <- apply(y_mcmc, 2, quantile, probs=probs)
    d_pred <- data.frame(X=x, t(qua))
    colnames(d_pred) <- c('X', paste0('p', probs*100))
    return(d_pred)
}


check_rhat <- function(fit){
  all(summary(fit)$summary[,"Rhat"] <= 1.10, na.rm=T)
}


add_chosen_col_days <- function(d){
  d <- d %>% arrange(id, days_to_choice)
  x <- paste(d$id, d$days_to_choice, sep="") %>% factor() %>% fct_inorder()
  xx <- table(x)
  chosen_ind <- names(xx)
  chosen_num <- c()
  for (i in xx){
    chosen_num <- c(chosen_num, 1:i)
  }
  d$chosen_num <- chosen_num
  d
}

add_chosen_col_choice <- function(d){
  d <- d %>% arrange(id, choice)
  x <- paste(d$id, d$choice, sep="") %>% factor() %>% fct_inorder()
  xx <- table(x)
  chosen_ind <- names(xx)
  chosen_num <- c()
  for (i in xx){
    chosen_num <- c(chosen_num, 1:i)
  }
  d$chosen_num <- chosen_num
  d
}

add_chosen_col_days <- function(d){
  d <- d %>% arrange(id, days_to_choice)
  x <- paste(d$id, d$days_to_choice, sep="") %>% factor() %>% fct_inorder()
  xx <- table(x)
  chosen_ind <- names(xx)
  chosen_num <- c()
  for (i in xx){
    chosen_num <- c(chosen_num, 1:i)
  }
  d$chosen_num <- chosen_num
  d
}