# 心理学データ解析応用/伴走サイトコード -----------------------------------------------------
#  Programmed by kosugitti
#  Licence ; Creative Commons BY-SA license (CC BY-SA) version 4.0
## Lesson 4. Alternatives for t-test. Cmdstanr Version

# 準備 ----------------------------------------------------------------------

rm(list = ls())
library(tidyverse)
library(cmdstanr)
library(bayesplot)
## MAP関数
map_estimation <- function(z) {
  density(z)$x[which.max(density(z)$y)]
}

## MCMCサンプルをデータフレームにする関数
MCMCtoDF <- function(fit) {
  fit$draws() %>%
    posterior::as_draws_df() %>%
    tibble::as_tibble() %>%
    dplyr::select(-lp__, -.draw, -.chain, -.iteration) %>%
    tibble::rowid_to_column("iter") %>%
    tidyr::pivot_longer(-iter) -> MCMCsample
  return(MCMCsample)
}

## MCMCデータフレームを要約する関数
MCMCsummary <- function(MCMCsample) {
  MCMCsample %>%
    dplyr::group_by(name) %>%
    dplyr::summarise(
      EAP = mean(value),
      MED = median(value),
      MAP = map_estimation(value),
      SD = sd(value),
      L95 = quantile(value, prob = 0.025),
      U95 = quantile(value, prob = 0.975),
      Rhat = posterior::rhat_basic(value)
    ) %>%
    mutate(across(where(is.numeric), ~ num(., digits = 3)))
}


# データなど -------------------------------------------------------------------

groupA <- c(30, 50, 70, 90, 60, 50, 70, 60)
groupB <- c(20, 40, 60, 40, 40, 50, 40, 30)

## t検定

t.test(groupA, groupB, var.equal = TRUE)


# Modeling ----------------------------------------------------------------
model <- cmdstanr::cmdstan_model("cmdstan/ttest01.stan")
model$check_syntax()
dataSet <- list(X1 = groupA, X2 = groupB, N1 = 8, N2 = 8)

fit <- model$sample(
  data = dataSet,
  chains = 4,
  parallel_chains = 4,
  iter_warmup = 1000,
  iter_sampling = 5000
)

## 結果
fit %>%
  MCMCtoDF() %>%
  MCMCsummary()

## 可視化
fit$draws() %>%
  bayesplot::mcmc_areas(
    pars = c("mu1", "mu2"),
    prob = 0.5, # 50% intervals
    prob_outer = 0.95, # 99%
    point_est = "mean"
  )

# Welchの補正 ----------------------------------------------------------------

t.test(groupA, groupB, var.equal = FALSE)


# 微妙な差のデータ ----------------------------------------------------------------

groupA <- c(30, 50, 70, 90, 60, 50, 70, 60)
groupB <- c(30, 45, 60, 40, 60, 50, 40, 30)

## t検定

t.test(groupA, groupB, var.equal = FALSE)

## 同じモデルでこのデータで再推定
dataSet <- list(X1 = groupA, X2 = groupB, N1 = 8, N2 = 8)
fit <- model$sample(
  data = dataSet,
  chains = 4,
  parallel_chains = 4,
  iter_warmup = 1000,
  iter_sampling = 5000
)

# サンプリングから計算 --------------------------------------------------------------

## MCMCサンプルをデータフレームにする
fit$draws() %>%
  posterior::as_draws_df() %>%
  tibble::as_tibble() %>%
  dplyr::select(-.draw, -.chain, -.iteration) -> MCMCsample

MCMCsample %>%
  mutate(diff = mu1 - mu2) %>%
  mutate(FLG = if_else(diff > 1, 1, 0)) %>%
  summary()

# Generated quantities ----------------------------------------------------

model <- cmdstanr::cmdstan_model("cmdstan/ttest03.stan")
model$check_syntax()
groupA <- c(30, 50, 70, 90, 60, 50, 70, 60)
groupB <- c(30, 45, 60, 40, 60, 50, 40, 30)

## 生成量を計算するモデルを実行
dataSet <- list(X1 = groupA, X2 = groupB, N1 = 8, N2 = 8)

fit <- model$sample(
  data = dataSet,
  chains = 4,
  parallel_chains = 4,
  iter_warmup = 1000,
  iter_sampling = 5000
)

fit %>%
  MCMCtoDF() %>%
  MCMCsummary()


# 課題 ----------------------------------------------------------------------
potatoA <- c(8.4, 11.3, 8.1, 11.2, 5.8, 6.3, 7.1, 10.9, 7.1, 6.5, 5.0, 3.0, 7.2, 6.5, 6.4, 6.4, 9.3, 8.3)
potatoB <- c(6.7, 7.2, 4.2, 11.0, 7.5, 8.9, 7.0, 8.0, 7.2, 4.2, 6.0, 9.0, 8.6, 9.0, 5.0)