# Mixed ANOVA Analysis for DGEN Variables
# EOHI Experiment 2 Data Analysis - DGEN Level Analysis with TIME, DOMAIN, and INTERVAL factors
# Variables: DGEN_past_5_Pref, DGEN_past_5_Pers, DGEN_past_5_Val, DGEN_past_10_Pref, DGEN_past_10_Pers, DGEN_past_10_Val,
#           DGEN_fut_5_Pref, DGEN_fut_5_Pers, DGEN_fut_5_Val, DGEN_fut_10_Pref, DGEN_fut_10_Pers, DGEN_fut_10_Val

# Load required libraries
library(tidyverse)
library(ez)
library(car)
library(afex)         # For aov_ez (cleaner ANOVA output)
library(nortest)      # For normality tests
library(emmeans)      # For post-hoc comparisons
library(purrr)        # For map functions
library(effsize)      # For Cohen's d calculations
library(effectsize)   # For effect size calculations

# Global options to remove scientific notation
options(scipen = 999)

# Set contrasts to sum for mixed ANOVA (necessary for proper interpretation)
options(contrasts = c("contr.sum", "contr.poly"))

setwd("C:/Users/irina/Documents/DND/EOHI/eohi2")

# Read the data
data <- read.csv("eohi2.csv")

# Display basic information about the dataset
print(paste("Dataset dimensions:", paste(dim(data), collapse = " x")))
print(paste("Number of participants:", length(unique(data$pID))))

# Verify the specific variables we need
required_vars <- c("DGEN_past_5_Pref", "DGEN_past_5_Pers", "DGEN_past_5_Val", 
                   "DGEN_past_10_Pref", "DGEN_past_10_Pers", "DGEN_past_10_Val",
                   "DGEN_fut_5_Pref", "DGEN_fut_5_Pers", "DGEN_fut_5_Val", 
                   "DGEN_fut_10_Pref", "DGEN_fut_10_Pers", "DGEN_fut_10_Val")

missing_vars <- required_vars[!required_vars %in% colnames(data)]
if (length(missing_vars) > 0) {
  print(paste("Warning: Missing variables:", paste(missing_vars, collapse = ", ")))
} else {
  print("All required DGEN variables found!")
}

# Define variable mapping for the three within-subjects factors
variable_mapping <- data.frame(
  variable = required_vars,
  TIME = c(rep("Past", 6), rep("Future", 6)),
  DOMAIN = rep(c("Preferences", "Personality", "Values", "Preferences", "Personality", "Values"), 2),
  INTERVAL = rep(c("5", "5", "5", "10", "10", "10"), 2),
  stringsAsFactors = FALSE
)

# Variable mapping created
print("Variable mapping:")
print(variable_mapping)

# Efficient data pivoting using pivot_longer
long_data <- data %>%
  select(pID, ResponseId, temporal_DO, interval_DO, all_of(required_vars)) %>%
  pivot_longer(
    cols = all_of(required_vars),
    names_to = "variable",
    values_to = "DGEN_SCORE"
  ) %>%
  left_join(variable_mapping, by = "variable") %>%
  # Convert to factors with proper levels
  mutate(
    TIME = factor(TIME, levels = c("Past", "Future")),
    DOMAIN = factor(DOMAIN, levels = c("Preferences", "Personality", "Values")),
    INTERVAL = factor(INTERVAL, levels = c("5", "10")),
    pID = as.factor(pID),
    temporal_DO = as.factor(temporal_DO),
    interval_DO = as.factor(interval_DO)
  ) %>%
  # Select final columns and remove any rows with missing values
  select(pID, ResponseId, temporal_DO, interval_DO, TIME, DOMAIN, INTERVAL, DGEN_SCORE) %>%
  filter(!is.na(DGEN_SCORE))

print(paste("Long data dimensions:", paste(dim(long_data), collapse = " x")))
print(paste("Number of participants:", length(unique(long_data$pID))))
print("Factor levels:")
print(paste("TIME:", paste(levels(long_data$TIME), collapse = ", ")))
print(paste("DOMAIN:", paste(levels(long_data$DOMAIN), collapse = ", ")))
print(paste("INTERVAL:", paste(levels(long_data$INTERVAL), collapse = ", ")))
print(paste("temporal_DO:", paste(levels(long_data$temporal_DO), collapse = ", ")))
print(paste("interval_DO:", paste(levels(long_data$interval_DO), collapse = ", ")))

# =============================================================================
# DESCRIPTIVE STATISTICS
# =============================================================================

# Overall descriptive statistics by TIME, DOMAIN, and INTERVAL
desc_stats <- long_data %>%
  group_by(TIME, DOMAIN, INTERVAL) %>%
  summarise(
    n = n(),
    mean = round(mean(DGEN_SCORE, na.rm = TRUE), 5),
    variance = round(var(DGEN_SCORE, na.rm = TRUE), 5),
    sd = round(sd(DGEN_SCORE, na.rm = TRUE), 5),
    median = round(median(DGEN_SCORE, na.rm = TRUE), 5),
    q1 = round(quantile(DGEN_SCORE, 0.25, na.rm = TRUE), 5),
    q3 = round(quantile(DGEN_SCORE, 0.75, na.rm = TRUE), 5),
    min = round(min(DGEN_SCORE, na.rm = TRUE), 5),
    max = round(max(DGEN_SCORE, na.rm = TRUE), 5),
    .groups = 'drop'
  )

print("Descriptive statistics by TIME, DOMAIN, and INTERVAL:")
print(desc_stats)

# Descriptive statistics by between-subjects factors
desc_stats_by_between <- long_data %>%
  group_by(temporal_DO, interval_DO, TIME, DOMAIN, INTERVAL) %>%
  summarise(
    n = n(),
    mean = round(mean(DGEN_SCORE, na.rm = TRUE), 5),
    variance = round(var(DGEN_SCORE, na.rm = TRUE), 5),
    sd = round(sd(DGEN_SCORE, na.rm = TRUE), 5),
    .groups = 'drop'
  )

print("Descriptive statistics by between-subjects factors:")
print(desc_stats_by_between)

# Calculate mean differences for key comparisons
print("\n=== KEY MEAN DIFFERENCES ===")

# Past vs Future differences for each DOMAIN × INTERVAL combination
past_future_diffs <- long_data %>%
  group_by(DOMAIN, INTERVAL, pID) %>%
  summarise(
    past_score = DGEN_SCORE[TIME == "Past"],
    future_score = DGEN_SCORE[TIME == "Future"],
    difference = past_score - future_score,
    .groups = 'drop'
  ) %>%
  group_by(DOMAIN, INTERVAL) %>%
  summarise(
    n = n(),
    mean_diff = round(mean(difference, na.rm = TRUE), 5),
    sd_diff = round(sd(difference, na.rm = TRUE), 5),
    se_diff = round(sd(difference, na.rm = TRUE) / sqrt(n()), 5),
    .groups = 'drop'
  )

print("Past vs Future differences by DOMAIN × INTERVAL:")
print(past_future_diffs)

# 5 vs 10 interval differences for each TIME × DOMAIN combination
interval_diffs <- long_data %>%
  group_by(TIME, DOMAIN, pID) %>%
  summarise(
    interval_5_score = DGEN_SCORE[INTERVAL == "5"],
    interval_10_score = DGEN_SCORE[INTERVAL == "10"],
    difference = interval_5_score - interval_10_score,
    .groups = 'drop'
  ) %>%
  group_by(TIME, DOMAIN) %>%
  summarise(
    n = n(),
    mean_diff = round(mean(difference, na.rm = TRUE), 5),
    sd_diff = round(sd(difference, na.rm = TRUE), 5),
    se_diff = round(sd(difference, na.rm = TRUE) / sqrt(n()), 5),
    .groups = 'drop'
  )

print("\n5 vs 10 interval differences by TIME × DOMAIN:")
print(interval_diffs)

# =============================================================================
# ASSUMPTION TESTING
# =============================================================================

# Remove missing values for assumption testing
long_data_clean <- long_data[!is.na(long_data$DGEN_SCORE), ]
print(paste("Data after removing missing values:", paste(dim(long_data_clean), collapse = " x")))

# 1. Missing values check
missing_summary <- long_data %>%
  group_by(TIME, DOMAIN, INTERVAL) %>%
  summarise(
    n_total = n(),
    n_missing = sum(is.na(DGEN_SCORE)),
    pct_missing = round(100 * n_missing / n_total, 2),
    .groups = 'drop'
  )

print("Missing values by TIME, DOMAIN, and INTERVAL:")
print(missing_summary)

# 2. Outlier detection
outlier_summary <- long_data_clean %>%
  group_by(TIME, DOMAIN, INTERVAL) %>%
  summarise(
    n = n(),
    mean = mean(DGEN_SCORE),
    sd = sd(DGEN_SCORE),
    q1 = quantile(DGEN_SCORE, 0.25),
    q3 = quantile(DGEN_SCORE, 0.75),
    iqr = q3 - q1,
    lower_bound = q1 - 1.5 * iqr,
    upper_bound = q3 + 1.5 * iqr,
    n_outliers = sum(DGEN_SCORE < lower_bound | DGEN_SCORE > upper_bound),
    .groups = 'drop'
  )

print("Outlier summary (IQR method):")
print(outlier_summary)

# 3. Anderson-Darling normality test
normality_results <- long_data_clean %>%
  group_by(TIME, DOMAIN, INTERVAL) %>%
  summarise(
    n = n(),
    ad_statistic = ad.test(.data$DGEN_SCORE)$statistic,
    ad_p_value = ad.test(.data$DGEN_SCORE)$p.value,
    .groups = 'drop'
  )

print("Anderson-Darling normality test results:")
# Round only the numeric columns
normality_results_rounded <- normality_results %>%
  mutate(across(where(is.numeric), ~ round(.x, 5)))
print(normality_results_rounded)

# 4. Homogeneity of variance tests
# Test homogeneity across TIME within each DOMAIN × INTERVAL combination
homogeneity_time <- long_data_clean %>%
  group_by(DOMAIN, INTERVAL) %>%
  summarise(
    levene_F = leveneTest(DGEN_SCORE ~ TIME)$`F value`[1],
    levene_p = leveneTest(DGEN_SCORE ~ TIME)$`Pr(>F)`[1],
    .groups = 'drop'
  )

print("Homogeneity of variance across TIME within each DOMAIN × INTERVAL:")
print(homogeneity_time)

# Test homogeneity across DOMAIN within each TIME × INTERVAL combination
homogeneity_domain <- long_data_clean %>%
  group_by(TIME, INTERVAL) %>%
  summarise(
    levene_F = leveneTest(DGEN_SCORE ~ DOMAIN)$`F value`[1],
    levene_p = leveneTest(DGEN_SCORE ~ DOMAIN)$`Pr(>F)`[1],
    .groups = 'drop'
  )

print("Homogeneity of variance across DOMAIN within each TIME × INTERVAL:")
print(homogeneity_domain)

# Test homogeneity across INTERVAL within each TIME × DOMAIN combination
homogeneity_interval <- long_data_clean %>%
  group_by(TIME, DOMAIN) %>%
  summarise(
    levene_F = leveneTest(DGEN_SCORE ~ INTERVAL)$`F value`[1],
    levene_p = leveneTest(DGEN_SCORE ~ INTERVAL)$`Pr(>F)`[1],
    .groups = 'drop'
  )

print("Homogeneity of variance across INTERVAL within each TIME × DOMAIN:")
print(homogeneity_interval)

# =============================================================================
# HARTLEY'S F-MAX TEST WITH BOOTSTRAP CRITICAL VALUES
# =============================================================================

# More efficient bootstrap function for Hartley's F-max test
bootstrap_hartley_critical <- function(data, group_var, response_var, n_iter = 1000) {
  # Get unique groups and their sample sizes
  groups <- unique(data[[group_var]])
  
  # Calculate observed variances for each group
  observed_vars <- data %>%
    dplyr::group_by(!!rlang::sym(group_var)) %>%
    dplyr::summarise(var = var(!!rlang::sym(response_var), na.rm = TRUE), .groups = 'drop') %>%
    dplyr::pull(var)
  
  # Handle invalid variances
  if(any(observed_vars <= 0 | is.na(observed_vars))) {
    observed_vars[observed_vars <= 0 | is.na(observed_vars)] <- 1e-10
  }
  
  # Calculate observed F-max ratio
  observed_ratio <- max(observed_vars) / min(observed_vars)
  
  # Pre-allocate storage for bootstrap ratios
  bootstrap_ratios <- numeric(n_iter)
  
  # Get group data once
  group_data_list <- map(groups, ~ {
    group_data <- data[data[[group_var]] == .x, response_var]
    group_data[!is.na(group_data)]
  })
  
  # Bootstrap with pre-allocated storage
  for(i in 1:n_iter) {
    # Bootstrap sample from each group independently
    sample_vars <- map_dbl(group_data_list, ~ {
      bootstrap_sample <- sample(.x, size = length(.x), replace = TRUE)
      var(bootstrap_sample, na.rm = TRUE)
    })
    bootstrap_ratios[i] <- max(sample_vars) / min(sample_vars)
  }
  
  # Remove invalid ratios
  valid_ratios <- bootstrap_ratios[is.finite(bootstrap_ratios) & !is.na(bootstrap_ratios)]
  
  if(length(valid_ratios) == 0) {
    stop("No valid bootstrap ratios generated")
  }
  
  # Calculate critical value (95th percentile)
  critical_95 <- quantile(valid_ratios, 0.95, na.rm = TRUE)
  
  # Return only essential information
  return(list(
    observed_ratio = observed_ratio,
    critical_95 = critical_95,
    n_valid_iterations = length(valid_ratios)
  ))
}

# Hartley's F-max test across between-subjects factors within each within-subjects combination
print("\n=== HARTLEY'S F-MAX TEST RESULTS ===")
set.seed(123)  # For reproducibility

# Test across temporal_DO within each TIME × DOMAIN × INTERVAL combination
print("F-max test across temporal_DO within each TIME × DOMAIN × INTERVAL combination:")
hartley_temporal_results <- long_data_clean %>%
  group_by(TIME, DOMAIN, INTERVAL) %>%
  summarise(
    hartley_result = list(bootstrap_hartley_critical(pick(temporal_DO, DGEN_SCORE), "temporal_DO", "DGEN_SCORE")),
    .groups = 'drop'
  ) %>%
  mutate(
    observed_ratio = map_dbl(hartley_result, ~ .x$observed_ratio),
    critical_95 = map_dbl(hartley_result, ~ .x$critical_95),
    significant = observed_ratio > critical_95
  ) %>%
  select(TIME, DOMAIN, INTERVAL, observed_ratio, critical_95, significant)

print(hartley_temporal_results)

# Test across interval_DO within each TIME × DOMAIN × INTERVAL combination
print("F-max test across interval_DO within each TIME × DOMAIN × INTERVAL combination:")
hartley_interval_results <- long_data_clean %>%
  group_by(TIME, DOMAIN, INTERVAL) %>%
  summarise(
    hartley_result = list(bootstrap_hartley_critical(pick(interval_DO, DGEN_SCORE), "interval_DO", "DGEN_SCORE")),
    .groups = 'drop'
  ) %>%
  mutate(
    observed_ratio = map_dbl(hartley_result, ~ .x$observed_ratio),
    critical_95 = map_dbl(hartley_result, ~ .x$critical_95),
    significant = observed_ratio > critical_95
  ) %>%
  select(TIME, DOMAIN, INTERVAL, observed_ratio, critical_95, significant)

print(hartley_interval_results)