# mixed anova not working 
# 12/09/2025
# add sum contrasts 

# Mixed ANOVA Analysis for Domain Means
# EOHI Experiment Data Analysis - Domain Level Analysis
# Variables: NPast_mean_pref, NPast_mean_pers, NPast_mean_val, NPast_mean_life
#           NFut_mean_pref, NFut_mean_pers, NFut_mean_val, NFut_mean_life

# Load required libraries
library(tidyverse)
library(ez)
library(car)
library(nortest)      # For normality tests
library(ggplot2)      # For plotting
library(emmeans)      # For post-hoc comparisons
library(purrr)        # For map functions

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

setwd("C:/Users/irina/Documents/DND/EOHI/eohi1")

# Read the data
data <- read.csv("exp1.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("NPast_mean_pref", "NPast_mean_pers", "NPast_mean_val", "NPast_mean_life",
                   "NFut_mean_pref", "NFut_mean_pers", "NFut_mean_val", "NFut_mean_life")

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 domain mean variables found!")
}

# Define domain mapping
domain_mapping <- data.frame(
  variable = c("NPast_mean_pref", "NPast_mean_pers", "NPast_mean_val", "NPast_mean_life",
               "NFut_mean_pref", "NFut_mean_pers", "NFut_mean_val", "NFut_mean_life"),
  time = c(rep("Past", 4), rep("Future", 4)),
  domain = rep(c("Preferences", "Personality", "Values", "Life"), 2),
  stringsAsFactors = FALSE
)

# Domain mapping created

# More efficient data pivoting using tidyr
pivot_domain_means <- function(data, domain_mapping) {
  # Use pivot_longer for efficient reshaping
  long_data <- data %>%
    select(pID, ResponseId, TEMPORAL_DO, all_of(domain_mapping$variable)) %>%
    pivot_longer(
      cols = all_of(domain_mapping$variable),
      names_to = "variable",
      values_to = "MEAN_DIFFERENCE"
    ) %>%
    left_join(domain_mapping, by = "variable") %>%
    select(-variable) %>%
    # Convert to factors with proper levels
    mutate(
      TIME = factor(time, levels = c("Past", "Future")),
      DOMAIN = factor(domain, levels = c("Preferences", "Personality", "Values", "Life")),
      pID = as.factor(pID),
      TEMPORAL_DO = as.factor(TEMPORAL_DO)
    ) %>%
    select(pID, ResponseId, TEMPORAL_DO, TIME, DOMAIN, MEAN_DIFFERENCE)
  
  return(long_data)
}

# Pivot data to long format
tryCatch({
  long_data <- pivot_domain_means(data, domain_mapping)
}, error = function(e) {
  print(paste("Error in data pivoting:", e$message))
  stop("Cannot proceed without proper data structure")
})

print(paste("Long data dimensions:", paste(dim(long_data), collapse = " x")))
print(paste("Number of participants:", length(unique(long_data$pID))))

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

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

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

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

print("Descriptive statistics by TEMPORAL_DO, TIME, and DOMAIN:")
print(desc_stats_by_temporal)


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

# Remove missing values for assumption testing
long_data_clean <- long_data[!is.na(long_data$MEAN_DIFFERENCE), ]
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) %>%
  summarise(
    n_total = n(),
    n_missing = sum(is.na(MEAN_DIFFERENCE)),
    pct_missing = round(100 * n_missing / n_total, 2),
    .groups = 'drop'
  )

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

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

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

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

print("Anderson-Darling normality test results:")
print(round(normality_results, 5))

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

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

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

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

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

# Function to calculate Hartley's F-max ratio
calculate_hartley_ratio <- function(variances) {
  max(variances, na.rm = TRUE) / min(variances, na.rm = TRUE)
}

# =============================================================================
# CALCULATE OBSERVED F-MAX RATIOS FOR MIXED ANOVA
# =============================================================================

# For mixed ANOVA: Test homogeneity across BETWEEN-SUBJECTS factor (TEMPORAL_DO)
# within each combination of within-subjects factors (TIME × DOMAIN)

# First, let's check what values TEMPORAL_DO actually has
print("=== CHECKING TEMPORAL_DO VALUES ===")
print("Unique TEMPORAL_DO values:")
print(unique(long_data_clean$TEMPORAL_DO))
print("TEMPORAL_DO value counts:")
print(table(long_data_clean$TEMPORAL_DO))

print("\n=== OBSERVED F-MAX RATIOS: TEMPORAL_DO within each TIME × DOMAIN combination ===")

observed_temporal_ratios <- long_data_clean %>%
  group_by(TIME, DOMAIN) %>%
  summarise(
    # Calculate variances for each TEMPORAL_DO level within this TIME × DOMAIN combination
    past_var = var(MEAN_DIFFERENCE[TEMPORAL_DO == "01PAST"], na.rm = TRUE),
    fut_var = var(MEAN_DIFFERENCE[TEMPORAL_DO == "02FUT"], na.rm = TRUE),
    # Calculate F-max ratio
    f_max_ratio = max(past_var, fut_var) / min(past_var, fut_var),
    .groups = 'drop'
  ) %>%
  select(TIME, DOMAIN, past_var, fut_var, f_max_ratio)

print(observed_temporal_ratios)

# 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 %>%
    group_by(!!sym(group_var)) %>%
    summarise(var = var(!!sym(response_var), na.rm = TRUE), .groups = 'drop') %>%
    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 TEMPORAL_DO within each TIME × DOMAIN combination
print("\n=== HARTLEY'S F-MAX TEST RESULTS ===")
set.seed(123)  # For reproducibility

hartley_temporal_results <- long_data_clean %>%
  group_by(TIME, DOMAIN) %>%
  summarise(
    hartley_result = list(bootstrap_hartley_critical(pick(TEMPORAL_DO, MEAN_DIFFERENCE), "TEMPORAL_DO", "MEAN_DIFFERENCE")),
    .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, observed_ratio, critical_95, significant)

print(hartley_temporal_results)

# =============================================================================
# MIXED ANOVA ANALYSIS
# =============================================================================

# Mixed ANOVA using aov()
# Between-subjects: TEMPORAL_DO (2 levels: 01PAST, 02FUT)
# Within-subjects: TIME (2 levels: Past, Future) × DOMAIN (4 levels: Preferences, Personality, Values, Life)

mixed_anova_model <- aov(MEAN_DIFFERENCE ~ TEMPORAL_DO * TIME * DOMAIN + Error(pID/(TIME * DOMAIN)), 
                        data = long_data_clean)

print("Mixed ANOVA Results:")
print(summary(mixed_anova_model))

# Extract effect sizes (generalized eta squared)
# For aov() objects, we need to extract from the summary
anova_summary <- summary(mixed_anova_model)

# Effect sizes will be calculated separately

# Post-hoc comparisons using emmeans
print("\n=== POST-HOC COMPARISONS ===")

# Main effect of TIME
print("Main Effect of TIME:")
time_emmeans <- emmeans(mixed_anova_model, ~ TIME)
time_contrasts <- pairs(time_emmeans, adjust = "bonferroni")
print(time_contrasts)

# Main effect of DOMAIN
print("\nMain Effect of DOMAIN:")
domain_emmeans <- emmeans(mixed_anova_model, ~ DOMAIN)
domain_contrasts <- pairs(domain_emmeans, adjust = "bonferroni")
print(domain_contrasts)

# Main effect of TEMPORAL_DO
print("\nMain Effect of TEMPORAL_DO:")
temporal_emmeans <- emmeans(mixed_anova_model, ~ TEMPORAL_DO)
temporal_contrasts <- pairs(temporal_emmeans, adjust = "bonferroni")
print(temporal_contrasts)

# Two-way interactions
print("\nTIME × DOMAIN Interaction:")
time_domain_emmeans <- emmeans(mixed_anova_model, ~ TIME * DOMAIN)
time_domain_contrasts <- pairs(time_domain_emmeans, by = "TIME", adjust = "bonferroni")
print(time_domain_contrasts)

print("\nTEMPORAL_DO × TIME Interaction:")
temporal_time_emmeans <- emmeans(mixed_anova_model, ~ TEMPORAL_DO * TIME)
temporal_time_contrasts <- pairs(temporal_time_emmeans, by = "TEMPORAL_DO", adjust = "bonferroni")
print(temporal_time_contrasts)

print("\nTEMPORAL_DO × DOMAIN Interaction:")
temporal_domain_emmeans <- emmeans(mixed_anova_model, ~ TEMPORAL_DO * DOMAIN)
temporal_domain_contrasts <- pairs(temporal_domain_emmeans, by = "TEMPORAL_DO", adjust = "bonferroni")
print(temporal_domain_contrasts)

# Three-way interaction
print("\nTEMPORAL_DO × TIME × DOMAIN Interaction:")
three_way_emmeans <- emmeans(mixed_anova_model, ~ TEMPORAL_DO * TIME * DOMAIN)
three_way_contrasts <- pairs(three_way_emmeans, by = c("TEMPORAL_DO", "TIME"), adjust = "bonferroni")
print(three_way_contrasts)