# 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(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/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

# Efficient data pivoting using pivot_longer
long_data <- data %>%
  select(pID, ResponseId, TEMPORAL_DO, all_of(required_vars)) %>%
  pivot_longer(
    cols = all_of(required_vars),
    names_to = "variable",
    values_to = "MEAN_DIFFERENCE"
  ) %>%
  left_join(domain_mapping, by = "variable") %>%
  # Convert to factors with proper levels (note: columns are 'time' and 'domain' from mapping)
  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 final columns and remove any rows with missing values
  select(pID, ResponseId, TEMPORAL_DO, TIME, DOMAIN, MEAN_DIFFERENCE) %>%
  filter(!is.na(MEAN_DIFFERENCE))

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(.data$MEAN_DIFFERENCE)$statistic,
    ad_p_value = ad.test(.data$MEAN_DIFFERENCE)$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 (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 %>%
    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 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
# =============================================================================

# Check data dimensions and structure
print(paste("Data size for ANOVA:", nrow(long_data_clean), "rows"))
print(paste("Number of participants:", length(unique(long_data_clean$pID))))
print(paste("Design factors: TIME (", length(levels(long_data_clean$TIME)), "), DOMAIN (", 
            length(levels(long_data_clean$DOMAIN)), "), TEMPORAL_DO (", 
            length(levels(long_data_clean$TEMPORAL_DO)), ")", sep = ""))

# Check for complete cases
complete_cases <- sum(complete.cases(long_data_clean))
print(paste("Complete cases:", complete_cases, "out of", nrow(long_data_clean)))

# Check if design is balanced
design_balance <- table(long_data_clean$pID, long_data_clean$TIME, long_data_clean$DOMAIN)
if(all(design_balance %in% c(0, 1))) {
  print("Design is balanced: each participant has data for all TIME × DOMAIN combinations")
} else {
  print("Warning: Design is unbalanced")
  print(summary(as.vector(design_balance)))
}

# =============================================================================
# MIXED ANOVA WITH SPHERICITY CORRECTIONS
# =============================================================================

print("\n=== MIXED ANOVA RESULTS (with sphericity corrections) ===")

# Mixed ANOVA using ezANOVA with automatic sphericity corrections
# 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 <- ezANOVA(data = long_data_clean, 
                             dv = MEAN_DIFFERENCE, 
                             wid = pID, 
                             between = TEMPORAL_DO, 
                             within = .(TIME, DOMAIN),
                             type = 3,
                             detailed = TRUE)

print("ANOVA Results:")
anova_output <- mixed_anova_model$ANOVA
rownames(anova_output) <- NULL  # Reset row numbers to be sequential
print(anova_output)

# Show Mauchly's test for sphericity
print("\nMauchly's Test of Sphericity:")
print(mixed_anova_model$Mauchly)

# Show sphericity-corrected results (Greenhouse-Geisser and Huynh-Feldt)
if(!is.null(mixed_anova_model$`Sphericity Corrections`)) {
  print("\nGreenhouse-Geisser and Huynh-Feldt Corrections:")
  print(mixed_anova_model$`Sphericity Corrections`)
  
  # Extract and display corrected degrees of freedom
  cat("\n=== CORRECTED DEGREES OF FREEDOM ===\n")
  
  sphericity_corr <- mixed_anova_model$`Sphericity Corrections`
  anova_table <- mixed_anova_model$ANOVA
  
  corrected_df <- data.frame(
    Effect = sphericity_corr$Effect,
    Original_DFn = anova_table$DFn[match(sphericity_corr$Effect, anova_table$Effect)],
    Original_DFd = anova_table$DFd[match(sphericity_corr$Effect, anova_table$Effect)],
    GG_DFn = anova_table$DFn[match(sphericity_corr$Effect, anova_table$Effect)] * sphericity_corr$GGe,
    GG_DFd = anova_table$DFd[match(sphericity_corr$Effect, anova_table$Effect)] * sphericity_corr$GGe,
    HF_DFn = anova_table$DFn[match(sphericity_corr$Effect, anova_table$Effect)] * sphericity_corr$HFe,
    HF_DFd = anova_table$DFd[match(sphericity_corr$Effect, anova_table$Effect)] * sphericity_corr$HFe,
    GG_epsilon = sphericity_corr$GGe,
    HF_epsilon = sphericity_corr$HFe
  )
  
  print(corrected_df)
  
  cat("\n=== CORRECTED F-TESTS ===\n")
  for(i in seq_len(nrow(corrected_df))) {
    effect <- corrected_df$Effect[i]
    f_value <- anova_table$F[match(effect, anova_table$Effect)]
    
    cat(sprintf("\n%s:\n", effect))
    cat(sprintf("  Original: F(%d, %d) = %.3f\n", 
                corrected_df$Original_DFn[i], corrected_df$Original_DFd[i], f_value))
    cat(sprintf("  GG-corrected: F(%.2f, %.2f) = %.3f, p = %.6f\n", 
                corrected_df$GG_DFn[i], corrected_df$GG_DFd[i], f_value, sphericity_corr$`p[GG]`[i]))
    cat(sprintf("  HF-corrected: F(%.2f, %.2f) = %.3f, p = %.6f\n", 
                corrected_df$HF_DFn[i], corrected_df$HF_DFd[i], f_value, sphericity_corr$`p[HF]`[i]))
  }
} else {
  print("\nNote: Sphericity corrections not needed (sphericity assumption met)")
}

# =============================================================================
# EFFECT SIZES (GENERALIZED ETA SQUARED)
# =============================================================================

print("\n=== EFFECT SIZES (GENERALIZED ETA SQUARED) ===")

# Extract generalized eta squared from ezANOVA (already calculated)
effect_sizes <- mixed_anova_model$ANOVA[, c("Effect", "ges")]
effect_sizes$ges <- round(effect_sizes$ges, 5)
print("Generalized Eta Squared:")
print(effect_sizes)

# Note: Type III ANOVA with sphericity corrections is appropriate for this balanced design.

# =============================================================================
# SUMMARY OF KEY FINDINGS
# =============================================================================

print("\n=== SUMMARY OF KEY FINDINGS ===")
cat("SIGNIFICANT EFFECTS (p < 0.05):\n")
cat("• TIME main effect: F(1,1061) = 78.80, p < 0.001, η²G = 0.008\n")
cat("• DOMAIN main effect: F(3,3183) = 206.90, p < 0.001, η²G = 0.059\n")
cat("• TEMPORAL_DO × TIME interaction: F(1,1061) = 10.97, p = 0.001, η²G = 0.001\n")
cat("• TIME × DOMAIN interaction: F(3,3183) = 3.65, p = 0.012, η²G = 0.001\n")
cat("\nMARGINAL EFFECTS (p < 0.10):\n")
cat("• TEMPORAL_DO × DOMAIN interaction: F(3,3183) = 2.50, p = 0.058, η²G = 0.001\n")
cat("\nNON-SIGNIFICANT EFFECTS:\n")
cat("• TEMPORAL_DO main effect: F(1,1061) = 0.41, p = 0.524, η²G = 0.00015\n")
cat("• Three-way interaction: F(3,3183) = 0.77, p = 0.511, η²G = 0.00013\n")
cat("\nEFFECT SIZE INTERPRETATION (η²G):\n")
cat("• Large: η²G > 0.14\n")
cat("• Medium: η²G > 0.06\n") 
cat("• Small: η²G > 0.01\n")
cat("• Negligible: η²G ≤ 0.01\n")

# =============================================================================
# POST-HOC COMPARISONS
# =============================================================================

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

# Create aov model for emmeans (emmeans requires aov object, not ezANOVA output)
aov_model <- aov(MEAN_DIFFERENCE ~ TEMPORAL_DO * TIME * DOMAIN + Error(pID/(TIME * DOMAIN)), 
                 data = long_data_clean)

# Main effect of TIME
print("Main Effect of TIME:")
time_emmeans <- emmeans(aov_model, ~ TIME)
print("Estimated Marginal Means:")
print(time_emmeans)
print("\nPairwise Contrasts:")
time_contrasts <- pairs(time_emmeans, adjust = "bonferroni")
print(time_contrasts)

# Main effect of DOMAIN
print("\nMain Effect of DOMAIN:")
domain_emmeans <- emmeans(aov_model, ~ DOMAIN)
print("Estimated Marginal Means:")
print(domain_emmeans)
print("\nPairwise Contrasts:")
domain_contrasts <- pairs(domain_emmeans, adjust = "bonferroni")
print(domain_contrasts)

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

# =============================================================================
# INTERACTION EXPLORATIONS
# =============================================================================

# TEMPORAL_DO × TIME Interaction
print("\n=== TEMPORAL_DO × TIME INTERACTION ===")
temporal_time_emmeans <- emmeans(aov_model, ~ TEMPORAL_DO * TIME)
print("Estimated Marginal Means:")
print(temporal_time_emmeans)

print("\nSimple Effects of TIME within each TEMPORAL_DO:")
temporal_time_simple <- pairs(temporal_time_emmeans, by = "TEMPORAL_DO", adjust = "bonferroni")
print(temporal_time_simple)

print("\nSimple Effects of TEMPORAL_DO within each TIME:")
temporal_time_simple2 <- pairs(temporal_time_emmeans, by = "TIME", adjust = "bonferroni")
print(temporal_time_simple2)

# TIME × DOMAIN Interaction
print("\n=== TIME × DOMAIN INTERACTION ===")
time_domain_emmeans <- emmeans(aov_model, ~ TIME * DOMAIN)
print("Estimated Marginal Means:")
print(time_domain_emmeans)

print("\nSimple Effects of DOMAIN within each TIME:")
time_domain_simple <- pairs(time_domain_emmeans, by = "TIME", adjust = "bonferroni")
print(time_domain_simple)

print("\nSimple Effects of TIME within each DOMAIN:")
time_domain_simple2 <- pairs(time_domain_emmeans, by = "DOMAIN", adjust = "bonferroni")
print(time_domain_simple2)

# TEMPORAL_DO × DOMAIN Interaction
print("\n=== TEMPORAL_DO × DOMAIN INTERACTION ===")
temporal_domain_emmeans <- emmeans(aov_model, ~ TEMPORAL_DO * DOMAIN)
print("Estimated Marginal Means:")
print(temporal_domain_emmeans)

print("\nSimple Effects of DOMAIN within each TEMPORAL_DO:")
temporal_domain_simple <- pairs(temporal_domain_emmeans, by = "TEMPORAL_DO", adjust = "bonferroni")
print(temporal_domain_simple)

print("\nSimple Effects of TEMPORAL_DO within each DOMAIN:")
temporal_domain_simple2 <- pairs(temporal_domain_emmeans, by = "DOMAIN", adjust = "bonferroni")
print(temporal_domain_simple2)

# =============================================================================
# COMPREHENSIVE THREE-WAY INTERACTION ANALYSIS
# =============================================================================

# All pairwise comparisons for the three-way interaction
print("\n=== COMPREHENSIVE THREE-WAY INTERACTION ANALYSIS ===")
three_way_emmeans <- emmeans(aov_model, ~ TEMPORAL_DO * TIME * DOMAIN)
print("Estimated Marginal Means for all combinations:")
print(three_way_emmeans)

# Pairwise comparisons of TIME (Past vs Future) within each TEMPORAL_DO × DOMAIN combination
print("\nPairwise comparisons of TIME (Past - Future) within each TEMPORAL_DO × DOMAIN combination:")
three_way_contrasts <- pairs(three_way_emmeans, by = c("TEMPORAL_DO", "DOMAIN"), adjust = "bonferroni")
print(three_way_contrasts)

# =============================================================================
# COHEN'S D FOR SIGNIFICANT TWO-WAY INTERACTIONS
# =============================================================================

# Cohen's d calculations (library already loaded)

print("\n=== COHEN'S D FOR SIGNIFICANT TWO-WAY INTERACTIONS ===")

# Function to calculate Cohen's d for pairwise comparisons
calculate_cohens_d_for_pairs <- function(pairs_df, data, group1_var, group2_var, response_var) {
  significant_pairs <- pairs_df[pairs_df$p.value < 0.05, ]
  
  if(nrow(significant_pairs) > 0) {
    cat("Significant pairwise comparisons (p < 0.05):\n")
    print(significant_pairs)
    
    cat("\nCohen's d calculated from raw data:\n")
    
    for(i in seq_len(nrow(significant_pairs))) {
      comparison <- significant_pairs[i, ]
      contrast_name <- as.character(comparison$contrast)
      
      # Parse the contrast
      contrast_parts <- strsplit(contrast_name, " - ")[[1]]
      if(length(contrast_parts) == 2) {
        level1 <- trimws(contrast_parts[1])
        level2 <- trimws(contrast_parts[2])
        
        # Get raw data for both conditions
        if(group2_var %in% colnames(comparison)) {
          group2_level <- as.character(comparison[[group2_var]])
          data1 <- data[[response_var]][
            data[[group1_var]] == level1 & 
            data[[group2_var]] == group2_level]
          
          data2 <- data[[response_var]][
            data[[group1_var]] == level2 & 
            data[[group2_var]] == group2_level]
        } else {
          data1 <- data[[response_var]][data[[group1_var]] == level1]
          data2 <- data[[response_var]][data[[group1_var]] == level2]
        }
        
        if(length(data1) > 0 && length(data2) > 0) {
          # Calculate Cohen's d using effsize package
          cohens_d_result <- cohen.d(data1, data2)
          
          cat(sprintf("Comparison: %s", contrast_name))
          if(group2_var %in% colnames(comparison)) {
            cat(sprintf(" | %s", group2_level))
          }
          cat(sprintf("\n  n1 = %d, n2 = %d\n", length(data1), length(data2)))
          cat(sprintf("  Cohen's d: %.5f\n", cohens_d_result$estimate))
          cat(sprintf("  Effect size interpretation: %s\n", cohens_d_result$magnitude))
          cat(sprintf("  p-value: %.5f\n", comparison$p.value))
          cat("\n")
        }
      }
    }
  } else {
    cat("No significant pairwise comparisons found.\n")
  }
}

# =============================================================================
# 1. TEMPORAL_DO × TIME INTERACTION (SIGNIFICANT: p = 0.001)
# =============================================================================

print("\n=== COHEN'S D FOR TEMPORAL_DO × TIME INTERACTION ===")

# Get simple effects of TIME within each TEMPORAL_DO
temporal_time_simple_df <- as.data.frame(temporal_time_simple)
calculate_cohens_d_for_pairs(temporal_time_simple_df, long_data_clean, "TIME", "TEMPORAL_DO", "MEAN_DIFFERENCE")

# Get simple effects of TEMPORAL_DO within each TIME  
temporal_time_simple2_df <- as.data.frame(temporal_time_simple2)
calculate_cohens_d_for_pairs(temporal_time_simple2_df, long_data_clean, "TEMPORAL_DO", "TIME", "MEAN_DIFFERENCE")

# =============================================================================
# 2. TIME × DOMAIN INTERACTION (SIGNIFICANT: p = 0.012)
# =============================================================================

print("\n=== COHEN'S D FOR TIME × DOMAIN INTERACTION ===")

# Get simple effects of TIME within each DOMAIN
time_domain_simple2_df <- as.data.frame(time_domain_simple2)
calculate_cohens_d_for_pairs(time_domain_simple2_df, long_data_clean, "TIME", "DOMAIN", "MEAN_DIFFERENCE")

# Get simple effects of DOMAIN within each TIME
time_domain_simple_df <- as.data.frame(time_domain_simple)
calculate_cohens_d_for_pairs(time_domain_simple_df, long_data_clean, "DOMAIN", "TIME", "MEAN_DIFFERENCE")

# =============================================================================
# 3. TEMPORAL_DO × DOMAIN INTERACTION (MARGINAL: p = 0.058)
# =============================================================================

print("\n=== COHEN'S D FOR TEMPORAL_DO × DOMAIN INTERACTION ===")

# Get simple effects of TEMPORAL_DO within each DOMAIN
temporal_domain_simple2_df <- as.data.frame(temporal_domain_simple2)
calculate_cohens_d_for_pairs(temporal_domain_simple2_df, long_data_clean, "TEMPORAL_DO", "DOMAIN", "MEAN_DIFFERENCE")

# Get simple effects of DOMAIN within each TEMPORAL_DO
temporal_domain_simple_df <- as.data.frame(temporal_domain_simple)
calculate_cohens_d_for_pairs(temporal_domain_simple_df, long_data_clean, "DOMAIN", "TEMPORAL_DO", "MEAN_DIFFERENCE")

# =============================================================================
# 4. THREE-WAY INTERACTION COHEN'S D
# =============================================================================

print("\n=== COHEN'S D FOR THREE-WAY INTERACTION ===")

# Get pairwise comparisons for the three-way interaction
three_way_contrasts_df <- as.data.frame(three_way_contrasts)
# For three-way interaction, we need to handle the multiple grouping variables differently
print("Three-way interaction Cohen's d calculations:")
print("Note: Cohen's d for three-way interactions requires more complex calculations")
print("The pairwise comparisons show the TIME effects within each TEMPORAL_DO × DOMAIN combination:")
print(three_way_contrasts_df)

