747 lines
28 KiB
R
747 lines
28 KiB
R
# Mixed ANOVA Analysis for DGEN Variables
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# EOHI Experiment 2 Data Analysis - DGEN Level Analysis with TIME, DOMAIN, and INTERVAL factors
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# 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,
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# DGEN_fut_5_Pref, DGEN_fut_5_Pers, DGEN_fut_5_Val, DGEN_fut_10_Pref, DGEN_fut_10_Pers, DGEN_fut_10_Val
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# Load required libraries
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library(tidyverse)
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library(ez)
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library(car)
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library(afex) # For aov_ez (cleaner ANOVA output)
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library(nortest) # For normality tests
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library(emmeans) # For post-hoc comparisons
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library(purrr) # For map functions
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library(effsize) # For Cohen's d calculations
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library(effectsize) # For effect size calculations
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# Global options to remove scientific notation
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options(scipen = 999)
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# Set contrasts to sum for mixed ANOVA (necessary for proper interpretation)
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options(contrasts = c("contr.sum", "contr.poly"))
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setwd("C:/Users/irina/Documents/DND/EOHI/eohi2")
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# Read the data
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data <- read.csv("eohi2.csv")
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# Verify the specific variables we need
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required_vars <- c("DGEN_past_5_Pref", "DGEN_past_5_Pers", "DGEN_past_5_Val",
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"DGEN_past_10_Pref", "DGEN_past_10_Pers", "DGEN_past_10_Val",
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"DGEN_fut_5_Pref", "DGEN_fut_5_Pers", "DGEN_fut_5_Val",
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"DGEN_fut_10_Pref", "DGEN_fut_10_Pers", "DGEN_fut_10_Val")
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missing_vars <- required_vars[!required_vars %in% colnames(data)]
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if (length(missing_vars) > 0) {
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print(paste("Warning: Missing variables:", paste(missing_vars, collapse = ", ")))
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}
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# Define variable mapping for the three within-subjects factors
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variable_mapping <- data.frame(
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variable = required_vars,
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TIME = c(rep("Past", 6), rep("Future", 6)),
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DOMAIN = rep(c("Preferences", "Personality", "Values"), 4),
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INTERVAL = rep(c("5", "5", "5", "10", "10", "10"), 2),
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stringsAsFactors = FALSE
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)
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print("Variable mapping:")
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print(variable_mapping)
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# Efficient data pivoting using pivot_longer
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long_data <- data %>%
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select(ResponseId, TEMPORAL_DO, INTERVAL_DO, all_of(required_vars)) %>%
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pivot_longer(
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cols = all_of(required_vars),
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names_to = "variable",
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values_to = "DGEN_SCORE"
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) %>%
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left_join(variable_mapping, by = "variable") %>%
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# Convert to factors with proper levels
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mutate(
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TIME = factor(TIME, levels = c("Past", "Future")),
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DOMAIN = factor(DOMAIN, levels = c("Preferences", "Personality", "Values")),
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INTERVAL = factor(INTERVAL, levels = c("5", "10")),
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pID = as.factor(ResponseId), # Use ResponseId as participant ID
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temporal_DO = as.factor(TEMPORAL_DO),
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interval_DO = as.factor(INTERVAL_DO)
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) %>%
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# Select final columns and remove any rows with missing values
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select(pID, ResponseId, temporal_DO, interval_DO, TIME, DOMAIN, INTERVAL, DGEN_SCORE) %>%
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filter(!is.na(DGEN_SCORE))
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# =============================================================================
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# DESCRIPTIVE STATISTICS
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# =============================================================================
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# Overall descriptive statistics by TIME, DOMAIN, and INTERVAL
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desc_stats <- long_data %>%
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group_by(TIME, DOMAIN, INTERVAL) %>%
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summarise(
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n = n(),
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mean = round(mean(DGEN_SCORE, na.rm = TRUE), 5),
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variance = round(var(DGEN_SCORE, na.rm = TRUE), 5),
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sd = round(sd(DGEN_SCORE, na.rm = TRUE), 5),
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median = round(median(DGEN_SCORE, na.rm = TRUE), 5),
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q1 = round(quantile(DGEN_SCORE, 0.25, na.rm = TRUE), 5),
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q3 = round(quantile(DGEN_SCORE, 0.75, na.rm = TRUE), 5),
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min = round(min(DGEN_SCORE, na.rm = TRUE), 5),
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max = round(max(DGEN_SCORE, na.rm = TRUE), 5),
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.groups = 'drop'
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)
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print("Descriptive statistics by TIME, DOMAIN, and INTERVAL:")
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print(desc_stats)
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# Descriptive statistics by between-subjects factors
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desc_stats_by_between <- long_data %>%
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group_by(temporal_DO, interval_DO, TIME, DOMAIN, INTERVAL) %>%
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summarise(
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n = n(),
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mean = round(mean(DGEN_SCORE, na.rm = TRUE), 5),
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variance = round(var(DGEN_SCORE, na.rm = TRUE), 5),
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sd = round(sd(DGEN_SCORE, na.rm = TRUE), 5),
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.groups = 'drop'
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)
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print("Descriptive statistics by between-subjects factors:")
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print(desc_stats_by_between)
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# Calculate mean differences for key comparisons
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print("\n=== KEY MEAN DIFFERENCES ===")
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# Past vs Future differences for each DOMAIN × INTERVAL combination
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past_future_diffs <- long_data %>%
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group_by(DOMAIN, INTERVAL, pID) %>%
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summarise(
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past_score = DGEN_SCORE[TIME == "Past"],
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future_score = DGEN_SCORE[TIME == "Future"],
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difference = past_score - future_score,
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.groups = 'drop'
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) %>%
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group_by(DOMAIN, INTERVAL) %>%
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summarise(
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n = n(),
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mean_diff = round(mean(difference, na.rm = TRUE), 5),
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sd_diff = round(sd(difference, na.rm = TRUE), 5),
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se_diff = round(sd(difference, na.rm = TRUE) / sqrt(n()), 5),
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.groups = 'drop'
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)
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print("Past vs Future differences by DOMAIN × INTERVAL:")
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print(past_future_diffs)
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# 5 vs 10 interval differences for each TIME × DOMAIN combination
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interval_diffs <- long_data %>%
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group_by(TIME, DOMAIN, pID) %>%
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summarise(
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interval_5_score = DGEN_SCORE[INTERVAL == "5"],
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interval_10_score = DGEN_SCORE[INTERVAL == "10"],
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difference = interval_5_score - interval_10_score,
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.groups = 'drop'
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) %>%
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group_by(TIME, DOMAIN) %>%
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summarise(
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n = n(),
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mean_diff = round(mean(difference, na.rm = TRUE), 5),
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sd_diff = round(sd(difference, na.rm = TRUE), 5),
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se_diff = round(sd(difference, na.rm = TRUE) / sqrt(n()), 5),
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.groups = 'drop'
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)
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print("\n5 vs 10 interval differences by TIME × DOMAIN:")
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print(interval_diffs)
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# =============================================================================
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# ASSUMPTION TESTING
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# =============================================================================
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# Remove missing values for assumption testing
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long_data_clean <- long_data[!is.na(long_data$DGEN_SCORE), ]
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# 1. Missing values check
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missing_summary <- long_data %>%
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group_by(TIME, DOMAIN, INTERVAL) %>%
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summarise(
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n_total = n(),
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n_missing = sum(is.na(DGEN_SCORE)),
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pct_missing = round(100 * n_missing / n_total, 2),
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.groups = 'drop'
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)
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print("Missing values by TIME, DOMAIN, and INTERVAL:")
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print(missing_summary)
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# 2. Outlier detection
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outlier_summary <- long_data_clean %>%
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group_by(TIME, DOMAIN, INTERVAL) %>%
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summarise(
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n = n(),
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mean = mean(DGEN_SCORE),
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sd = sd(DGEN_SCORE),
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q1 = quantile(DGEN_SCORE, 0.25),
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q3 = quantile(DGEN_SCORE, 0.75),
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iqr = q3 - q1,
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lower_bound = q1 - 1.5 * iqr,
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upper_bound = q3 + 1.5 * iqr,
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n_outliers = sum(DGEN_SCORE < lower_bound | DGEN_SCORE > upper_bound),
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.groups = 'drop'
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)
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print("Outlier summary (IQR method):")
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print(outlier_summary)
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# 3. Anderson-Darling normality test
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normality_results <- long_data_clean %>%
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group_by(TIME, DOMAIN, INTERVAL) %>%
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summarise(
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n = n(),
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ad_statistic = ad.test(.data$DGEN_SCORE)$statistic,
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ad_p_value = ad.test(.data$DGEN_SCORE)$p.value,
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.groups = 'drop'
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)
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print("Anderson-Darling normality test results:")
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# Round only the numeric columns
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normality_results_rounded <- normality_results %>%
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mutate(across(where(is.numeric), ~ round(.x, 5)))
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print(normality_results_rounded)
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# 4. Homogeneity of variance tests
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# Test homogeneity across TIME within each DOMAIN × INTERVAL combination
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homogeneity_time <- long_data_clean %>%
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group_by(DOMAIN, INTERVAL) %>%
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summarise(
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levene_F = leveneTest(DGEN_SCORE ~ TIME)$`F value`[1],
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levene_p = leveneTest(DGEN_SCORE ~ TIME)$`Pr(>F)`[1],
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.groups = 'drop'
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)
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print("Homogeneity of variance across TIME within each DOMAIN × INTERVAL:")
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print(homogeneity_time)
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# Test homogeneity across DOMAIN within each TIME × INTERVAL combination
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homogeneity_domain <- long_data_clean %>%
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group_by(TIME, INTERVAL) %>%
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summarise(
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levene_F = leveneTest(DGEN_SCORE ~ DOMAIN)$`F value`[1],
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levene_p = leveneTest(DGEN_SCORE ~ DOMAIN)$`Pr(>F)`[1],
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.groups = 'drop'
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)
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print("Homogeneity of variance across DOMAIN within each TIME × INTERVAL:")
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print(homogeneity_domain)
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# Test homogeneity across INTERVAL within each TIME × DOMAIN combination
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homogeneity_interval <- long_data_clean %>%
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group_by(TIME, DOMAIN) %>%
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summarise(
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levene_F = leveneTest(DGEN_SCORE ~ INTERVAL)$`F value`[1],
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levene_p = leveneTest(DGEN_SCORE ~ INTERVAL)$`Pr(>F)`[1],
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.groups = 'drop'
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)
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print("Homogeneity of variance across INTERVAL within each TIME × DOMAIN:")
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print(homogeneity_interval)
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# =============================================================================
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# HARTLEY'S F-MAX TEST WITH BOOTSTRAP CRITICAL VALUES
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# =============================================================================
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# More efficient bootstrap function for Hartley's F-max test
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bootstrap_hartley_critical <- function(data, group_var, response_var, n_iter = 1000) {
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# Get unique groups and their sample sizes
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groups <- unique(data[[group_var]])
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# Calculate observed variances for each group
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observed_vars <- data %>%
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dplyr::group_by(!!rlang::sym(group_var)) %>%
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dplyr::summarise(var = var(!!rlang::sym(response_var), na.rm = TRUE), .groups = 'drop') %>%
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dplyr::pull(var)
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# Handle invalid variances
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if(any(observed_vars <= 0 | is.na(observed_vars))) {
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observed_vars[observed_vars <= 0 | is.na(observed_vars)] <- 1e-10
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}
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# Calculate observed F-max ratio
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observed_ratio <- max(observed_vars) / min(observed_vars)
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# Pre-allocate storage for bootstrap ratios
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bootstrap_ratios <- numeric(n_iter)
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# Get group data once
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group_data_list <- map(groups, ~ {
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group_data <- data[data[[group_var]] == .x, response_var]
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group_data[!is.na(group_data)]
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})
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# Bootstrap with pre-allocated storage
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for(i in 1:n_iter) {
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# Bootstrap sample from each group independently
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sample_vars <- map_dbl(group_data_list, ~ {
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bootstrap_sample <- sample(.x, size = length(.x), replace = TRUE)
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var(bootstrap_sample, na.rm = TRUE)
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})
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bootstrap_ratios[i] <- max(sample_vars) / min(sample_vars)
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}
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# Remove invalid ratios
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valid_ratios <- bootstrap_ratios[is.finite(bootstrap_ratios) & !is.na(bootstrap_ratios)]
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if(length(valid_ratios) == 0) {
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stop("No valid bootstrap ratios generated")
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}
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# Calculate critical value (95th percentile)
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critical_95 <- quantile(valid_ratios, 0.95, na.rm = TRUE)
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# Return only essential information
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return(list(
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observed_ratio = observed_ratio,
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critical_95 = critical_95,
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n_valid_iterations = length(valid_ratios)
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))
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}
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# Hartley's F-max test across between-subjects factors within each within-subjects combination
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print("\n=== HARTLEY'S F-MAX TEST RESULTS ===")
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set.seed(123) # For reproducibility
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# Test across temporal_DO within each TIME × DOMAIN × INTERVAL combination
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print("F-max test across temporal_DO within each TIME × DOMAIN × INTERVAL combination:")
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hartley_temporal_results <- long_data_clean %>%
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group_by(TIME, DOMAIN, INTERVAL) %>%
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summarise(
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hartley_result = list(bootstrap_hartley_critical(pick(temporal_DO, DGEN_SCORE), "temporal_DO", "DGEN_SCORE")),
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.groups = 'drop'
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) %>%
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mutate(
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observed_ratio = map_dbl(hartley_result, ~ .x$observed_ratio),
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critical_95 = map_dbl(hartley_result, ~ .x$critical_95),
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significant = observed_ratio > critical_95
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) %>%
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select(TIME, DOMAIN, INTERVAL, observed_ratio, critical_95, significant)
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print(hartley_temporal_results)
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# Test across interval_DO within each TIME × DOMAIN × INTERVAL combination
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print("F-max test across interval_DO within each TIME × DOMAIN × INTERVAL combination:")
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hartley_interval_results <- long_data_clean %>%
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group_by(TIME, DOMAIN, INTERVAL) %>%
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summarise(
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hartley_result = list(bootstrap_hartley_critical(pick(interval_DO, DGEN_SCORE), "interval_DO", "DGEN_SCORE")),
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.groups = 'drop'
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) %>%
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mutate(
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observed_ratio = map_dbl(hartley_result, ~ .x$observed_ratio),
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critical_95 = map_dbl(hartley_result, ~ .x$critical_95),
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significant = observed_ratio > critical_95
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) %>%
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select(TIME, DOMAIN, INTERVAL, observed_ratio, critical_95, significant)
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print(hartley_interval_results)
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# =============================================================================
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# MIXED ANOVA ANALYSIS
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# =============================================================================
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# Check if design is balanced
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design_balance <- table(long_data_clean$pID, long_data_clean$TIME, long_data_clean$DOMAIN, long_data_clean$INTERVAL)
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if(all(design_balance %in% c(0, 1))) {
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print("Design is balanced: each participant has data for all TIME × DOMAIN × INTERVAL combinations")
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} else {
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print("Warning: Design is unbalanced")
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print(summary(as.vector(design_balance)))
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}
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# =============================================================================
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# MIXED ANOVA WITH SPHERICITY CORRECTIONS
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# =============================================================================
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print("=== MIXED ANOVA RESULTS (with sphericity corrections) ===")
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# Mixed ANOVA using ezANOVA with automatic sphericity corrections
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# Between-subjects: temporal_DO (2 levels) × interval_DO (2 levels)
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# Within-subjects: TIME (2 levels: Past, Future) × DOMAIN (3 levels: Preferences, Personality, Values) × INTERVAL (2 levels: 5, 10)
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mixed_anova_model <- ezANOVA(data = long_data_clean,
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dv = DGEN_SCORE,
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wid = pID,
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between = .(temporal_DO, interval_DO),
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within = .(TIME, DOMAIN, INTERVAL),
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type = 3,
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detailed = TRUE)
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anova_output <- mixed_anova_model$ANOVA
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rownames(anova_output) <- NULL # Reset row numbers to be sequential
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print(anova_output)
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print("Mauchly's Test of Sphericity:")
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print(mixed_anova_model$Mauchly)
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# Show sphericity-corrected results (Greenhouse-Geisser and Huynh-Feldt)
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if(!is.null(mixed_anova_model$`Sphericity Corrections`)) {
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print("Greenhouse-Geisser and Huynh-Feldt Corrections:")
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print(mixed_anova_model$`Sphericity Corrections`)
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print("=== CORRECTED DEGREES OF FREEDOM ===")
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sphericity_corr <- mixed_anova_model$`Sphericity Corrections`
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anova_table <- mixed_anova_model$ANOVA
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corrected_df <- data.frame(
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Effect = sphericity_corr$Effect,
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Original_DFn = anova_table$DFn[match(sphericity_corr$Effect, anova_table$Effect)],
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Original_DFd = anova_table$DFd[match(sphericity_corr$Effect, anova_table$Effect)],
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GG_DFn = anova_table$DFn[match(sphericity_corr$Effect, anova_table$Effect)] * sphericity_corr$GGe,
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GG_DFd = anova_table$DFd[match(sphericity_corr$Effect, anova_table$Effect)] * sphericity_corr$GGe,
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HF_DFn = anova_table$DFn[match(sphericity_corr$Effect, anova_table$Effect)] * sphericity_corr$HFe,
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HF_DFd = anova_table$DFd[match(sphericity_corr$Effect, anova_table$Effect)] * sphericity_corr$HFe,
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GG_epsilon = sphericity_corr$GGe,
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HF_epsilon = sphericity_corr$HFe
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)
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print(corrected_df)
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print("=== CORRECTED F-TESTS ===")
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corrected_results <- data.frame(
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Effect = corrected_df$Effect,
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Original_F = anova_table$F[match(corrected_df$Effect, anova_table$Effect)],
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Original_DFn = corrected_df$Original_DFn,
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Original_DFd = corrected_df$Original_DFd,
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GG_DFn = corrected_df$GG_DFn,
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GG_DFd = corrected_df$GG_DFd,
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HF_DFn = corrected_df$HF_DFn,
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HF_DFd = corrected_df$HF_DFd,
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GG_p = sphericity_corr$`p[GG]`,
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HF_p = sphericity_corr$`p[HF]`
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)
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print(corrected_results)
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}
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# =============================================================================
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# EFFECT SIZES (GENERALIZED ETA SQUARED)
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# =============================================================================
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print("=== EFFECT SIZES (GENERALIZED ETA SQUARED) ===")
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|
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# Extract generalized eta squared from ezANOVA (already calculated)
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effect_sizes <- mixed_anova_model$ANOVA[, c("Effect", "ges")]
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effect_sizes$ges <- round(effect_sizes$ges, 5)
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print(effect_sizes)
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|
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# =============================================================================
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# POST-HOC COMPARISONS
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# =============================================================================
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|
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print("=== POST-HOC COMPARISONS ===")
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|
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# Create aov model for emmeans (emmeans requires aov object, not ezANOVA output)
|
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aov_model <- aov(DGEN_SCORE ~ temporal_DO * interval_DO * TIME * DOMAIN * INTERVAL + Error(pID/(TIME * DOMAIN * INTERVAL)),
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data = long_data_clean)
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|
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# Main effects
|
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print("Main Effect of TIME:")
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time_emmeans <- emmeans(aov_model, ~ TIME)
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print(time_emmeans)
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time_contrasts <- pairs(time_emmeans, adjust = "bonferroni")
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print(time_contrasts)
|
||
|
||
print("Main Effect of DOMAIN:")
|
||
domain_emmeans <- emmeans(aov_model, ~ DOMAIN)
|
||
print(domain_emmeans)
|
||
domain_contrasts <- pairs(domain_emmeans, adjust = "bonferroni")
|
||
print(domain_contrasts)
|
||
|
||
print("Main Effect of INTERVAL:")
|
||
interval_emmeans <- emmeans(aov_model, ~ INTERVAL)
|
||
print(interval_emmeans)
|
||
interval_contrasts <- pairs(interval_emmeans, adjust = "bonferroni")
|
||
print(interval_contrasts)
|
||
|
||
print("Main Effect of temporal_DO:")
|
||
temporal_emmeans <- emmeans(aov_model, ~ temporal_DO)
|
||
temporal_contrasts <- pairs(temporal_emmeans, adjust = "bonferroni")
|
||
print(temporal_contrasts)
|
||
|
||
print("Main Effect of interval_DO:")
|
||
interval_do_emmeans <- emmeans(aov_model, ~ interval_DO)
|
||
interval_do_contrasts <- pairs(interval_do_emmeans, adjust = "bonferroni")
|
||
print(interval_do_contrasts)
|
||
|
||
# =============================================================================
|
||
# TWO-WAY INTERACTION EXPLORATIONS
|
||
# =============================================================================
|
||
|
||
# TIME × DOMAIN Interaction
|
||
print("=== TIME × DOMAIN INTERACTION ===")
|
||
time_domain_emmeans <- emmeans(aov_model, ~ TIME * DOMAIN)
|
||
print(time_domain_emmeans)
|
||
time_domain_simple <- pairs(time_domain_emmeans, by = "TIME", adjust = "bonferroni")
|
||
print(time_domain_simple)
|
||
time_domain_simple2 <- pairs(time_domain_emmeans, by = "DOMAIN", adjust = "bonferroni")
|
||
print(time_domain_simple2)
|
||
|
||
# TIME × INTERVAL Interaction
|
||
print("=== TIME × INTERVAL INTERACTION ===")
|
||
time_interval_emmeans <- emmeans(aov_model, ~ TIME * INTERVAL)
|
||
print(time_interval_emmeans)
|
||
time_interval_simple <- pairs(time_interval_emmeans, by = "TIME", adjust = "bonferroni")
|
||
print(time_interval_simple)
|
||
time_interval_simple2 <- pairs(time_interval_emmeans, by = "INTERVAL", adjust = "bonferroni")
|
||
print(time_interval_simple2)
|
||
|
||
# DOMAIN × INTERVAL Interaction
|
||
print("=== DOMAIN × INTERVAL INTERACTION ===")
|
||
domain_interval_emmeans <- emmeans(aov_model, ~ DOMAIN * INTERVAL)
|
||
print(domain_interval_emmeans)
|
||
domain_interval_simple <- pairs(domain_interval_emmeans, by = "DOMAIN", adjust = "bonferroni")
|
||
print(domain_interval_simple)
|
||
domain_interval_simple2 <- pairs(domain_interval_emmeans, by = "INTERVAL", adjust = "bonferroni")
|
||
print(domain_interval_simple2)
|
||
|
||
# Between-subjects interactions
|
||
print("=== temporal_DO × interval_DO INTERACTION ===")
|
||
temporal_interval_do_emmeans <- emmeans(aov_model, ~ temporal_DO * interval_DO)
|
||
print(temporal_interval_do_emmeans)
|
||
temporal_interval_do_simple <- pairs(temporal_interval_do_emmeans, by = "temporal_DO", adjust = "bonferroni")
|
||
print(temporal_interval_do_simple)
|
||
temporal_interval_do_simple2 <- pairs(temporal_interval_do_emmeans, by = "interval_DO", adjust = "bonferroni")
|
||
print(temporal_interval_do_simple2)
|
||
|
||
# =============================================================================
|
||
# THREE-WAY INTERACTION ANALYSES
|
||
# =============================================================================
|
||
|
||
# TIME × DOMAIN × INTERVAL Interaction
|
||
print("=== TIME × DOMAIN × INTERVAL INTERACTION ===")
|
||
three_way_emmeans <- emmeans(aov_model, ~ TIME * DOMAIN * INTERVAL)
|
||
print(three_way_emmeans)
|
||
three_way_contrasts <- pairs(three_way_emmeans, by = c("DOMAIN", "INTERVAL"), adjust = "bonferroni")
|
||
print(three_way_contrasts)
|
||
|
||
# Between-subjects × within-subjects interactions
|
||
print("=== temporal_DO × TIME INTERACTION ===")
|
||
temporal_time_emmeans <- emmeans(aov_model, ~ temporal_DO * TIME)
|
||
print(temporal_time_emmeans)
|
||
temporal_time_simple <- pairs(temporal_time_emmeans, by = "temporal_DO", adjust = "bonferroni")
|
||
print(temporal_time_simple)
|
||
temporal_time_simple2 <- pairs(temporal_time_emmeans, by = "TIME", adjust = "bonferroni")
|
||
print(temporal_time_simple2)
|
||
|
||
# =============================================================================
|
||
# COHEN'S D CALCULATIONS FOR SIGNIFICANT EFFECTS
|
||
# =============================================================================
|
||
|
||
print("=== COHEN'S D FOR SIGNIFICANT EFFECTS ===")
|
||
|
||
# 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) {
|
||
print(significant_pairs)
|
||
|
||
# Calculate Cohen's d for all significant pairs
|
||
cohens_d_results <- data.frame()
|
||
|
||
for(i in seq_len(nrow(significant_pairs))) {
|
||
comparison <- significant_pairs[i, ]
|
||
contrast_name <- as.character(comparison$contrast)
|
||
|
||
# Parse the contrast
|
||
contrast_parts <- strsplit(contrast_name, " - ")[[1]]
|
||
if(length(contrast_parts) == 2) {
|
||
level1 <- trimws(contrast_parts[1])
|
||
level2 <- trimws(contrast_parts[2])
|
||
|
||
# Get raw data for both conditions
|
||
if(!is.null(group2_var) && group2_var %in% colnames(comparison)) {
|
||
group2_level <- as.character(comparison[[group2_var]])
|
||
data1 <- data[[response_var]][
|
||
data[[group1_var]] == level1 &
|
||
data[[group2_var]] == group2_level]
|
||
|
||
data2 <- data[[response_var]][
|
||
data[[group1_var]] == level2 &
|
||
data[[group2_var]] == group2_level]
|
||
} else {
|
||
data1 <- data[[response_var]][data[[group1_var]] == level1]
|
||
data2 <- data[[response_var]][data[[group1_var]] == level2]
|
||
}
|
||
|
||
if(length(data1) > 0 && length(data2) > 0) {
|
||
# Calculate Cohen's d using effsize package
|
||
cohens_d_result <- cohen.d(data1, data2)
|
||
|
||
result_row <- data.frame(
|
||
Comparison = contrast_name,
|
||
n1 = length(data1),
|
||
n2 = length(data2),
|
||
Cohens_d = round(cohens_d_result$estimate, 5),
|
||
Effect_size = cohens_d_result$magnitude,
|
||
p_value = round(comparison$p.value, 5)
|
||
)
|
||
|
||
if(!is.null(group2_var) && group2_var %in% colnames(comparison)) {
|
||
result_row$Group_level <- group2_level
|
||
}
|
||
|
||
cohens_d_results <- rbind(cohens_d_results, result_row)
|
||
}
|
||
}
|
||
}
|
||
|
||
if(nrow(cohens_d_results) > 0) {
|
||
print(cohens_d_results)
|
||
}
|
||
} else {
|
||
cat("No significant pairwise comparisons found.\n")
|
||
}
|
||
}
|
||
|
||
# Calculate Cohen's d for main effects
|
||
time_contrasts_df <- as.data.frame(time_contrasts)
|
||
calculate_cohens_d_for_pairs(time_contrasts_df, long_data_clean, "TIME", NULL, "DGEN_SCORE")
|
||
|
||
domain_contrasts_df <- as.data.frame(domain_contrasts)
|
||
calculate_cohens_d_for_pairs(domain_contrasts_df, long_data_clean, "DOMAIN", NULL, "DGEN_SCORE")
|
||
|
||
interval_contrasts_df <- as.data.frame(interval_contrasts)
|
||
calculate_cohens_d_for_pairs(interval_contrasts_df, long_data_clean, "INTERVAL", NULL, "DGEN_SCORE")
|
||
|
||
# Calculate Cohen's d for two-way interactions
|
||
time_domain_simple_df <- as.data.frame(time_domain_simple)
|
||
calculate_cohens_d_for_pairs(time_domain_simple_df, long_data_clean, "DOMAIN", "TIME", "DGEN_SCORE")
|
||
|
||
time_domain_simple2_df <- as.data.frame(time_domain_simple2)
|
||
calculate_cohens_d_for_pairs(time_domain_simple2_df, long_data_clean, "TIME", "DOMAIN", "DGEN_SCORE")
|
||
|
||
# Calculate Cohen's d for three-way interaction
|
||
three_way_contrasts_df <- as.data.frame(three_way_contrasts)
|
||
significant_three_way <- three_way_contrasts_df[three_way_contrasts_df$p.value < 0.05, ]
|
||
|
||
if(nrow(significant_three_way) > 0) {
|
||
three_way_cohens_d <- data.frame()
|
||
|
||
for(i in seq_len(nrow(significant_three_way))) {
|
||
comparison <- significant_three_way[i, ]
|
||
|
||
# Extract the grouping variables
|
||
domain_level <- as.character(comparison$DOMAIN)
|
||
interval_level <- as.character(comparison$INTERVAL)
|
||
|
||
# Get data for Past and Future within this DOMAIN × INTERVAL combination
|
||
past_data <- long_data_clean$DGEN_SCORE[
|
||
long_data_clean$DOMAIN == domain_level &
|
||
long_data_clean$INTERVAL == interval_level &
|
||
long_data_clean$TIME == "Past"
|
||
]
|
||
|
||
future_data <- long_data_clean$DGEN_SCORE[
|
||
long_data_clean$DOMAIN == domain_level &
|
||
long_data_clean$INTERVAL == interval_level &
|
||
long_data_clean$TIME == "Future"
|
||
]
|
||
|
||
if(length(past_data) > 0 && length(future_data) > 0) {
|
||
# Calculate Cohen's d using effsize package
|
||
cohens_d_result <- cohen.d(past_data, future_data)
|
||
|
||
result_row <- data.frame(
|
||
DOMAIN = domain_level,
|
||
INTERVAL = interval_level,
|
||
Comparison = "Past vs Future",
|
||
n_Past = length(past_data),
|
||
n_Future = length(future_data),
|
||
Cohens_d = round(cohens_d_result$estimate, 5),
|
||
Effect_size = cohens_d_result$magnitude,
|
||
p_value = round(comparison$p.value, 5),
|
||
Estimated_difference = round(comparison$estimate, 5)
|
||
)
|
||
|
||
three_way_cohens_d <- rbind(three_way_cohens_d, result_row)
|
||
}
|
||
}
|
||
|
||
if(nrow(three_way_cohens_d) > 0) {
|
||
print(three_way_cohens_d)
|
||
}
|
||
} else {
|
||
cat("No significant TIME effects found within any DOMAIN × INTERVAL combination.\n")
|
||
}
|
||
|
||
# =============================================================================
|
||
# INTERACTION PLOT: temporal_DO × TIME × INTERVAL (Emmeans only)
|
||
# =============================================================================
|
||
|
||
print("=== INTERACTION PLOT ===")
|
||
|
||
# Define color palette for TIME
|
||
time_colors <- c("Past" = "#648FFF", "Future" = "#DC267F")
|
||
|
||
# Create emmeans for temporal_DO × TIME × INTERVAL
|
||
emm_3way_plot <- emmeans(aov_model, ~ temporal_DO * TIME * INTERVAL)
|
||
|
||
# Prepare emmeans data frame
|
||
emmeans_3way_plot <- emm_3way_plot %>%
|
||
as.data.frame() %>%
|
||
filter(!is.na(lower.CL) & !is.na(upper.CL) & !is.na(emmean)) %>%
|
||
rename(
|
||
ci_lower = lower.CL,
|
||
ci_upper = upper.CL,
|
||
plot_mean = emmean
|
||
) %>%
|
||
mutate(
|
||
temporal_DO = factor(temporal_DO, levels = c("01PAST", "02FUT")),
|
||
TIME = factor(TIME, levels = c("Past", "Future")),
|
||
INTERVAL = factor(INTERVAL, levels = c("5", "10")),
|
||
x_pos = as.numeric(temporal_DO),
|
||
time_offset = (as.numeric(TIME) - 1.5) * 0.2,
|
||
x_dodged = x_pos + time_offset
|
||
)
|
||
|
||
# Create 3-way interaction plot with facets
|
||
interaction_plot_3way <- ggplot(emmeans_3way_plot) +
|
||
geom_errorbar(
|
||
aes(x = x_dodged, ymin = ci_lower, ymax = ci_upper, color = TIME),
|
||
width = 0.1,
|
||
linewidth = 1,
|
||
alpha = 0.8
|
||
) +
|
||
geom_point(
|
||
aes(x = x_dodged, y = plot_mean, fill = TIME, shape = TIME),
|
||
size = 5,
|
||
stroke = 1.2,
|
||
color = "black"
|
||
) +
|
||
facet_wrap(~INTERVAL, nrow = 1) +
|
||
labs(
|
||
x = "Order",
|
||
y = "Mean absolute difference from present",
|
||
title = "temporal_DO × TIME × INTERVAL Interaction (Estimated Marginal Means)"
|
||
) +
|
||
scale_x_continuous(
|
||
breaks = c(1, 2),
|
||
labels = c("Past First", "Future First"),
|
||
limits = c(0.5, 2.5)
|
||
) +
|
||
scale_color_manual(name = "Temporal Direction", values = time_colors) +
|
||
scale_fill_manual(name = "Temporal Direction", values = time_colors) +
|
||
scale_shape_manual(name = "Temporal Direction", values = c(21, 22)) +
|
||
theme_minimal(base_size = 13) +
|
||
theme(
|
||
axis.text = element_text(size = 11),
|
||
axis.title = element_text(size = 12),
|
||
plot.title = element_text(size = 14, hjust = 0.5),
|
||
legend.position = "right",
|
||
legend.title = element_text(size = 11),
|
||
legend.text = element_text(size = 10),
|
||
panel.grid.major.x = element_blank(),
|
||
panel.grid.minor = element_blank(),
|
||
panel.border = element_rect(color = "gray80", fill = NA, linewidth = 0.5),
|
||
strip.text = element_text(size = 11, face = "bold")
|
||
)
|
||
|
||
print(interaction_plot_3way) |