404 lines
14 KiB
R
404 lines
14 KiB
R
# Mixed ANOVA Analysis for Domain Means
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# EOHI Experiment Data Analysis - Domain Level Analysis
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# Variables: NPast_mean_pref, NPast_mean_pers, NPast_mean_val, NPast_mean_life
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# NFut_mean_pref, NFut_mean_pers, NFut_mean_val, NFut_mean_life
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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(nortest) # For normality tests
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library(ggplot2) # For plotting
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library(emmeans) # For post-hoc comparisons
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# Global options to remove scientific notation
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options(scipen = 999)
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setwd("C:/Users/irina/Documents/DND/EOHI/eohi1")
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# Read the data
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data <- read.csv("exp1.csv")
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# Display basic information about the dataset
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print(dim(data))
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print(length(unique(data$pID)))
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# Check experimental conditions
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print(table(data$GROUP, data$TEMPORAL_DO, data$ITEM_DO))
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# Check what domain mean columns are available
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domain_mean_cols <- colnames(data)[grepl("mean_(pref|pers|val|life)", colnames(data))]
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print(domain_mean_cols)
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# Verify the specific variables we need
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required_vars <- c("NPast_mean_pref", "NPast_mean_pers", "NPast_mean_val", "NPast_mean_life",
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"NFut_mean_pref", "NFut_mean_pers", "NFut_mean_val", "NFut_mean_life")
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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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} else {
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print("All required domain mean variables found!")
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}
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# Define domain mapping
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domain_mapping <- data.frame(
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variable = c("NPast_mean_pref", "NPast_mean_pers", "NPast_mean_val", "NPast_mean_life",
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"NFut_mean_pref", "NFut_mean_pers", "NFut_mean_val", "NFut_mean_life"),
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time = c(rep("Past", 4), rep("Future", 4)),
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domain = rep(c("Preferences", "Personality", "Values", "Life"), 2),
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stringsAsFactors = FALSE
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)
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print(domain_mapping)
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# Function to pivot data to long format
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pivot_domain_means <- function(data, domain_mapping) {
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long_data <- data.frame()
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for (i in 1:nrow(domain_mapping)) {
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var_name <- domain_mapping$variable[i]
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time_level <- domain_mapping$time[i]
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domain_level <- domain_mapping$domain[i]
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# Check if variable exists
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if (!var_name %in% colnames(data)) {
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print(paste("Warning: Variable", var_name, "not found in data"))
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next
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}
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# Create subset for this variable
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subset_data <- data[, c("pID", "ResponseId", "TEMPORAL_DO", var_name)]
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subset_data$TIME <- time_level
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subset_data$DOMAIN <- domain_level
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subset_data$MEAN_DIFFERENCE <- subset_data[[var_name]]
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subset_data[[var_name]] <- NULL # Remove original column
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# Add to long data
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long_data <- rbind(long_data, subset_data)
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}
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# Convert to factors with proper levels
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long_data$TIME <- factor(long_data$TIME, levels = c("Past", "Future"))
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long_data$DOMAIN <- factor(long_data$DOMAIN, levels = c("Preferences", "Personality", "Values", "Life"))
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long_data$pID <- as.factor(long_data$pID)
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long_data$TEMPORAL_DO <- as.factor(long_data$TEMPORAL_DO)
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return(long_data)
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}
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# Pivot data to long format
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tryCatch({
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long_data <- pivot_domain_means(data, domain_mapping)
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}, error = function(e) {
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print(paste("Error in data pivoting:", e$message))
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stop("Cannot proceed without proper data structure")
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})
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print(dim(long_data))
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print(length(unique(long_data$pID)))
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print(levels(long_data$TIME))
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print(levels(long_data$DOMAIN))
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# Check data types
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print(is.factor(long_data$TIME))
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print(is.factor(long_data$DOMAIN))
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print(is.factor(long_data$pID))
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print(is.numeric(long_data$MEAN_DIFFERENCE))
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# Show first 20 rows
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print(utils::head(long_data, 20))
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# Display structure and sample
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str(long_data)
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print(utils::head(long_data, 10))
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# Show example data for one participant
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participant_1_data <- long_data[long_data$pID == 1, c("pID", "TEMPORAL_DO", "TIME", "DOMAIN", "MEAN_DIFFERENCE")]
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print(participant_1_data)
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# =============================================================================
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# DESCRIPTIVE STATISTICS
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# =============================================================================
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# Overall descriptive statistics by TIME and DOMAIN
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desc_stats <- long_data %>%
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group_by(TIME, DOMAIN) %>%
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summarise(
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n = n(),
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mean = round(mean(MEAN_DIFFERENCE, na.rm = TRUE), 5),
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variance = round(var(MEAN_DIFFERENCE, na.rm = TRUE), 5),
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sd = round(sd(MEAN_DIFFERENCE, na.rm = TRUE), 5),
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median = round(median(MEAN_DIFFERENCE, na.rm = TRUE), 5),
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q1 = round(quantile(MEAN_DIFFERENCE, 0.25, na.rm = TRUE), 5),
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q3 = round(quantile(MEAN_DIFFERENCE, 0.75, na.rm = TRUE), 5),
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min = round(min(MEAN_DIFFERENCE, na.rm = TRUE), 5),
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max = round(max(MEAN_DIFFERENCE, na.rm = TRUE), 5),
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.groups = 'drop'
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)
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print("Descriptive statistics by TIME and DOMAIN:")
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print(desc_stats)
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# Descriptive statistics by between-subjects factors
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desc_stats_by_temporal <- long_data %>%
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group_by(TEMPORAL_DO, TIME, DOMAIN) %>%
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summarise(
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n = n(),
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mean = round(mean(MEAN_DIFFERENCE, na.rm = TRUE), 5),
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variance = round(var(MEAN_DIFFERENCE, na.rm = TRUE), 5),
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sd = round(sd(MEAN_DIFFERENCE, na.rm = TRUE), 5),
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.groups = 'drop'
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)
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print("Descriptive statistics by TEMPORAL_DO, TIME, and DOMAIN:")
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print(desc_stats_by_temporal)
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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$MEAN_DIFFERENCE), ]
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print(paste("Data after removing missing values:", dim(long_data_clean)))
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# 1. Missing values check
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missing_summary <- long_data %>%
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group_by(TIME, DOMAIN) %>%
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summarise(
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n_total = n(),
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n_missing = sum(is.na(MEAN_DIFFERENCE)),
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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 and DOMAIN:")
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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) %>%
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summarise(
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n = n(),
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mean = mean(MEAN_DIFFERENCE),
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sd = sd(MEAN_DIFFERENCE),
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q1 = quantile(MEAN_DIFFERENCE, 0.25),
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q3 = quantile(MEAN_DIFFERENCE, 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(MEAN_DIFFERENCE < lower_bound | MEAN_DIFFERENCE > 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. Normality tests
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normality_results <- long_data_clean %>%
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group_by(TIME, DOMAIN) %>%
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summarise(
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n = n(),
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shapiro_W = ifelse(n >= 3 & n <= 5000,
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shapiro.test(MEAN_DIFFERENCE)$statistic,
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NA),
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shapiro_p = ifelse(n >= 3 & n <= 5000,
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shapiro.test(MEAN_DIFFERENCE)$p.value,
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NA),
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anderson_A = ifelse(n >= 7,
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ad.test(MEAN_DIFFERENCE)$statistic,
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NA),
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anderson_p = ifelse(n >= 7,
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ad.test(MEAN_DIFFERENCE)$p.value,
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NA),
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.groups = 'drop'
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)
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print("Normality test results:")
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print(normality_results)
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# Note: Anderson-Darling p-values may appear identical due to machine precision limits
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# All p-values are extremely small (< 2.2e-16) indicating strong non-normality
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# The test statistics (A values) are actually different across conditions
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# Debug: Check if Anderson-Darling test is working properly
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print("\n=== DEBUG: Anderson-Darling Test Details ===")
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# Get unique combinations of TIME and DOMAIN
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unique_combos <- long_data_clean %>%
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select(TIME, DOMAIN) %>%
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distinct()
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# Run Anderson-Darling test for each combination
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for(i in 1:nrow(unique_combos)) {
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time_val <- unique_combos$TIME[i]
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domain_val <- unique_combos$DOMAIN[i]
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# Subset data for this combination
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subset_data <- long_data_clean %>%
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filter(TIME == time_val, DOMAIN == domain_val)
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cat("TIME:", time_val, "DOMAIN:", domain_val, "n =", nrow(subset_data), "\n")
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# Run Anderson-Darling test
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if(nrow(subset_data) >= 7) {
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ad_result <- ad.test(subset_data$MEAN_DIFFERENCE)
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print(ad_result)
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} else {
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cat("Sample size too small for Anderson-Darling test\n")
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}
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cat("\n")
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}
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# 4. Homogeneity of variance (Levene's test)
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# Test homogeneity across TIME within each DOMAIN
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homogeneity_time <- long_data_clean %>%
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group_by(DOMAIN) %>%
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summarise(
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levene_F = leveneTest(MEAN_DIFFERENCE ~ TIME)$`F value`[1],
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levene_p = leveneTest(MEAN_DIFFERENCE ~ TIME)$`Pr(>F)`[1],
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.groups = 'drop'
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)
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print("Homogeneity of variance across TIME within each DOMAIN:")
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print(homogeneity_time)
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# Test homogeneity across DOMAIN within each TIME
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homogeneity_domain <- long_data_clean %>%
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group_by(TIME) %>%
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summarise(
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levene_F = leveneTest(MEAN_DIFFERENCE ~ DOMAIN)$`F value`[1],
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levene_p = leveneTest(MEAN_DIFFERENCE ~ 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:")
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print(homogeneity_domain)
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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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# Function to calculate Hartley's F-max ratio
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calculate_hartley_ratio <- function(variances) {
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max(variances, na.rm = TRUE) / min(variances, na.rm = TRUE)
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}
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# =============================================================================
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# CALCULATE OBSERVED F-MAX RATIOS FOR MIXED ANOVA
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# =============================================================================
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# For mixed ANOVA: Test homogeneity across BETWEEN-SUBJECTS factor (TEMPORAL_DO)
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# within each combination of within-subjects factors (TIME × DOMAIN)
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# First, let's check what values TEMPORAL_DO actually has
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print("=== CHECKING TEMPORAL_DO VALUES ===")
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print("Unique TEMPORAL_DO values:")
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print(unique(long_data_clean$TEMPORAL_DO))
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print("TEMPORAL_DO value counts:")
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print(table(long_data_clean$TEMPORAL_DO))
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print("\n=== OBSERVED F-MAX RATIOS: TEMPORAL_DO within each TIME × DOMAIN combination ===")
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# Get the actual TEMPORAL_DO levels
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temporal_levels <- sort(unique(long_data_clean$TEMPORAL_DO))
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print(paste("TEMPORAL_DO levels:", paste(temporal_levels, collapse = ", ")))
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observed_temporal_ratios <- long_data_clean %>%
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group_by(TIME, DOMAIN) %>%
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summarise(
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# Calculate variances for each TEMPORAL_DO level within this TIME × DOMAIN combination
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temporal_var_1 = var(MEAN_DIFFERENCE[TEMPORAL_DO == temporal_levels[1]], na.rm = TRUE),
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temporal_var_2 = var(MEAN_DIFFERENCE[TEMPORAL_DO == temporal_levels[2]], na.rm = TRUE),
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# Calculate F-max ratio
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f_max_ratio = max(temporal_var_1, temporal_var_2) / min(temporal_var_1, temporal_var_2),
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.groups = 'drop'
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) %>%
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select(TIME, DOMAIN, temporal_var_1, temporal_var_2, f_max_ratio)
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print(observed_temporal_ratios)
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# Function to bootstrap critical values 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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n_groups <- length(groups)
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# Calculate observed variances for each group
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observed_vars <- data %>%
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group_by(!!sym(group_var)) %>%
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summarise(var = var(!!sym(response_var), na.rm = TRUE), .groups = 'drop') %>%
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pull(var)
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# Debug: Check for zero or invalid variances
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if(any(observed_vars <= 0 | is.na(observed_vars))) {
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cat("Warning: Invalid observed variances detected:", observed_vars, "\n")
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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 <- calculate_hartley_ratio(observed_vars)
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# Bootstrap under null hypothesis (equal variances)
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# Bootstrap from each group independently to create natural variation
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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)] # Remove any NA values
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})
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# Bootstrap F-max ratios under null hypothesis
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bootstrap_ratios <- replicate(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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calculate_hartley_ratio(sample_vars)
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})
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# Calculate critical value (95th percentile)
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# Remove any NaN or infinite values
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valid_ratios <- bootstrap_ratios[is.finite(bootstrap_ratios) & !is.na(bootstrap_ratios)]
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# Debug: Check what's happening
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cat("Total bootstrap ratios:", length(bootstrap_ratios), "\n")
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cat("Valid bootstrap ratios:", length(valid_ratios), "\n")
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cat("Sample of bootstrap ratios:", head(bootstrap_ratios, 5), "\n")
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cat("Sample of valid ratios:", head(valid_ratios, 5), "\n")
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if(length(valid_ratios) == 0) {
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cat("All bootstrap ratios were invalid. Sample ratios:", head(bootstrap_ratios, 10), "\n")
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stop("No valid bootstrap ratios generated")
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}
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critical_95 <- quantile(valid_ratios, 0.95, na.rm = TRUE)
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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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bootstrap_ratios = bootstrap_ratios
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))
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}
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# Hartley's F-max test across TEMPORAL_DO within each TIME × DOMAIN combination
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print("\n=== HARTLEY'S F-MAX TEST: TEMPORAL_DO within each TIME × DOMAIN combination ===")
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set.seed(123) # For reproducibility
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hartley_temporal_results <- long_data_clean %>%
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group_by(TIME, DOMAIN) %>%
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summarise(
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hartley_result = list(bootstrap_hartley_critical(pick(TEMPORAL_DO, MEAN_DIFFERENCE), "TEMPORAL_DO", "MEAN_DIFFERENCE")),
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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, observed_ratio, critical_95, significant)
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print(hartley_temporal_results)
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