410 lines
15 KiB
R
410 lines
15 KiB
R
# mixed anova not working
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# 12/09/2025
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# add sum contrasts
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# 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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library(purrr) # For map functions
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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/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(paste("Dataset dimensions:", paste(dim(data), collapse = " x")))
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print(paste("Number of participants:", length(unique(data$pID))))
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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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# Domain mapping created
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# More efficient data pivoting (avoiding pivot_longer issues)
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pivot_domain_means <- function(data, domain_mapping) {
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# Pre-allocate the result data frame for better performance
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n_rows <- nrow(data) * nrow(domain_mapping)
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long_data <- data.frame(
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pID = character(n_rows),
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ResponseId = character(n_rows),
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TEMPORAL_DO = character(n_rows),
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TIME = character(n_rows),
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DOMAIN = character(n_rows),
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MEAN_DIFFERENCE = numeric(n_rows),
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stringsAsFactors = FALSE
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)
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row_idx <- 1
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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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# Get the number of rows for this variable
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n_data_rows <- nrow(data)
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# Fill in the data for this variable
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long_data$pID[row_idx:(row_idx + n_data_rows - 1)] <- as.character(data$pID)
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long_data$ResponseId[row_idx:(row_idx + n_data_rows - 1)] <- as.character(data$ResponseId)
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long_data$TEMPORAL_DO[row_idx:(row_idx + n_data_rows - 1)] <- as.character(data$TEMPORAL_DO)
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long_data$TIME[row_idx:(row_idx + n_data_rows - 1)] <- time_level
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long_data$DOMAIN[row_idx:(row_idx + n_data_rows - 1)] <- domain_level
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long_data$MEAN_DIFFERENCE[row_idx:(row_idx + n_data_rows - 1)] <- data[[var_name]]
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row_idx <- row_idx + n_data_rows
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}
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# Convert to factors with proper levels
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long_data$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(paste("Long data dimensions:", paste(dim(long_data), collapse = " x")))
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print(paste("Number of participants:", length(unique(long_data$pID))))
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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:", paste(dim(long_data_clean), collapse = " x")))
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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. Anderson-Darling normality test (streamlined)
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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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ad_statistic = ad.test(.data$MEAN_DIFFERENCE)$statistic,
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ad_p_value = ad.test(.data$MEAN_DIFFERENCE)$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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print(round(normality_results, 5))
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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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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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past_var = var(MEAN_DIFFERENCE[TEMPORAL_DO == "01PAST"], na.rm = TRUE),
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fut_var = var(MEAN_DIFFERENCE[TEMPORAL_DO == "02FUT"], na.rm = TRUE),
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# Calculate F-max ratio
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f_max_ratio = max(past_var, fut_var) / min(past_var, fut_var),
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.groups = 'drop'
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) %>%
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select(TIME, DOMAIN, past_var, fut_var, f_max_ratio)
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print(observed_temporal_ratios)
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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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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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# 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 TEMPORAL_DO within each TIME × DOMAIN 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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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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# =============================================================================
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# MIXED ANOVA ANALYSIS
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# =============================================================================
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# Mixed ANOVA using aov()
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# Between-subjects: TEMPORAL_DO (2 levels: 01PAST, 02FUT)
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# Within-subjects: TIME (2 levels: Past, Future) × DOMAIN (4 levels: Preferences, Personality, Values, Life)
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mixed_anova_model <- aov(MEAN_DIFFERENCE ~ TEMPORAL_DO * TIME * DOMAIN + Error(pID/(TIME * DOMAIN)),
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data = long_data_clean)
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print("Mixed ANOVA Results:")
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print(summary(mixed_anova_model))
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# Extract effect sizes (generalized eta squared)
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# For aov() objects, we need to extract from the summary
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anova_summary <- summary(mixed_anova_model)
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# Effect sizes will be calculated separately
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# Post-hoc comparisons using emmeans
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print("\n=== POST-HOC COMPARISONS ===")
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# Main effect of TIME
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print("Main Effect of TIME:")
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time_emmeans <- emmeans(mixed_anova_model, ~ TIME)
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time_contrasts <- pairs(time_emmeans, adjust = "bonferroni")
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print(time_contrasts)
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# Main effect of DOMAIN
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print("\nMain Effect of DOMAIN:")
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domain_emmeans <- emmeans(mixed_anova_model, ~ DOMAIN)
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domain_contrasts <- pairs(domain_emmeans, adjust = "bonferroni")
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print(domain_contrasts)
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# Main effect of TEMPORAL_DO
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print("\nMain Effect of TEMPORAL_DO:")
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temporal_emmeans <- emmeans(mixed_anova_model, ~ TEMPORAL_DO)
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temporal_contrasts <- pairs(temporal_emmeans, adjust = "bonferroni")
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print(temporal_contrasts)
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# Two-way interactions
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print("\nTIME × DOMAIN Interaction:")
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time_domain_emmeans <- emmeans(mixed_anova_model, ~ TIME * DOMAIN)
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time_domain_contrasts <- pairs(time_domain_emmeans, by = "TIME", adjust = "bonferroni")
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print(time_domain_contrasts)
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print("\nTEMPORAL_DO × TIME Interaction:")
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temporal_time_emmeans <- emmeans(mixed_anova_model, ~ TEMPORAL_DO * TIME)
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temporal_time_contrasts <- pairs(temporal_time_emmeans, by = "TEMPORAL_DO", adjust = "bonferroni")
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print(temporal_time_contrasts)
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print("\nTEMPORAL_DO × DOMAIN Interaction:")
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temporal_domain_emmeans <- emmeans(mixed_anova_model, ~ TEMPORAL_DO * DOMAIN)
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temporal_domain_contrasts <- pairs(temporal_domain_emmeans, by = "TEMPORAL_DO", adjust = "bonferroni")
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print(temporal_domain_contrasts)
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# Three-way interaction
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print("\nTEMPORAL_DO × TIME × DOMAIN Interaction:")
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three_way_emmeans <- emmeans(mixed_anova_model, ~ TEMPORAL_DO * TIME * DOMAIN)
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three_way_contrasts <- pairs(three_way_emmeans, by = c("TEMPORAL_DO", "TIME"), adjust = "bonferroni")
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print(three_way_contrasts)
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