280 lines
11 KiB
R
280 lines
11 KiB
R
# Mixed ANOVA Analysis for Domain Means - EOHI2
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# EOHI Experiment Data Analysis - Domain Level Analysis with INTERVAL factor
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# Variables: NPast_5_pref_MEAN, NPast_5_pers_MEAN, NPast_5_val_MEAN, etc.
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# NFut_5_pref_MEAN, NFut_5_pers_MEAN, NFut_5_val_MEAN, etc.
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# NPast_10_pref_MEAN, NPast_10_pers_MEAN, NPast_10_val_MEAN, etc.
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# NFut_10_pref_MEAN, NFut_10_pers_MEAN, NFut_10_val_MEAN, etc.
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# 5.10past_pref_MEAN, 5.10past_pers_MEAN, 5.10past_val_MEAN
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# 5.10fut_pref_MEAN, 5.10fut_pers_MEAN, 5.10fut_val_MEAN
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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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# 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$ResponseId))))
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# Verify the specific variables we need
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required_vars <- c("NPast_5_pref_MEAN", "NPast_5_pers_MEAN", "NPast_5_val_MEAN",
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"NPast_10_pref_MEAN", "NPast_10_pers_MEAN", "NPast_10_val_MEAN",
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"NFut_5_pref_MEAN", "NFut_5_pers_MEAN", "NFut_5_val_MEAN",
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"NFut_10_pref_MEAN", "NFut_10_pers_MEAN", "NFut_10_val_MEAN",
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"5.10past_pref_MEAN", "5.10past_pers_MEAN", "5.10past_val_MEAN",
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"5.10fut_pref_MEAN", "5.10fut_pers_MEAN", "5.10fut_val_MEAN")
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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 with TIME, DOMAIN, and INTERVAL factors
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domain_mapping <- data.frame(
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variable = required_vars,
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time = c(rep("Past", 3), rep("Past", 3), rep("Future", 3), rep("Future", 3),
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rep("Past", 3), rep("Future", 3)),
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domain = rep(c("Preferences", "Personality", "Values"), 6),
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interval = c(rep("5", 3), rep("10", 3), rep("5", 3), rep("10", 3),
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rep("5_10", 3), rep("5_10", 3)),
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stringsAsFactors = FALSE
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)
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print("Domain mapping created:")
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print(domain_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 = "MEAN_DIFFERENCE"
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) %>%
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left_join(domain_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", "5_10")),
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ResponseId = as.factor(ResponseId),
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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(ResponseId, TEMPORAL_DO, INTERVAL_DO, TIME, DOMAIN, INTERVAL, MEAN_DIFFERENCE) %>%
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filter(!is.na(MEAN_DIFFERENCE))
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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$ResponseId))))
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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(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, 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(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 between-subjects factors:")
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print(desc_stats_by_between)
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# Summary by between-subjects factors only
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desc_stats_between_only <- long_data %>%
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group_by(TEMPORAL_DO, INTERVAL_DO) %>%
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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 between-subjects factors only:")
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print(desc_stats_between_only)
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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, INTERVAL) %>%
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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, 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(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
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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$MEAN_DIFFERENCE)$statistic,
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ad_p_value = ad.test(.data$MEAN_DIFFERENCE)$p.value,
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.groups = 'drop'
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)
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print("Anderson-Darling normality test results:")
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# Round only the numeric columns
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normality_results_rounded <- normality_results %>%
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mutate(across(where(is.numeric), ~ round(.x, 5)))
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print(normality_results_rounded)
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# 4. Homogeneity of variance (Levene's test)
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# Test homogeneity across TIME within each DOMAIN × INTERVAL combination
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homogeneity_time <- long_data_clean %>%
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group_by(DOMAIN, INTERVAL) %>%
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summarise(
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levene_F = leveneTest(MEAN_DIFFERENCE ~ TIME)$`F value`[1],
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levene_p = leveneTest(MEAN_DIFFERENCE ~ TIME)$`Pr(>F)`[1],
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.groups = 'drop'
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)
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print("Homogeneity of variance across TIME within each DOMAIN × INTERVAL combination:")
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print(homogeneity_time)
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# Test homogeneity across DOMAIN within each TIME × INTERVAL combination
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homogeneity_domain <- long_data_clean %>%
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group_by(TIME, INTERVAL) %>%
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summarise(
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levene_F = leveneTest(MEAN_DIFFERENCE ~ DOMAIN)$`F value`[1],
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levene_p = leveneTest(MEAN_DIFFERENCE ~ DOMAIN)$`Pr(>F)`[1],
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.groups = 'drop'
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)
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print("Homogeneity of variance across DOMAIN within each TIME × INTERVAL combination:")
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print(homogeneity_domain)
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# Test homogeneity across INTERVAL within each TIME × DOMAIN combination
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homogeneity_interval <- long_data_clean %>%
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group_by(TIME, DOMAIN) %>%
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summarise(
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levene_F = leveneTest(MEAN_DIFFERENCE ~ INTERVAL)$`F value`[1],
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levene_p = leveneTest(MEAN_DIFFERENCE ~ INTERVAL)$`Pr(>F)`[1],
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.groups = 'drop'
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)
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print("Homogeneity of variance across INTERVAL within each TIME × DOMAIN combination:")
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print(homogeneity_interval)
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# 5. Hartley's F-max test for between-subjects factors
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print("\n=== HARTLEY'S F-MAX TEST FOR BETWEEN-SUBJECTS FACTORS ===")
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# Check what values the between-subjects factors actually have
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print("Unique TEMPORAL_DO values:")
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print(unique(long_data_clean$TEMPORAL_DO))
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print("Unique INTERVAL_DO values:")
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print(unique(long_data_clean$INTERVAL_DO))
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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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# Hartley's F-max test across TEMPORAL_DO within each TIME × DOMAIN × INTERVAL combination
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print("\n=== HARTLEY'S F-MAX TEST: TEMPORAL_DO within each TIME × DOMAIN × INTERVAL combination ===")
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observed_temporal_ratios <- long_data_clean %>%
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group_by(TIME, DOMAIN, INTERVAL) %>%
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summarise(
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# Calculate variances for each TEMPORAL_DO level within this 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, INTERVAL, past_var, fut_var, f_max_ratio)
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print(observed_temporal_ratios)
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# Hartley's F-max test across INTERVAL_DO within each TIME × DOMAIN × TEMPORAL_DO combination
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print("\n=== HARTLEY'S F-MAX TEST: INTERVAL_DO within each TIME × DOMAIN × TEMPORAL_DO combination ===")
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observed_interval_ratios <- long_data_clean %>%
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group_by(TIME, DOMAIN, TEMPORAL_DO) %>%
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summarise(
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# Calculate variances for each INTERVAL_DO level within this combination
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int5_var = var(MEAN_DIFFERENCE[INTERVAL_DO == "5"], na.rm = TRUE),
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int10_var = var(MEAN_DIFFERENCE[INTERVAL_DO == "10"], na.rm = TRUE),
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# Calculate F-max ratio
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f_max_ratio = max(int5_var, int10_var) / min(int5_var, int10_var),
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.groups = 'drop'
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) %>%
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select(TIME, DOMAIN, TEMPORAL_DO, int5_var, int10_var, f_max_ratio)
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print(observed_interval_ratios) |