118 lines
3.9 KiB
R
118 lines
3.9 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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# Read the data
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data <- read.csv("eohi1/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$domain[i]
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# Check if variable exists
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if (!var_name %in% colnames(data)) {
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print(paste("Warning: Variable", var_name, "not found in data"))
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next
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}
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# Create subset for this variable
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subset_data <- data[, c("pID", "ResponseId", "GROUP", "TEMPORAL_DO", "ITEM_DO", var_name)]
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subset_data$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$GROUP <- as.factor(long_data$GROUP)
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long_data$TEMPORAL_DO <- as.factor(long_data$TEMPORAL_DO)
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long_data$ITEM_DO <- as.factor(long_data$ITEM_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", "GROUP", "TEMPORAL_DO", "ITEM_DO", "TIME", "DOMAIN", "MEAN_DIFFERENCE")]
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print(participant_1_data)
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