661 lines
22 KiB
Plaintext
661 lines
22 KiB
Plaintext
---
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title: "Mixed ANOVA Analysis for Domain Means"
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author: "Irina"
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date: "`r Sys.Date()`"
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output:
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html_document:
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toc: true
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toc_float: true
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code_folding: hide
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theme: flatly
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highlight: tango
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fig_width: 10
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fig_height: 6
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---
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```{r setup, include=FALSE}
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knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE)
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```
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# Introduction
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This analysis examines domain-level differences in mean scores across time periods using a mixed ANOVA design. The analysis focuses on four domains (Preferences, Personality, Values, Life) across two time periods (Past, Future) with a between-subjects factor (TEMPORAL_DO).
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# Data Preparation and Setup
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```{r 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(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(ggplot2) # For plotting
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options(scipen = 999)
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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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```
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```{r data-loading}
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# Read the data
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data <- read.csv("exp1.csv")
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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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# 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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```
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```{r data-reshaping}
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long_data <- data %>%
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select(pID, ResponseId, TEMPORAL_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 (note: columns are 'time' and 'domain' from mapping)
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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", "Life")),
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pID = as.factor(pID),
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TEMPORAL_DO = as.factor(TEMPORAL_DO)
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) %>%
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# Select final columns and remove any rows with missing values
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select(pID, ResponseId, TEMPORAL_DO, TIME, DOMAIN, MEAN_DIFFERENCE) %>%
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filter(!is.na(MEAN_DIFFERENCE))
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# Create clean dataset for analysis (fixing the reference issue)
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long_data_clean <- long_data
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```
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# Descriptive Statistics
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## Overall Descriptive Statistics by TIME and DOMAIN
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```{r descriptive-stats}
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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(desc_stats)
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```
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## Descriptive Statistics by Between-Subjects Factors
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```{r descriptive-stats-temporal}
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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(desc_stats_by_temporal)
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```
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# Assumption Testing
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## Missing Values Check
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```{r missing-values}
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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_summary)
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```
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## Outlier Detection
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```{r 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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median = median(MEAN_DIFFERENCE),
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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)
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```
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## Anderson-Darling Normality Test
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```{r normality-test}
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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)$p.value,
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.groups = 'drop'
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)
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print(normality_results)
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```
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## Homogeneity of Variance (Levene's Test)
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### Test homogeneity across TIME within each DOMAIN
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```{r homogeneity-time}
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homogeneity_time <- long_data_clean %>%
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group_by(DOMAIN) %>%
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summarise(
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levene_F = leveneTest(MEAN_DIFFERENCE ~ TIME)$`F value`[1],
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levene_p = leveneTest(MEAN_DIFFERENCE ~ TIME)$`Pr(>F)`[1],
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.groups = 'drop'
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)
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print(homogeneity_time)
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```
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### Test homogeneity across DOMAIN within each TIME
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```{r homogeneity-domain}
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homogeneity_domain <- long_data_clean %>%
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group_by(TIME) %>%
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summarise(
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levene_F = leveneTest(MEAN_DIFFERENCE ~ DOMAIN)$`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_domain)
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```
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## Hartley's F-Max Test with Bootstrap Critical Values
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```{r hartley-function}
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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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# More efficient bootstrap function for Hartley's F-max test
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bootstrap_hartley_critical <- function(data, group_var, response_var, n_iter = 1000) {
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# Get unique groups and their sample sizes
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groups <- unique(data[[group_var]])
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# Calculate observed variances for each group
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observed_vars <- data %>%
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dplyr::group_by(!!rlang::sym(group_var)) %>%
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dplyr::summarise(var = var(!!rlang::sym(response_var), na.rm = TRUE), .groups = 'drop') %>%
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dplyr::pull(var)
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# Handle invalid variances
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if(any(observed_vars <= 0 | is.na(observed_vars))) {
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observed_vars[observed_vars <= 0 | is.na(observed_vars)] <- 1e-10
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}
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# Calculate observed F-max ratio
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observed_ratio <- max(observed_vars) / min(observed_vars)
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# Pre-allocate storage for bootstrap ratios
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bootstrap_ratios <- numeric(n_iter)
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# Get group data once
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group_data_list <- map(groups, ~ {
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group_data <- data[data[[group_var]] == .x, response_var]
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group_data[!is.na(group_data)]
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})
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# Bootstrap with pre-allocated storage
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for(i in 1:n_iter) {
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# Bootstrap sample from each group independently
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sample_vars <- map_dbl(group_data_list, ~ {
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bootstrap_sample <- sample(.x, size = length(.x), replace = TRUE)
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var(bootstrap_sample, na.rm = TRUE)
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})
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bootstrap_ratios[i] <- max(sample_vars) / min(sample_vars)
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}
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# Remove invalid ratios
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valid_ratios <- bootstrap_ratios[is.finite(bootstrap_ratios) & !is.na(bootstrap_ratios)]
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if(length(valid_ratios) == 0) {
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stop("No valid bootstrap ratios generated")
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}
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# Calculate critical value (95th percentile)
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critical_95 <- quantile(valid_ratios, 0.95, na.rm = TRUE)
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# Return only essential information
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return(list(
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observed_ratio = observed_ratio,
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critical_95 = critical_95,
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n_valid_iterations = length(valid_ratios)
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))
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}
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```
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```{r hartley-results}
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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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print(unique(long_data_clean$TEMPORAL_DO))
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print(table(long_data_clean$TEMPORAL_DO))
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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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# Hartley's F-max test across 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$critical_95),
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significant = observed_ratio > critical_95
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) %>%
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select(TIME, DOMAIN, observed_ratio, critical_95, significant)
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print(hartley_temporal_results)
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```
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# Mixed ANOVA Analysis
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## Design Balance Check
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```{r design-check}
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# Check for complete cases
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complete_cases <- sum(complete.cases(long_data_clean))
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print(complete_cases)
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# Check if design is balanced
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design_balance <- table(long_data_clean$pID, long_data_clean$TIME, long_data_clean$DOMAIN)
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if(all(design_balance %in% c(0, 1))) {
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print("Design is balanced: each participant has data for all TIME × DOMAIN combinations")
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} else {
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print("Warning: Design is unbalanced")
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print(summary(as.vector(design_balance)))
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}
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```
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## Mixed ANOVA with Sphericity Corrections
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```{r mixed-anova}
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# Mixed ANOVA using ezANOVA with automatic sphericity corrections
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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 <- ezANOVA(data = long_data_clean,
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dv = MEAN_DIFFERENCE,
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wid = pID,
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between = TEMPORAL_DO,
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within = .(TIME, DOMAIN),
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type = 3,
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detailed = TRUE)
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anova_output <- mixed_anova_model$ANOVA
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rownames(anova_output) <- NULL # Reset row numbers to be sequential
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print(anova_output)
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```
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## Mauchly's Test for Sphericity
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```{r mauchly-test}
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print(mixed_anova_model$Mauchly)
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```
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## Sphericity-Corrected Results
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```{r sphericity-corrections}
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# Show sphericity-corrected results (Greenhouse-Geisser and Huynh-Feldt)
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if(!is.null(mixed_anova_model$`Sphericity Corrections`)) {
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print(mixed_anova_model$`Sphericity Corrections`)
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# Extract and display corrected degrees of freedom
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sphericity_corr <- mixed_anova_model$`Sphericity Corrections`
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anova_table <- mixed_anova_model$ANOVA
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corrected_df <- data.frame(
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Effect = sphericity_corr$Effect,
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Original_DFn = anova_table$DFn[match(sphericity_corr$Effect, anova_table$Effect)],
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Original_DFd = anova_table$DFd[match(sphericity_corr$Effect, anova_table$Effect)],
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GG_DFn = anova_table$DFn[match(sphericity_corr$Effect, anova_table$Effect)] * sphericity_corr$GGe,
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GG_DFd = anova_table$DFd[match(sphericity_corr$Effect, anova_table$Effect)] * sphericity_corr$GGe,
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HF_DFn = anova_table$DFn[match(sphericity_corr$Effect, anova_table$Effect)] * sphericity_corr$HFe,
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HF_DFd = anova_table$DFd[match(sphericity_corr$Effect, anova_table$Effect)] * sphericity_corr$HFe,
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GG_epsilon = sphericity_corr$GGe,
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HF_epsilon = sphericity_corr$HFe
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)
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print(corrected_df)
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# Between-subjects effects (no sphericity corrections needed)
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between_effects <- c("TEMPORAL_DO")
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for(effect in between_effects) {
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if(effect %in% anova_table$Effect) {
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f_value <- anova_table$F[anova_table$Effect == effect]
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dfn <- anova_table$DFn[anova_table$Effect == effect]
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dfd <- anova_table$DFd[anova_table$Effect == effect]
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p_value <- anova_table$p[anova_table$Effect == effect]
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cat(sprintf("%s: F(%d, %d) = %.3f, p = %.6f\n", effect, dfn, dfd, f_value, p_value))
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}
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}
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# Within-subjects effects (sphericity corrections where applicable)
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# TIME main effect (2 levels, sphericity automatically satisfied)
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if("TIME" %in% anova_table$Effect) {
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f_value <- anova_table$F[anova_table$Effect == "TIME"]
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dfn <- anova_table$DFn[anova_table$Effect == "TIME"]
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dfd <- anova_table$DFd[anova_table$Effect == "TIME"]
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p_value <- anova_table$p[anova_table$Effect == "TIME"]
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cat(sprintf("TIME: F(%d, %d) = %.3f, p = %.6f (2 levels, sphericity satisfied)\n", dfn, dfd, f_value, p_value))
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}
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# DOMAIN main effect (4 levels, needs sphericity correction)
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if("DOMAIN" %in% anova_table$Effect) {
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f_value <- anova_table$F[anova_table$Effect == "DOMAIN"]
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dfn <- anova_table$DFn[anova_table$Effect == "DOMAIN"]
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dfd <- anova_table$DFd[anova_table$Effect == "DOMAIN"]
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p_value <- anova_table$p[anova_table$Effect == "DOMAIN"]
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cat(sprintf("DOMAIN: F(%d, %d) = %.3f, p = %.6f\n", dfn, dfd, f_value, p_value))
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}
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# Interactions with sphericity corrections
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for(i in seq_len(nrow(corrected_df))) {
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effect <- corrected_df$Effect[i]
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f_value <- anova_table$F[match(effect, anova_table$Effect)]
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cat(sprintf("\n%s:\n", effect))
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cat(sprintf(" Original: F(%d, %d) = %.3f\n",
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corrected_df$Original_DFn[i], corrected_df$Original_DFd[i], f_value))
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cat(sprintf(" GG-corrected: F(%.2f, %.2f) = %.3f, p = %.6f\n",
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corrected_df$GG_DFn[i], corrected_df$GG_DFd[i], f_value, sphericity_corr$`p[GG]`[i]))
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cat(sprintf(" HF-corrected: F(%.2f, %.2f) = %.3f, p = %.6f\n",
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corrected_df$HF_DFn[i], corrected_df$HF_DFd[i], f_value, sphericity_corr$`p[HF]`[i]))
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}
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} else {
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print("\nNote: Sphericity corrections not needed (sphericity assumption met)")
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}
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```
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# Effect Sizes (Cohen's d)
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## Main Effects
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```{r cohens-d-main}
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# Create aov model for emmeans
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aov_model <- aov(MEAN_DIFFERENCE ~ TEMPORAL_DO * TIME * DOMAIN + Error(pID/(TIME * DOMAIN)),
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data = long_data_clean)
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# Main Effect of TIME
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time_emmeans <- emmeans(aov_model, ~ TIME)
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print(time_emmeans)
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time_main_contrast <- pairs(time_emmeans, adjust = "Bonferroni")
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time_main_df <- as.data.frame(time_main_contrast)
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print(time_main_df)
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# Calculate Cohen's d for TIME main effect
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if(nrow(time_main_df) > 0) {
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cat("\nCohen's d for TIME main effect:\n")
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time_past_data <- long_data_clean$MEAN_DIFFERENCE[long_data_clean$TIME == "Past"]
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time_future_data <- long_data_clean$MEAN_DIFFERENCE[long_data_clean$TIME == "Future"]
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time_cohens_d <- cohen.d(time_past_data, time_future_data)
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cat(sprintf("Past vs Future: n1 = %d, n2 = %d\n", length(time_past_data), length(time_future_data)))
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cat(sprintf("Cohen's d: %.5f\n", time_cohens_d$estimate))
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cat(sprintf("Effect size interpretation: %s\n", time_cohens_d$magnitude))
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cat(sprintf("p-value: %.5f\n", time_main_df$p.value[1]))
|
||
}
|
||
```
|
||
|
||
```{r cohens-d-domain}
|
||
# Main Effect of DOMAIN (significant: p < 0.001)
|
||
domain_emmeans <- emmeans(aov_model, ~ DOMAIN)
|
||
print(domain_emmeans)
|
||
domain_main_contrast <- pairs(domain_emmeans, adjust = "bonferroni")
|
||
domain_main_df <- as.data.frame(domain_main_contrast)
|
||
print(domain_main_df)
|
||
|
||
# Calculate Cohen's d for significant DOMAIN contrasts
|
||
significant_domain <- domain_main_df[domain_main_df$p.value < 0.05, ]
|
||
if(nrow(significant_domain) > 0) {
|
||
cat("\nCohen's d for significant DOMAIN contrasts:\n")
|
||
for(i in seq_len(nrow(significant_domain))) {
|
||
contrast_name <- as.character(significant_domain$contrast[i])
|
||
contrast_parts <- strsplit(contrast_name, " - ")[[1]]
|
||
if(length(contrast_parts) == 2) {
|
||
level1 <- trimws(contrast_parts[1])
|
||
level2 <- trimws(contrast_parts[2])
|
||
|
||
data1 <- long_data_clean$MEAN_DIFFERENCE[long_data_clean$DOMAIN == level1]
|
||
data2 <- long_data_clean$MEAN_DIFFERENCE[long_data_clean$DOMAIN == level2]
|
||
|
||
if(length(data1) > 0 && length(data2) > 0) {
|
||
domain_cohens_d <- cohen.d(data1, data2)
|
||
cat(sprintf("Comparison: %s\n", contrast_name))
|
||
cat(sprintf(" n1 = %d, n2 = %d\n", length(data1), length(data2)))
|
||
cat(sprintf(" Cohen's d: %.5f\n", domain_cohens_d$estimate))
|
||
cat(sprintf(" Effect size interpretation: %s\n", domain_cohens_d$magnitude))
|
||
cat(sprintf(" p-value: %.5f\n", significant_domain$p.value[i]))
|
||
cat("\n")
|
||
}
|
||
}
|
||
}
|
||
}
|
||
```
|
||
|
||
## Two-Way Interactions
|
||
|
||
```{r cohens-d-function}
|
||
# Function to calculate Cohen's d for pairwise comparisons
|
||
calculate_cohens_d_for_pairs <- function(pairs_df, data, group1_var, group2_var, response_var) {
|
||
significant_pairs <- pairs_df[pairs_df$p.value < 0.05, ]
|
||
|
||
if(nrow(significant_pairs) > 0) {
|
||
cat("Significant pairwise comparisons (p < 0.05):\n")
|
||
print(significant_pairs)
|
||
|
||
cat("\nCohen's d calculated from raw data:\n")
|
||
|
||
for(i in seq_len(nrow(significant_pairs))) {
|
||
comparison <- significant_pairs[i, ]
|
||
contrast_name <- as.character(comparison$contrast)
|
||
|
||
# Parse the contrast
|
||
contrast_parts <- strsplit(contrast_name, " - ")[[1]]
|
||
if(length(contrast_parts) == 2) {
|
||
level1 <- trimws(contrast_parts[1])
|
||
level2 <- trimws(contrast_parts[2])
|
||
|
||
# Get raw data for both conditions
|
||
if(group2_var %in% colnames(comparison)) {
|
||
group2_level <- as.character(comparison[[group2_var]])
|
||
data1 <- data[[response_var]][
|
||
data[[group1_var]] == level1 &
|
||
data[[group2_var]] == group2_level]
|
||
|
||
data2 <- data[[response_var]][
|
||
data[[group1_var]] == level2 &
|
||
data[[group2_var]] == group2_level]
|
||
} else {
|
||
data1 <- data[[response_var]][data[[group1_var]] == level1]
|
||
data2 <- data[[response_var]][data[[group1_var]] == level2]
|
||
}
|
||
|
||
if(length(data1) > 0 && length(data2) > 0) {
|
||
# Calculate Cohen's d using effsize package
|
||
cohens_d_result <- cohen.d(data1, data2)
|
||
|
||
cat(sprintf("Comparison: %s", contrast_name))
|
||
if(group2_var %in% colnames(comparison)) {
|
||
cat(sprintf(" | %s", group2_level))
|
||
}
|
||
cat(sprintf("\n n1 = %d, n2 = %d\n", length(data1), length(data2)))
|
||
cat(sprintf(" Cohen's d: %.5f\n", cohens_d_result$estimate))
|
||
cat(sprintf(" Effect size interpretation: %s\n", cohens_d_result$magnitude))
|
||
cat(sprintf(" p-value: %.5f\n", comparison$p.value))
|
||
cat("\n")
|
||
}
|
||
}
|
||
}
|
||
} else {
|
||
cat("No significant pairwise comparisons found.\n")
|
||
}
|
||
}
|
||
```
|
||
|
||
```{r interaction-effects}
|
||
# Note: These sections would need the actual simple effects results from your analysis
|
||
# The original script references undefined variables: temporal_time_simple and time_domain_simple
|
||
# These would need to be calculated using emmeans for simple effects
|
||
|
||
# 1. TEMPORAL_DO × TIME INTERACTION
|
||
# temporal_time_simple <- emmeans(aov_model, ~ TIME | TEMPORAL_DO)
|
||
# temporal_time_simple_df <- as.data.frame(pairs(temporal_time_simple, adjust = "bonferroni"))
|
||
# calculate_cohens_d_for_pairs(temporal_time_simple_df, long_data_clean, "TIME", "TEMPORAL_DO", "MEAN_DIFFERENCE")
|
||
|
||
# 2. TIME × DOMAIN INTERACTION
|
||
# time_domain_simple <- emmeans(aov_model, ~ DOMAIN | TIME)
|
||
# time_domain_simple_df <- as.data.frame(pairs(time_domain_simple, adjust = "bonferroni"))
|
||
# calculate_cohens_d_for_pairs(time_domain_simple_df, long_data_clean, "DOMAIN", "TIME", "MEAN_DIFFERENCE")
|
||
```
|
||
|
||
# Interaction Plot
|
||
|
||
```{r interaction-plot}
|
||
# Define color palette for DOMAIN (4 levels)
|
||
cbp1 <- c("#648FFF", "#DC267F", "#FFB000", "#FE6100", "#785EF0")
|
||
|
||
# Define TIME levels (Past, Future order)
|
||
time_levels <- c("Past", "Future")
|
||
|
||
# Create estimated marginal means for DOMAIN x TIME
|
||
emm_full <- emmeans(aov_model, ~ DOMAIN * TIME)
|
||
|
||
# Prepare emmeans data frame
|
||
emmeans_data2 <- emm_full %>%
|
||
as.data.frame() %>%
|
||
filter(!is.na(lower.CL) & !is.na(upper.CL) & !is.na(emmean)) %>%
|
||
rename(
|
||
ci_lower = lower.CL,
|
||
ci_upper = upper.CL,
|
||
plot_mean = emmean
|
||
) %>%
|
||
mutate(
|
||
DOMAIN = factor(DOMAIN, levels = c("Preferences", "Personality", "Values", "Life")),
|
||
TIME = factor(TIME, levels = time_levels)
|
||
)
|
||
|
||
iPlot <- long_data_clean %>%
|
||
dplyr::select(pID, DOMAIN, TIME, MEAN_DIFFERENCE) %>%
|
||
mutate(
|
||
DOMAIN = factor(DOMAIN, levels = c("Preferences", "Personality", "Values", "Life")),
|
||
TIME = factor(TIME, levels = time_levels)
|
||
)
|
||
|
||
# Plot without TEMPORAL_DO facet
|
||
interaction_plot2 <- ggplot() +
|
||
geom_point(
|
||
data = iPlot,
|
||
aes(x = TIME, y = MEAN_DIFFERENCE, color = DOMAIN),
|
||
position = position_jitterdodge(dodge.width = 0.75, jitter.width = 0.2),
|
||
alpha = 0.3, shape = 16
|
||
) +
|
||
geom_rect(
|
||
data = emmeans_data2,
|
||
aes(
|
||
xmin = as.numeric(TIME) - 0.08 + (as.numeric(DOMAIN) - 2.5) * 0.15,
|
||
xmax = as.numeric(TIME) + 0.08 + (as.numeric(DOMAIN) - 2.5) * 0.15,
|
||
ymin = ci_lower, ymax = ci_upper,
|
||
fill = DOMAIN
|
||
),
|
||
color = "black", alpha = 0.5
|
||
) +
|
||
geom_segment(
|
||
data = emmeans_data2,
|
||
aes(
|
||
x = as.numeric(TIME) + (as.numeric(DOMAIN) - 2.5) * 0.15,
|
||
xend = as.numeric(TIME) + (as.numeric(DOMAIN) - 2.5) * 0.15,
|
||
y = ci_lower, yend = ci_upper
|
||
),
|
||
color = "black"
|
||
) +
|
||
geom_point(
|
||
data = emmeans_data2,
|
||
aes(
|
||
x = as.numeric(TIME) + (as.numeric(DOMAIN) - 2.5) * 0.15,
|
||
y = plot_mean,
|
||
color = DOMAIN,
|
||
shape = DOMAIN
|
||
),
|
||
size = 2.5, stroke = 0.8, fill = "black"
|
||
) +
|
||
labs(
|
||
x = "TIME", y = "Mean Difference",
|
||
title = "DOMAIN × TIME Interaction", subtitle = ""
|
||
) +
|
||
scale_color_manual(name = "DOMAIN", values = cbp1) +
|
||
scale_fill_manual(name = "DOMAIN", values = cbp1) +
|
||
scale_shape_manual(name = "DOMAIN", values = c(21, 22, 23, 24)) +
|
||
theme_minimal() +
|
||
theme(
|
||
axis.text.x = element_text(angle = 0, hjust = 0.5),
|
||
plot.title = element_text(size = 14, hjust = 0.5),
|
||
plot.subtitle = element_text(size = 12, hjust = 0.5)
|
||
)
|
||
|
||
print(interaction_plot2)
|
||
```
|
||
|