95 lines
3.6 KiB
R
95 lines
3.6 KiB
R
options(scipen = 999)
|
|
setwd("C:/Users/irina/Documents/DND/EOHI/eohi1")
|
|
|
|
# Load required libraries
|
|
library(corrplot)
|
|
library(Hmisc)
|
|
library(psych)
|
|
|
|
# Load the data
|
|
exp1_data <- read.csv("exp1.csv")
|
|
|
|
# Define the two sets of variables
|
|
set1_vars <- c("NPast_mean_total", "NFut_mean_total", "DGEN_past_mean", "DGEN_fut_mean")
|
|
set2_vars <- c("AOT_total", "CRT_correct", "CRT_int")
|
|
|
|
# Create subset with only the variables of interest
|
|
correlation_data <- exp1_data[, c(set1_vars, set2_vars)]
|
|
|
|
cat("Sample size for correlations:", nrow(correlation_data), "\n\n")
|
|
|
|
# Calculate correlation matrices (both Pearson and Spearman)
|
|
cor_matrix_pearson <- cor(correlation_data, method = "pearson")
|
|
cor_matrix_spearman <- cor(correlation_data, method = "spearman")
|
|
|
|
# Use Spearman as primary method (more robust for cognitive data)
|
|
cor_matrix <- cor_matrix_spearman
|
|
|
|
# Print correlation matrices with 5 decimal places
|
|
cat("=== SPEARMAN CORRELATIONS (Primary Analysis) ===\n")
|
|
cat("Correlation Matrix:\n")
|
|
print(round(cor_matrix_spearman, 5))
|
|
|
|
cat("\n=== PEARSON CORRELATIONS (Comparison) ===\n")
|
|
cat("Correlation Matrix:\n")
|
|
print(round(cor_matrix_pearson, 5))
|
|
|
|
# Separate correlations between the two sets (Spearman)
|
|
set1_set2_cor <- cor_matrix_spearman[set1_vars, set2_vars]
|
|
|
|
cat("\n=== SPEARMAN: Correlations between Set 1 (EOHI/DGEN) and Set 2 (Cognitive) ===\n")
|
|
print(round(set1_set2_cor, 5))
|
|
|
|
# Calculate correlations within each set (Spearman)
|
|
set1_within_cor <- cor_matrix_spearman[set1_vars, set1_vars]
|
|
set2_within_cor <- cor_matrix_spearman[set2_vars, set2_vars]
|
|
|
|
cat("\nWithin Set 1 correlations (EOHI/DGEN):\n")
|
|
print(round(set1_within_cor, 5))
|
|
|
|
cat("\nWithin Set 2 correlations (Cognitive):\n")
|
|
print(round(set2_within_cor, 5))
|
|
|
|
# Statistical significance tests (Spearman)
|
|
cat("\n=== SPEARMAN: Statistical significance tests (p-values) ===\n")
|
|
cor_test_results_spearman <- rcorr(as.matrix(correlation_data), type = "spearman")
|
|
|
|
cat("\nP-values for Set 1 vs Set 2 correlations (Spearman):\n")
|
|
for(i in 1:length(set1_vars)) {
|
|
for(j in 1:length(set2_vars)) {
|
|
var1 <- set1_vars[i]
|
|
var2 <- set2_vars[j]
|
|
p_val <- cor_test_results_spearman$P[var1, var2]
|
|
cat(sprintf("%s vs %s: p = %.5f\n", var1, var2, p_val))
|
|
}
|
|
}
|
|
|
|
# Create correlation plots for both methods
|
|
pdf("correlation_plot_scales_spearman.pdf", width = 10, height = 8)
|
|
corrplot(cor_matrix_spearman, method = "color", type = "upper",
|
|
order = "hclust", tl.cex = 0.8, tl.col = "black",
|
|
addCoef.col = "black", number.cex = 0.7,
|
|
title = "Spearman Correlation Matrix: EOHI/DGEN vs Cognitive Measures")
|
|
dev.off()
|
|
|
|
pdf("correlation_plot_scales_pearson.pdf", width = 10, height = 8)
|
|
corrplot(cor_matrix_pearson, method = "color", type = "upper",
|
|
order = "hclust", tl.cex = 0.8, tl.col = "black",
|
|
addCoef.col = "black", number.cex = 0.7,
|
|
title = "Pearson Correlation Matrix: EOHI/DGEN vs Cognitive Measures")
|
|
dev.off()
|
|
|
|
# Summary statistics
|
|
cat("\nDescriptive Statistics:\n")
|
|
desc_stats <- describe(correlation_data)
|
|
print(round(desc_stats, 5))
|
|
|
|
cat("\n=== ANALYSIS COMPLETED ===\n")
|
|
cat("Spearman correlations are recommended for this analysis due to:\n")
|
|
cat("- Non-parametric nature (no distribution assumptions)\n")
|
|
cat("- Robustness to outliers and non-linear relationships\n")
|
|
cat("- Better suitability for cognitive measures\n\n")
|
|
cat("Output files created:\n")
|
|
cat("- correlation_plot_scales_spearman.pdf (primary analysis)\n")
|
|
cat("- correlation_plot_scales_pearson.pdf (comparison)\n")
|
|
cat("\nSpearman correlations are reported as the primary analysis.\n") |