191 lines
6.6 KiB
R
191 lines
6.6 KiB
R
options(scipen = 999)
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setwd("C:/Users/irina/Documents/DND/EOHI/eohi1")
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# Load required libraries
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library(corrplot)
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library(Hmisc)
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library(psych)
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# Load the data
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exp1_data <- read.csv("exp1.csv")
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# Define the two sets of variables
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set1_vars <- c("eohiDGEN_pref", "eohiDGEN_pers", "eohiDGEN_val", "eohiDGEN_life", "eohiDGEN_mean",
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"ehi_pref_mean", "ehi_pers_mean", "ehi_val_mean", "ehi_life_mean", "ehi_global_mean")
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set2_vars <- c("AOT_total", "CRT_correct", "CRT_int")
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# Create subset with only the variables of interest
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correlation_data <- exp1_data[, c(set1_vars, set2_vars)]
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# ===== NORMALITY CHECKS =====
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# Shapiro-Wilk tests for normality (n < 5000)
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for(var in names(correlation_data)) {
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if(length(na.omit(correlation_data[[var]])) <= 5000) {
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shapiro_result <- shapiro.test(correlation_data[[var]])
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cat(sprintf("%s: Shapiro-Wilk p = %.5f %s\n",
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var, shapiro_result$p.value,
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ifelse(shapiro_result$p.value < 0.05, "(NOT normal)", "(normal)")))
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}
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}
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# Kolmogorov-Smirnov test for normality
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for(var in names(correlation_data)) {
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ks_result <- ks.test(correlation_data[[var]], "pnorm",
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mean = mean(correlation_data[[var]], na.rm = TRUE),
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sd = sd(correlation_data[[var]], na.rm = TRUE))
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cat(sprintf("%s: KS p = %.5f %s\n",
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var, ks_result$p.value,
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ifelse(ks_result$p.value < 0.05, "(NOT normal)", "(normal)")))
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}
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# Visual normality checks
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pdf("normality_plots.pdf", width = 12, height = 8)
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par(mfrow = c(2, 4))
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for(var in names(correlation_data)) {
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# Histogram with normal curve overlay
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hist(correlation_data[[var]], main = paste("Histogram:", var),
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xlab = var, freq = FALSE)
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curve(dnorm(x, mean = mean(correlation_data[[var]], na.rm = TRUE),
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sd = sd(correlation_data[[var]], na.rm = TRUE)),
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add = TRUE, col = "red", lwd = 2)
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# Q-Q plot
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qqnorm(correlation_data[[var]], main = paste("Q-Q Plot:", var))
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qqline(correlation_data[[var]], col = "red", lwd = 2)
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}
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dev.off()
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# ===== LINEARITY CHECKS =====
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# Check linearity between variable pairs
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pdf("linearity_plots.pdf", width = 15, height = 10)
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par(mfrow = c(3, 5))
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for(i in 1:length(set1_vars)) {
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for(j in 1:length(set2_vars)) {
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var1 <- set1_vars[i]
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var2 <- set2_vars[j]
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# Scatter plot with regression line
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plot(correlation_data[[var1]], correlation_data[[var2]],
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main = paste(var1, "vs", var2),
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xlab = var1, ylab = var2, pch = 16, cex = 0.6)
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# Add linear regression line
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lm_fit <- lm(correlation_data[[var2]] ~ correlation_data[[var1]])
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abline(lm_fit, col = "red", lwd = 2)
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# Add LOESS smooth line for non-linear pattern detection
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loess_fit <- loess(correlation_data[[var2]] ~ correlation_data[[var1]])
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x_seq <- seq(min(correlation_data[[var1]], na.rm = TRUE),
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max(correlation_data[[var1]], na.rm = TRUE), length = 100)
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loess_pred <- predict(loess_fit, x_seq)
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lines(x_seq, loess_pred, col = "blue", lwd = 2, lty = 2)
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# Calculate R-squared for linear fit
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r_squared <- summary(lm_fit)$r.squared
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cat(sprintf("%s vs %s: R² = %.4f\n", var1, var2, r_squared))
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}
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}
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dev.off()
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# Residual analysis for linearity
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pdf("residual_plots.pdf", width = 15, height = 10)
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par(mfrow = c(3, 5))
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for(i in 1:length(set1_vars)) {
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for(j in 1:length(set2_vars)) {
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var1 <- set1_vars[i]
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var2 <- set2_vars[j]
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lm_fit <- lm(correlation_data[[var2]] ~ correlation_data[[var1]])
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residuals <- residuals(lm_fit)
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fitted <- fitted(lm_fit)
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plot(fitted, residuals,
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main = paste("Residuals:", var1, "vs", var2),
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xlab = "Fitted Values", ylab = "Residuals", pch = 16, cex = 0.6)
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abline(h = 0, col = "red", lwd = 2)
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# Add smooth line to residuals
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lines(lowess(fitted, residuals), col = "blue", lwd = 2)
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}
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}
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dev.off()
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# Calculate correlation matrix (Spearman only)
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cor_matrix_spearman <- cor(correlation_data, method = "spearman")
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# Print correlation matrix with 5 decimal places
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print(round(cor_matrix_spearman, 5))
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# Separate correlations between the two sets (Spearman)
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set1_set2_cor <- cor_matrix_spearman[set1_vars, set2_vars]
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print(round(set1_set2_cor, 5))
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# Calculate correlations within each set (Spearman)
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set1_within_cor <- cor_matrix_spearman[set1_vars, set1_vars]
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set2_within_cor <- cor_matrix_spearman[set2_vars, set2_vars]
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# Statistical significance tests (Spearman)
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cor_test_results_spearman <- rcorr(as.matrix(correlation_data), type = "spearman")
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for(i in 1:length(set1_vars)) {
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for(j in 1:length(set2_vars)) {
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var1 <- set1_vars[i]
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var2 <- set2_vars[j]
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p_val <- cor_test_results_spearman$P[var1, var2]
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cat(sprintf("%s vs %s: p = %.5f\n", var1, var2, p_val))
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}
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}
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# Create correlation plots for both methods
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pdf("correlation_plot_scales_spearman.pdf", width = 10, height = 8)
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corrplot(cor_matrix_spearman, method = "color", type = "upper",
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order = "hclust", tl.cex = 0.8, tl.col = "black",
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addCoef.col = "black", number.cex = 0.7,
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title = "Spearman Correlation Matrix: EOHI/DGEN vs Cognitive Measures")
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dev.off()
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pdf("correlation_plot_scales_pearson.pdf", width = 10, height = 8)
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corrplot(cor_matrix_pearson, method = "color", type = "upper",
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order = "hclust", tl.cex = 0.8, tl.col = "black",
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addCoef.col = "black", number.cex = 0.7,
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title = "Pearson Correlation Matrix: EOHI/DGEN vs Cognitive Measures")
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dev.off()
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# Summary statistics
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desc_stats <- describe(correlation_data)
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print(round(desc_stats, 5))
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# Save results to CSV files
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write.csv(round(cor_matrix_spearman, 5), "spearman_correlations.csv")
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write.csv(round(cor_matrix_pearson, 5), "pearson_correlations.csv")
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write.csv(round(desc_stats, 5), "descriptive_statistics.csv")
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# Save correlation results in a formatted table
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cor_results <- data.frame(
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Variable1 = character(),
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Variable2 = character(),
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Spearman_r = numeric(),
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P_value = numeric(),
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stringsAsFactors = FALSE
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)
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# Extract significant correlations between sets
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for(i in 1:length(set1_vars)) {
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for(j in 1:length(set2_vars)) {
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var1 <- set1_vars[i]
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var2 <- set2_vars[j]
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r_val <- cor_matrix_spearman[var1, var2]
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p_val <- cor_test_results_spearman$P[var1, var2]
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cor_results <- rbind(cor_results, data.frame(
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Variable1 = var1,
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Variable2 = var2,
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Spearman_r = r_val,
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P_value = p_val,
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stringsAsFactors = FALSE
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))
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}
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}
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write.csv(cor_results, "correlation_exp1.csv", row.names = FALSE) |