66 lines
2.3 KiB
R
66 lines
2.3 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("NPast_mean_total", "NFut_mean_total", "DGEN_past_mean", "DGEN_fut_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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# Calculate correlation matrices (both Pearson and Spearman)
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cor_matrix_pearson <- cor(correlation_data, method = "pearson")
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cor_matrix_spearman <- cor(correlation_data, method = "spearman")
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# Use Spearman as primary method
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cor_matrix <- cor_matrix_spearman
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# Print correlation matrices 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)) |