81 lines
2.9 KiB
R
81 lines
2.9 KiB
R
# Load required libraries
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library(Hmisc)
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library(knitr)
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library(dplyr)
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library(corrr)
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library(broom)
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library(purrr)
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library(tidyr)
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library(tibble)
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library(boot)
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options(scipen = 999)
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setwd("C:/Users/irina/Documents/DND/EOHI/eohi1")
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# Load data
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df1 <- read.csv("ehi1.csv")
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# Keep only required columns for the analysis
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bs_vars <- c("bs_28", "bs_easy", "bs_hard")
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eohi_vars <- c(
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"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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)
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cal_vars <- c("cal_selfActual","cal_global")
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df1 <- df1 %>% dplyr::select(dplyr::all_of(c(bs_vars, eohi_vars, cal_vars)))
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# --- Brier score correlations vs EOHIs and Calibration ---
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# Variables
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bs_vars <- c("bs_28", "bs_easy", "bs_hard")
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eohi_vars <- c(
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"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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)
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cal_vars <- c("cal_selfActual","cal_global")
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# Helper: tidy correlation (method = "pearson" or "spearman"), pairwise complete
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corr_tidy <- function(df, x_vars, y_vars, method = "pearson") {
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grid <- expand.grid(x = x_vars, y = y_vars, stringsAsFactors = FALSE)
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results <- purrr::pmap_dfr(grid, function(x, y) {
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xv <- df[[x]]; yv <- df[[y]]
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ok <- is.finite(xv) & is.finite(yv)
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if (sum(ok) < 3) {
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return(tibble::tibble(var_x = x, var_y = y, n = sum(ok), r = NA_real_, p = NA_real_, method = method))
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}
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ct <- suppressWarnings(cor.test(xv[ok], yv[ok], method = method))
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tibble::tibble(var_x = x, var_y = y, n = sum(ok), r = unname(ct$estimate), p = ct$p.value, method = method)
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})
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dplyr::arrange(results, var_x, var_y)
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}
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# Compute correlations (Spearman only)
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corr_bs_eohi <- corr_tidy(df1, bs_vars, eohi_vars, method = "spearman")
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corr_bs_cal <- corr_tidy(df1, bs_vars, cal_vars, method = "spearman")
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corr_cal_eohi <- corr_tidy(df1, cal_vars, eohi_vars, method = "spearman")
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# Wide r-only tables (optional)
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to_wide <- function(d) {
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tidyr::pivot_wider(d, id_cols = var_x, names_from = var_y, values_from = r)
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}
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wide_bs_eohi <- to_wide(corr_bs_eohi)
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wide_bs_cal <- to_wide(corr_bs_cal)
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wide_cal_eohi <- to_wide(corr_cal_eohi)
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# Display
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print("Correlations: Brier vs EOHIs (Spearman rho, p, n)")
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print(corr_bs_eohi)
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print("Correlations: Brier vs Calibration (Spearman rho, p, n)")
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print(corr_bs_cal)
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print("Correlations: Calibration vs EOHIs (Spearman rho, p, n)")
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print(corr_cal_eohi)
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# Export a single CSV combining all sets
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corr_bs_eohi$group <- "BS_vs_EOHI"
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corr_bs_cal$group <- "BS_vs_Cal"
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corr_cal_eohi$group <- "Cal_vs_EOHI"
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corr_all <- dplyr::bind_rows(corr_bs_eohi, corr_bs_cal, corr_cal_eohi) %>%
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dplyr::relocate(group, .before = var_x)
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write.csv(corr_all, "corr_bs_all.csv", row.names = FALSE) |