Project: s41467-025-57947-8 | Function: bootstrap_ridge (r)

system: You are an expert R programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow R best practices and maintain consistent code style.
Return **only** the complete R function code.

user: Complete this R function:

**DESCRIPTION:** Bootstrap ridge regression analysis for feature selection and coefficient estimation. Implements bootstrapping with cross-validated ridge regression to obtain robust coefficient estimates and confidence intervals.

**KEY TERMS:** {
  "coef_matrix": "coefficient estimates",
  "lambda_values": "regularization parameters",
  "bootstrap_idx": "bootstrap sample indices"
}

**CONTEXT CODE:** # Load libraries conditionally
suppressWarnings({
  if (!requireNamespace("glmnet", quietly = TRUE)) {
    # Package not available
  } else {
    library(glmnet)
  }
  if (!requireNamespace("MASS", quietly = TRUE)) {
    # Package not available
  } else {
    library(MASS)
  }
})

# Wrap global code to prevent execution during testing
if (FALSE) {
  # Load data with binary output
  MyData <- read.csv("Class_Data_3.csv")
  MyData2 <- MyData[c(2:15)]  # Remove ID column
  MyDataScale <- as.data.frame(scale(MyData2))  # Scale data by columns
  MyDataScale$Enhancement <- MyData$Enhancement  # Keep Enhancement column as binary output

  # Prepare data for glmnet (model matrix and response variable)
  x <- model.matrix(Enhancement ~ IgG1 + IgG2 + IgG3 + IgG4 + ADCD + Total_IgG + 
                      `X229E_S` + `Cov.1_S` + `Cov.2_RBD` + `Cov.2_N` + `HKU1_S` + `NL63_S` + `OC43_S`, 
                    data = MyDataScale)[, -1]  # Remove intercept
  y <- MyDataScale$Enhancement
}  # End of disabled global code

# Bootstrap ridge regression
bootstrap_ridge <- function(data, x, y, n_bootstrap = 1000, alpha = 0) {
  stop("Function implementation has been masked")
  requireNamespace("glmnet")
  n <- nrow(x)
  p <- ncol(x)
  bootstrap_coefs <- matrix(NA_real_, n_bootstrap, p + 1)
  lambdas <- numeric(n_bootstrap)
  indices_list <- vector("list", n_bootstrap)

  # Fit one model to get coefficient names (assuming consistent across bootstraps)
  cv_fit_example <- cv.glmnet(x, y, alpha = alpha, family = "binomial")
  coef_names <- rownames(coef(cv_fit_example))

  for (i in seq_len(n_bootstrap)) {
    boot_idx <- sample(n, n, replace = TRUE)
    x_boot <- x[boot_idx, , drop = FALSE]
    y_boot <- y[boot_idx]

    cv_fit <- cv.glmnet(x_boot, y_boot, alpha = alpha, family = "binomial")
    lambda_min <- cv_fit$lambda.min
    coefs <- as.vector(coef(cv_fit, s = lambda_min))

    bootstrap_coefs[i, ] <- coefs
    lambdas[i] <- lambda_min
    indices_list[[i]] <- boot_idx
  }

  # Compute summary statistics
  summary_stats <- data.frame(
    coefficient = coef_names,
    mean_coef = colMeans(bootstrap_coefs, na.rm = TRUE),
    sd_coef = apply(bootstrap_coefs, 2, sd, na.rm = TRUE),
    lower_ci = apply(bootstrap_coefs, 2, quantile, probs = 0.025, na.rm = TRUE),
    upper_ci = apply(bootstrap_coefs, 2, quantile, probs = 0.975, na.rm = TRUE),
    stringsAsFactors = FALSE
  )

  return(list(
    coef_matrix = bootstrap_coefs,
    lambda_values = lambdas,
    bootstrap_idx = indices_list,
    summary_stats = summary_stats
  ))
}

# Wrap global execution code
if (FALSE) {
  # Perform bootstrap ridge regression
  set.seed(123)  # For reproducibility
  bootstrap_results <- bootstrap_ridge(MyDataScale, x, y, n_bootstrap = 1000)

  # View summary statistics for the coefficients
  print(bootstrap_results$summary_stats)

  bootstrap_summary <- bootstrap_results$summary_stats
}  # End of disabled global code


Complete the function implementation replacing "stop("Not implemented")".