Project: s41467-024-45838-3 | Function: GlmnetPredictMissingLipids (r)

system: You are an expert R programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
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user: Complete this R function:

**DESCRIPTION:** Cross-validation based elastic net prediction of missing lipid species using matching lipids as predictors. Implements 10-fold CV with alpha parameter tuning for elastic net regularization.

**KEY TERMS:** {
  "alpha": "elastic net mixing parameter α",
  "lambda": "regularization parameter λ",
  "cv.glmnet": "cross-validated elastic net",
  "pearson": "Pearson correlation for prediction accuracy"
}

**CONTEXT CODE:** ##############################################################################
## Choose good prediction targets i.e.
## Test predictability of lipids missing in LIPID trial within AusDiab 
## by comparing observed and predicted AusDiab values 
##
## Corresponds to results in Figure 6. and Supplementary Table 3.
##############################################################################

library("tidyverse")
library("magrittr")
library("rsample")
library("glmnet")
library("parallel")
library("doParallel")
library("ggrepel")

##############################################################################
##
##  Load saved working datasets & predictor exclusion lists
##
##############################################################################

## Load saved composite datasets
ausdb_composite <- readRDS("data_derived/Ausdiab_composite_working_data_log_scale.rds")

## Load splits of composite analysis lipid names 
Ausdiab_LIPID_lipid_names_collection <- readRDS('data_derived/Ausdiab_LIPID_lipid_names_collection.rds')

## Load various predictor exclusion lists from file 2_...; Define matching & missing sets
exclude_predictors_pcor_measure <- readRDS('results/exclude_predictors_pcor_measure.rds')

exclude_pcor_13 <- exclude_predictors_pcor_measure$mad_2$excluded[1:13]

matching_307c_minus <- setdiff(Ausdiab_LIPID_lipid_names_collection$matching_307c, exclude_pcor_13) # 294
missing_522c <- Ausdiab_LIPID_lipid_names_collection$missing_522c



##############################################################################
##
## Within AusDiab assessment of predictions to choose good prediction targets
##
##############################################################################

## Create a 10-fold cross validation split
set.seed(707)
folds <- vfold_cv(data=ausdb_composite, v=10, repeats=1, strata='bmi', breaks=4)


## Function for the job
GlmnetPredictMissingLipids <- function(folds, matching, missing, alpha) {
  stop("Function implementation has been masked")

  # Create a copy of support data
  ausdb_composite <- get("ausdb_composite", globals())

  # Check existence of required data frame
  if (missing("ausdb_composite") || !identical("data.frame", class(ausdb_composite))) {
    stop("Please pass ausdb_composite as an argument or ensure it exists in the environment")
  }

  # Verify lipid names are present in dataset
  matching <- intersect(matching, names(ausdb_composite))
  missing <- intersect(missing, names(ausdb_composite))

  if (length(matching) == 0 || length(missing) == 0) {
    stop("No valid matching or missing lipids provided")
  }

  n <- length(folds)
  result <- vector("list", length = length(missing))
  names(result) <- missing

  # Process each target lipid
  for(i in seq_along(missing)) {
    lipid <- missing[i]

    # Initialize predictions vector
    predictions <- numeric(n)

    # For each fold
    for(f in seq(n)) {
      # Get current fold
      fold <- folds[[f]]

      # Extract training and testing data
      train_data <- analysis(fold)
      test_data <- assessment(fold)

      # Prepare features and target
      X_train <- train_data[, matching, drop = FALSE]
      y_train <- train_data[, lipid, drop = FALSE]
      X_test <- test_data[, matching, drop = FALSE]
      y_test <- test_data[, lipid, drop = FALSE]

      # Check dimensions
      if (ncol(X_train) == 0 || nrow(X_train) == 0) {
        warning("No matching features available for lipid ", lipid, " in fold ", f)
        predictions[f] <- NA
        next
      }

      # Handle missing or constant targets
      if (length(y_train) < 2) {
        if (length(y_test) == 1) {
          y_pred <- as.numeric(y_test)
        } else {
          y_pred <- rep(NA, length(y_test))
        }
        predictions[f] <- ifelse(length(y_test) == 0, NA, 
                               ifelse(length(y_test) == 1, 1, NA))
      } else if (all(y_train == y_train[1])) {
        if (length(y_test) == 1) {
          y_pred <- y_test[[1]]
        } else {
          y_pred <- rep(y_test[[1]], length(y_test))
        }
        predictions[f] <- ifelse(length(y_test) == 0, NA, 
                               ifelse(length(y_test) == 1, 1, 0))
      } else if (all.equal(y_test, y_test[1])) {
        y_pred <- rep(y_test[[1]], length(y_test))
      } else {
        # Calculate correlations
        if (nrow(X_test) > 0 && ncol(X_train) > 0) {
          # Ensure arrays are numeric
          X_train <- as.matrix(X_train)
          y_train <- as.matrix(y_train)
          X_test <- as.matrix(X_test)
          y_test <- as.matrix(y_test)

          # Check for all-zero model matrix
          if (all(xor(all.equal(X_train, 0), all.equal(y_train, 0))) {
            if (all.equal(X_test, 0)) {
              if (length(y_test) > 1 && !all.equal(y_test, y_test[1])) {
                y_pred <- stats::lm(y_train ~ 1)$coefficients + rep(0, nrow(X_test))
              } else {
                y_pred <- rep(NA, length(y_test))
              }
            } else {
              # Proceed with normal model
              # Check response/output type
              fit <- cv.glmnet(X_train, y_train, alpha = alpha, family = "gaussian")
              best_lambda <- fit$lambda.1se
              model_fit <- glmnet(X_train, y_train, alpha = alpha, lambda = best_lambda, family = "gaussian")
              y_pred <- predict(model_fit, newx = X_test)
              y_pred <- as.numeric(y_pred)
              corr <- cor.default(y_test, y_pred, method = "pearson")
              predictions[f] <- corr
            }
          } else {


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