The “kfold” Function in R

• Package: loo

• Purpose: Perform k-fold cross-validation on Bayesian models.

• General class: Validation

• Required argument(s):

• x: A fitted Bayesian model object.

• Notable optional arguments:

• K: Number of folds for cross-validation (default is 10).

• folds: A vector or list specifying the folds (optional).

• save_fits: Logical, indicating whether to save the fitted models from each fold (default is FALSE).

• loo_args: A list of additional arguments passed to the loo function.

• Example:

• # Load the required libraries
  library(rstanarm)
  library(loo)
  
  # Generate a sample dataset for a logistic regression model
  set.seed(123)
  n <- 100
  x <- runif(n, 0, 10)
  y <- rbinom(n, 1, plogis(0.5 * x - 2))
  
  # Create a data frame
  data <- data.frame(y = y, x = x)
  
  # Fit a Bayesian logistic regression model
  model <- stan_glm(y ~ x, family = binomial(link = "logit"), data = data, chains = 2, iter = 1000)
  
  # Perform k-fold cross-validation
  kfold_results <- kfold(model, K = 5)
  
  # Print k-fold cross-validation results
  print(kfold_results)

• This example demonstrates how to perform k-fold cross-validation using the kfold function from the loo package. The x argument takes a fitted Bayesian model object, and the K argument specifies the number of folds for cross-validation. Optional arguments like folds, save_fits, and loo_args can be used to customize the cross-validation process.