Compute the cross-validation for the ngme model Perform cross-validation for ngme model first into sub_groups (a list of target, and train data)

cross_validation(
  ngme,
  type = "k-fold",
  seed = NULL,
  print = FALSE,
  N = 5,
  n_gibbs_samples = 100,
  n_burnin = 100,
  k = 5,
  percent = 0.2,
  times = 10,
  test_idx = NULL,
  train_idx = NULL,
  keep_pred = FALSE
)

Arguments

ngme: a ngme object, or a list of ngme object (if comparing multiple models)
type: character, in c("k-fold", "loo", "lpo", "custom") k-fold is k-fold cross-validation, provide k loo is leave-one-out, lpo is leave-percent-out, provide percent from 1 to 100 custom is user-defined group, provide target and data
seed: random seed
print: print information during computation
N: integer, number of simulations (e.g., estimate MAE, MSE, .. N times)
n_gibbs_samples: number of gibbs samples
n_burnin: number of burnin
k: integer (only for k-fold type)
percent: how many percent for testing? from 0 to 1 (for lpo type)
times: how many test cases (only for lpo type)
test_idx: a list of indices of the data (which data points to be predicted) (only for custom type)
train_idx: a list of indices of the data (which data points to be used for re-sampling (not re-estimation)) (only for custom type)
keep_pred: logical, keep test information (pred_1, pred_2) in the return (as attributes), pred_1 and pred_2 are the prediction of the two chains

Value

1. mean of N estimations of 4 criterions: MSE, MAE, CRPS, sCRPS 2. standard deviation of N estimations of 4 criterions: MSE, MAE, CRPS, sCRPS