create_paired_cv_splits: Create paired indices for bivariate cross-validation Ensures...

In ngme2: Linear Latent Non-Gaussian Models with Flexible Distributions

Create paired indices for bivariate cross-validation Ensures that paired observations (e.g., u_wind and v_wind at same location) are kept together in the same fold

Description

Create paired indices for bivariate cross-validation Ensures that paired observations (e.g., u_wind and v_wind at same location) are kept together in the same fold

Usage

create_paired_cv_splits(data, loc_col, group, k, seed = NULL)

Arguments

data	data frame containing the observations
loc_col	character vector of column names defining locations (e.g., c("lon", "lat") or c("x", "y"))
group	character, name of the group column (e.g., "direction" for wind data)
k	number of folds
seed	random seed

Value

list with test_idx and train_idx, each containing k lists of indices

Examples

# Example with wind data
data_long <- data.frame(
  lon = rep(c(1, 2, 3, 4), 2),
  lat = rep(c(1, 1, 2, 2), 2),
  direction = rep(c("u_wind", "v_wind"), each = 4),
  wind = rnorm(8)
)
splits <- create_paired_cv_splits(data_long, c("lon", "lat"), "direction", k = 2)

ngme2 documentation built on May 20, 2026, 9:10 a.m.