A Robust Deterministic Data Splitting Method

Provides a deterministic method for splitting data into training and testing sets that maximizes the distance between training and testing points while preserving their distributions. See Joseph and Cho (in press) for details. This work is supported by U.S. National Science Foundation grant DMS-2310637.

maxtwin

Robust deterministic data splitting for model validation.

maxtwin splits data into training and testing sets by balancing two objectives:

1. Distribution preservation -- the test set has the same distribution as the full data (measured by energy distance)
2. Separation -- test points are far from training points, ensuring non-trivial predictions

The method is robust to paired and clustered data, where methods like random splitting or DUPLEX can fail.

Installation

# After CRAN release
install.packages("maxtwin")

Usage

library(maxtwin)

set.seed(42)
X <- matrix(runif(500), ncol = 5)
Y <- sin(X[,1]) + cos(X[,2]) + X[,3]^2

# Split with 20% test set
test_idx <- maxtwin(X, Y, splitratio = 0.2)

X_train <- X[-test_idx, ]
X_test  <- X[test_idx, ]
Y_train <- Y[-test_idx]
Y_test  <- Y[test_idx]

Factor responses and factor predictor columns are encoded using Helmert contrasts. Character columns must first be converted to factors.

# Classification with a factor response
test_idx <- maxtwin(
  X = iris[, 1:4],
  Y = iris$Species,
  splitratio = 0.2
)

When there is no separate response, splitting can be based on all columns in a data frame:

test_idx <- maxtwin(iris, splitratio = 0.2)

Step 1 only (without iterative separation improvement)

test_idx <- initialmaxtwin(X, Y, splitratio = 0.2)

Options

Algorithm

MaxTwin (Algorithm 1 in the paper) proceeds in two steps:

Step 1: Apply twinning to select candidate test points, then within each Voronoi cell of the twinning set, pick the point with the largest nearest-neighbor distance.

Step 2: If the average separation distance falls below the threshold from Proposition 1, iteratively replace test points to improve separation while staying within a ball constraint that preserves distribution quality.

Dependencies

• FNN -- KD-tree based nearest neighbor search
• twinning -- Energy distance minimization

Testing

devtools::test()

Tests cover:

• Output correctness (size, bounds, uniqueness, sorting)
• Determinism (same seed = same result)
• Edge cases (univariate, high-dimensional, paired, and categorical data)
• Separation quality vs random splitting
• Consistency with original implementation

Reference

Joseph, V.R. and Cho, Y. (in press). A Robust Deterministic Data Splitting Method for Model Validation. Journal of Computational and Graphical Statistics.

License

GPL (>= 3)