Cross-Validation for Maxent Models

Splits occurrence data into k folds and trains one model per fold using the remaining folds for training and the held-out fold for testing. This mirrors Java Maxent's SampleSet.splitForCV().

Usage

maxent_cross_validate(
  env_vals,
  sample_indices,
  num_points,
  k = 5L,
  types = c("linear", "quadratic", "hinge"),
  n_hinges = 15L,
  max_iter = 500L,
  seed = 42L,
  categorical = NULL,
  bias_weights = NULL
)

Arguments

env_vals

Named list of numeric vectors (one per environmental variable, length = total points: background + occurrences).

sample_indices

Integer vector: 0-based indices of occurrence samples within env_vals.

num_points

Integer: total number of points (background + occurrences).

k

Integer: number of folds (default 5).

types

Character vector of feature types (default c("linear", "quadratic", "hinge")).

n_hinges

Integer: number of hinge knots (default 15).

max_iter

Integer: maximum training iterations (default 500).

seed

Integer: random seed for fold assignment (default 42).

categorical

Character vector of variable names that are categorical (default NULL).

bias_weights

Optional numeric vector of per-point bias weights (default NULL).

Value

A named list with:

fold_results

A list of per-fold result lists, each containing fold, train_gain, n_train, n_test, and lambdas.

summary

A data.frame with per-fold and overall mean/sd statistics.

Examples

if (FALSE) { # \dontrun{
env <- list(temp = rnorm(110), precip = rnorm(110))
cv <- maxent_cross_validate(env, sample_indices = 100:109,
                             num_points = 110L, k = 5L)
print(cv$summary)
} # }