Replicate Runs for Maxent Models — maxent

Trains multiple replicate models using either bootstrap sampling or subsample splitting of occurrence data. This mirrors Java Maxent's SampleSet.replicate() method.

Usage

maxent_replicate(
  env_vals,
  sample_indices,
  num_points,
  n_replicates = 5L,
  replicate_type = "bootstrap",
  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.
n_replicates: Integer: number of replicates (default 5).
replicate_type: Character: "bootstrap" (default) samples with replacement, "subsample" uses a 75% random subsample.
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 (default 42).
categorical: Character vector of variable names that are categorical (default NULL).
bias_weights: Optional numeric vector of per-point bias weights.

Value

A named list with:

replicate_results: List of per-replicate result lists.
summary: A data.frame with mean and sd statistics.

Examples

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