bootstrap_SA: Parameter fitting with bootstrap sampling of datasets

In ELAplus: Energy Landscape Analysis for Ecological Communities

Parameter fitting with bootstrap sampling of datasets

Description

Perform runSA algorithm and estimate parameters using bootstrap resampled data set (ocmat and enmat). The basic configuration is same as runSA.

Usage

bootstrap_SA(
  ocmat,
  enmat = NULL,
  bootstrap.it = 2000,
  rep = 128,
  threads = 1,
  we = 0.001,
  totalit = 1000,
  lambda = 0.01,
  runadamW = TRUE,
  maxlr = 0.005,
  sparse = TRUE,
  reporting = TRUE
)

Arguments

ocmat	Numeric matrix (0/1). Rows are samples and columns are species.
enmat	Optional numeric matrix. Environmental variables for each sample. Should be normalized/standardized beforehand.
bootstrap.it	number of bootstrap trials (default: 1000).
rep	Integer. Number of parallel runs (replicates) for optimization.
threads	Integer. Number of cores used when running in parallel.
we	Numeric. Weight decay parameter for AdamW.
totalit	Integer. Total number of optimization iterations.
lambda	Numeric. Sparsity penalty parameter (used when sparse = TRUE).
runadamW	Logical. If TRUE, use AdamW-style optimization.
maxlr	Numeric. Maximum learning rate.
sparse	Logical. If TRUE, apply sparsity constraint.
reporting	Logical. If TRUE, print progress messages.

Value

a list of SA parameters estimated with bootstrap resampling (length bootstrap.it).

ELAplus documentation built on May 28, 2026, 5:09 p.m.