s_map_rgl: Perform regularized S-map
In ong8181/macam: A collection of miscellaneous R functions for ecology

s_map_rgl

R Documentation

Perform regularized S-map

Description

s_map_rgl is a wrapper function of extended_lnlp() for regularized S-map. For multivariate S-map, please use extended_lnlp().

Usage

s_map_rgl(
  ts_data,
  E,
  lib = c(1, length(ts_data)),
  pred = lib,
  tp = 1,
  num_neighbors = length(ts_data) + 1,
  theta = 0,
  dist_w = NULL,
  regularized = FALSE,
  lambda = NULL,
  alpha = 0,
  glmnet_parallel = FALSE,
  random_seed = NULL,
  save_smap_coefficients = FALSE
)

Arguments

`ts_data`	Data.frame, matrix, or vector. One-column univariate time series.
`E`	Integer. Embedding dimension.
`lib`	Library indices.
`pred`	Prediction indices.
`tp`	Forecasting time ahead.
`num_neighbors`	Numeric. The number of nearest neighbors.
`theta`	Numeric. Weighing function for S-map.
`dist_w`	Matrix. Distance matrix used to calculate weights for S-map. Implemented for MDR S-map (Chang et al. 2021) Ecology Letters. If `NULL`, then weights are calculated based on Euclidean distance.
`regularized`	Logical If `TRUE`, regularized S-map will be performed. If `FALSE`, the normal S-map will be performed. Please use `rEDM::s_map` function.
`lambda`	Numeric. Specify the strength of penalty in the regularization.
`alpha`	Numeric. `alpha = 0` is the ridge regression, `alpha = 1` is the lasso regression, and `⁠0 < alpha < 1⁠` is an elastic net.
`glmnet_parallel`	Logical. If TRUE, the computation will be parallel (currently, experimental).
`random_seed`	Numeric. Random seed.
`save_smap_coefficients`	Logical. If `TRUE`, S-map coefficients will be saved.