get.inversion of the plant traits using different machine learning models

Usage

get.inversion(
  data,
  depVar,
  inputs,
  algorithm = "PLSR",
  method.resampling = NULL,
  n.cores = NULL,
  seed = 123,
  n.samples = 500,
  save.model = FALSE,
  save.path = NULL,
  ...
)

Arguments

data: The data frame containing the variables.
depVar: The dependent variable.
inputs: The independent variables.
algorithm: The type of machine learning model. Options: "PLSR" (Partial Least Squares Regression), "SVM" (Support Vector Machine), "RF" (Random Forest); "NN" (Neural Network); "GB" (Gradient boosting); "xGB" (eXtreme Gradient Boosting (XGBoost) with linear base learners); 'Bayesian' ( Bayesian Additive Regression Trees); 'AdaBag' ( Bagged AdaBoost); "qLASSO" (Quantile Regression with LASSO penalty); "RVM" (Relevance Vector Machines (RVM) with linear kernel); 'BRNN' (Bayesian Regularized Neural Networks); "Ensemble" (Stacking Ensemble models) Default is "PLSR".
method.resampling: The resampling method for controlling tht ML: Options are: "boot" (Bootstrapping); "boot632" (Bootstrapping-632); "optimism_boot"; "boot_all"; "cv" (cross-Validation); "repeatedcv" (repeats k-fold cross-validation with 3 times); "LOOCV" (Leave-One-Out Cross-Validation with 3 times); "LGOCV"
n.cores: The number of cores
seed: The seed for reproducibility. Default is 123.
n.samples: A integer with the number of samples used for tunning search (nsample/2) and create the ML model (n.sample)
save.model: Logical indicating whether to save the trained models. Default is FALSE.
save.path: Path to save the trained models. Required if save_models is TRUE.
...: Additional arguments (currently unused, reserved for future extensions).

Value

A list containing predictions, statistics, and plots for the specified machine learning model.

Examples

if (FALSE) { # \dontrun{
get.inversion(data = my_data, depVar = "Cab", inputs = c("NDVI", "TCARI"), ML = "SVM", seed = 123)
} # }