pls_permutation_by_groups: Run a PLSR model permutation analysis stratified by selected...

In TESTgroup-BNL/PLSR_for_plant_trait_prediction: A simple add-on package to aid in the fitting of leaf or canopy scale spectra-trait PLSR models

Run a PLSR model permutation analysis stratified by selected "groups". Can be used to determine the optimal number of components or conduct a boostrap uncertainty analysis

Description

Run a PLSR model permutation analysis stratified by selected "groups". Can be used to determine the optimal number of components or conduct a boostrap uncertainty analysis

Usage

pls_permutation_by_groups(
  dataset = NULL,
  targetVariable = NULL,
  maxComps = 20,
  iterations = 20,
  prop = 0.7,
  group_variables = NULL,
  verbose = FALSE
)

Arguments

dataset	input full PLSR dataset. Usually just the calibration dataset
targetVariable	What object or variable to use as the Y (predictand) in the PLSR model? Usually the "inVar" variable set at the beginning of a PLS script
maxComps	maximum number of components to use for each PLSR fit
iterations	how many different permutations to run
prop	proportion of data to preserve for each permutation
group_variables	Character vector of the form c("var1", "var2"..."varn") providing the factors used for stratified sampling in the PLSR permutation analysis
verbose	Should the function report the current iteration status/progress to the terminal or run silently? TRUE/FALSE. Default FALSE

Value

output a list containing the PRESS and coef_array. output <- list(PRESS=press.out, coef_array=coefs)

Author(s)

asierrl, Shawn P. Serbin, Julien Lamour

TESTgroup-BNL/PLSR_for_plant_trait_prediction documentation built on Feb. 15, 2025, 2:08 p.m.