tests/testthat/test.optimal_components.R

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

context("*** Test methods for finding optimal number of PLSR components ***")

### Setup data for tests
#Load Ely et al 2019 dataset
data("ely_plsr_data")
inVar <- "N_g_m2"
Start.wave <- 500
End.wave <- 2400
wv <- seq(Start.wave,End.wave,1)
plsr_data <- ely_plsr_data
spec <- as.matrix(plsr_data[, which(names(plsr_data) %in% paste0("Wave_",wv))])
plsr_data <- data.frame(plsr_data[, which(names(plsr_data) %notin% paste0("Wave_",wv))],
                        Spectra=I(spec))
###

test_that("Finding optimal components using the built-in PLS package approach", {
  method <- "pls"
  random_seed <- 1245565
  seg <- 50
  maxComps <- 20
  iterations <- 80
  prop <- 0.70
  
  nComps <- spectratrait::find_optimal_components(dataset=plsr_data, targetVariable=inVar,
                                                  method=method, maxComps=maxComps, seg=seg, 
                                                  random_seed=random_seed)
  expect_gte(nComps, 12)
})

test_that("Finding optimal components using the firstMin approach", {
  method <- "firstMin"
  random_seed <- 1245565
  seg <- 50
  maxComps <- 20
  iterations <- 80
  prop <- 0.70
  
  nComps <- spectratrait::find_optimal_components(dataset=plsr_data, targetVariable=inVar, 
                                                  method=method, maxComps=maxComps, 
                                                  iterations=iterations, seg=seg, prop=prop, 
                                                  random_seed=random_seed)
  expect_gte(nComps, 12)
  
})

test_that("Finding optimal components using the firstPlateau approach", {
  method <- "firstPlateau"
  random_seed <- 1245565
  seg <- 50
  maxComps <- 20
  iterations <- 80
  prop <- 0.70
  
  nComps <- spectratrait::find_optimal_components(dataset=plsr_data, targetVariable=inVar, 
                                                  method=method, maxComps=maxComps, 
                                                  iterations=iterations, seg=seg, prop=prop, 
                                                  random_seed=random_seed)
  expect_gte(nComps, 12)
  
})