Wavelength Selection Recipe Step

step_select_wavelengths() creates a specification of a recipe step that keeps the predictors (wavelengths) selected by select_wavelengths() and removes the others. The selection is made on the training data when the recipe is prepped, so that within tune::tune_grid() it is repeated on the analysis set of every resample, as it must be: a selection made on all the data would make the cross-validated error too optimistic.

Usage

step_select_wavelengths(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  outcome = NULL,
  method = "vip",
  num_terms = NULL,
  threshold = NULL,
  num_comp = 5,
  recursive = FALSE,
  intervals = 40,
  num_intervals = NULL,
  robust = FALSE,
  options = list(),
  res = NULL,
  columns = NULL,
  skip = FALSE,
  id = recipes::rand_id("select_wavelengths")
)

Arguments

recipe: A recipe object. The step will be added to the sequence of operations for this recipe.
...: One or more selector functions to choose the predictors (for example, the spectral channels). See recipes::selections().
role: Not used by this step, since no new variables are created.
trained: A logical indicating whether the step has been trained.
outcome: The outcome variable, as a bare name or a selector. If NULL (default), the single outcome of the recipe is used.
method: The OSC algorithm: "wold", "sjoblom" (default) or "fearn". See osc().
num_terms: The number of variables to keep ("vip", "sr"). If NULL (default), the variables above threshold are kept.
threshold: The importance above which variables are kept when num_terms = NULL. If NULL (default), 1 for VIP; SR has no default threshold.
num_comp: The number of components of the selection model (ncomp of select_wavelengths()). Default is 5.
recursive: If TRUE, remove the variables by backward elimination ("vip", "sr"; requires num_terms). Default is FALSE.
intervals: The number of contiguous intervals ("ipls"). Default is 40.
num_intervals: The number of intervals to select ("ipls"). If NULL (default), intervals are added while the RMSECV decreases.
robust: If TRUE, use the robust PLS model of rsimpls(). Default is FALSE.
options: A list of further arguments of select_wavelengths(): prop_drop, folds, and alpha, ndir, nsamp with robust = TRUE.
res: The selection, stored once the step has been trained.
columns: The names of the selected predictors, stored once the step has been trained.
skip: A logical. Should the step be skipped when the recipe is baked by recipes::bake()? Keep the default FALSE, so that new data are corrected with the same filter as the training data.
id: A character string that is unique to this step.

Value

An updated version of recipe with the new step added to the sequence of existing steps.

Details

See select_wavelengths() for the methods: "vip" and "sr" keep the num_terms most important predictors (or those above threshold), and "ipls" selects num_intervals contiguous intervals. The outcome is not needed when new data are baked.

Tuning

num_terms, num_intervals and num_comp (the components of the selection model) can be tuned with tune::tune(). Their default ranges are dials::num_terms() with 20 to 1000 predictors, num_intervals() with 1 to 10 intervals, and dials::num_comp() with 1 to 10 components. When the model also has a num_comp argument, give the step's parameter its own id, such as num_comp = tune("select_comp").

Tidying

tidy() returns a tibble with columns terms (the selected predictors), selected, importance and id.

Author

Christian L. Goueguel

Examples

library(recipes)
data(forageLIBS)
wl <- suppressWarnings(as.numeric(names(forageLIBS)))
dat <- forageLIBS[c(which(names(forageLIBS) == "Ca"), which(wl > 380 & wl < 430))]
rec <- recipe(Ca ~ ., data = dat[1:300, ]) |>
  step_select_wavelengths(all_predictors(), method = "sr", num_terms = 50)
prepped <- prep(rec)
ncol(bake(prepped, new_data = dat[301:368, -1]))
#> [1] 50
tidy(prepped, number = 1)
#> # A tibble: 594 × 4
#>    terms       selected importance id                      
#>    <chr>       <lgl>         <dbl> <chr>                   
#>  1 380.011254  FALSE     0.0356    select_wavelengths_YPnL6
#>  2 380.096143  FALSE     0.00637   select_wavelengths_YPnL6
#>  3 380.202312  FALSE     0.00273   select_wavelengths_YPnL6
#>  4 380.285294  FALSE     0.00274   select_wavelengths_YPnL6
#>  5 380.3682761 FALSE     0.000626  select_wavelengths_YPnL6
#>  6 380.4512581 FALSE     0.000356  select_wavelengths_YPnL6
#>  7 380.5342402 FALSE     0.00136   select_wavelengths_YPnL6
#>  8 380.6172222 FALSE     0.00337   select_wavelengths_YPnL6
#>  9 380.7002042 FALSE     0.0000189 select_wavelengths_YPnL6
#> 10 380.7831863 FALSE     0.000810  select_wavelengths_YPnL6
#> # ℹ 584 more rows