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Binning Recipe Step

Source: R/bin_spectra.R
step_bin_spectra.Rd

step_bin_spectra() creates a specification of a recipe step that replaces each group of width adjacent channels of the spectra by their mean, with bin_spectra().

Usage

step_bin_spectra(
  recipe,
  ...,
  width = 3,
  segments = TRUE,
  role = "predictor",
  trained = FALSE,
  columns = NULL,
  bins = NULL,
  skip = FALSE,
  id = recipes::rand_id("bin_spectra")
)

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().

width

The number of adjacent channels averaged, a positive integer. Default is 3; width = 1 returns the spectra unchanged.

segments

A logical: bin the segments between gaps of the wavelength axis separately (TRUE, default). Needs wavelengths as names.

role

The role of the binned columns. Default is "predictor".

trained

A logical indicating whether the step has been trained.

columns

The names of the selected columns, stored once the step has been trained.

bins

The groups of channels and the names of the binned columns, set when the recipe is prepped. Not to be set by the user.

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

The selected columns form one spectrum per row, and are replaced by the fewer binned columns, named by their mean wavelength (see bin_spectra()). The groups of channels are set from the column names when the recipe is prepped; nothing is estimated from the training data. The width can be tuned with tune::tune() (dials parameter window_size()). tidy() returns the terms, width and id.

See also

bin_spectra(), step_gap_derivative(), step_spectral_norm()

Examples

if (rlang::is_installed("recipes")) {
  data(forageLIBS)
  # binning, normalization to the total area and gap-segment derivative
  rec <- recipes::recipe(K ~ ., data = forageLIBS[-c(1:10, 12:14)]) |>
    step_bin_spectra(recipes::all_predictors(), width = 3) |>
    step_spectral_norm(recipes::all_predictors(), method = "area") |>
    step_gap_derivative(recipes::all_predictors(), gap = 3, segment = 1) |>
    recipes::prep()
  dim(recipes::bake(rec, new_data = NULL))
}
#> [1]  368 2385