Shot Rejection Recipe Step

step_reject_shots() creates a specification of a recipe step that removes the outlying laser shots of each sample from the training data, with reject_shots().

Usage

step_reject_shots(
  recipe,
  ...,
  sample,
  method = c("intensity", "correlation"),
  cutoff = 3.5,
  scale = c("floor", "sample", "pooled"),
  role = NA,
  trained = FALSE,
  columns = NULL,
  skip = TRUE,
  id = recipes::rand_id("reject_shots")
)

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 spectral columns (named by their wavelengths, see reject_shots()).

sample

The column identifying the sample of each shot, as a bare name or a selector. Give it the "id" role (see recipes::update_role()) so that it is not used as a predictor.

method

The criteria: one or more of "intensity", "correlation" (both by default) and "distance" (see reject_shots()).

cutoff

The robust z-score above which a shot is rejected. Default is 3.5.

scale

The robust scale of the z-scores: "floor" (default), "sample" or "pooled" (see Details).

role

Not used by this step, since no new variables are created.

trained

A logical indicating whether the step has been trained.

columns

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

skip

A logical: skip the step when new data are baked (TRUE, default). See details.

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 any existing operations.

Details

Each shot is compared with the other shots of the same sample (the sample column), so nothing is estimated from the training data as a whole. Because the step removes rows, skip = TRUE by default: shots are rejected when the recipe is prepped, but new data are not filtered when they are baked (predictions must be made for every row). Use a resampling scheme grouped by sample, such as rsample::group_vfold_cv(), so that shots of the same sample are not split between analysis and assessment sets.

To model sample means instead of single shots, average the shots before the recipe, with reject_shots() and average().

tidy() returns the spectral terms, the sample column, the criteria (method), the cutoff, the scale and id.

See also

reject_shots(), average(), step_line_ratio()

Examples

data(forageLIBS)
wl <- suppressWarnings(as.numeric(names(forageLIBS)))
spectra <- forageLIBS[which(wl > 760 & wl < 780)]  # the K I resonance lines
set.seed(1)
shots <- spectra[rep(1:4, each = 5), ] * stats::runif(20, 0.95, 1.05)
shots[7, ] <- 0.2 * shots[7, ]
shots <- cbind(sample = rep(c("A", "B", "C", "D"), each = 5), shots)
rec <- recipes::recipe(~ ., data = shots) |>
  step_reject_shots(recipes::all_numeric(), sample = "sample")
prepped <- recipes::prep(rec)
# the rejected shot is removed from the training data
nrow(recipes::bake(prepped, new_data = NULL))
#> [1] 19