Robust PCA (ROBPCA) Recipe Step

step_robpca() creates a specification of a recipe step that converts the selected variables into robust principal component scores, with robpca(). It is the robust counterpart of recipes::step_pca().

Usage

step_robpca(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  num_comp = 2,
  options = list(),
  prefix = "RPC",
  distances = FALSE,
  keep_original_cols = FALSE,
  res = NULL,
  columns = NULL,
  skip = FALSE,
  id = recipes::rand_id("robpca")
)

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: The role of the new score columns. Default is "predictor".
trained: A logical indicating whether the step has been trained.
num_comp: The number of principal components. Default is 2.
options: A list of further arguments passed to robpca(), such as alpha or ndir.
prefix: The prefix of the new column names. Default is "RPC".
distances: A logical: add the score and orthogonal distances as columns. Default is FALSE.
keep_original_cols: A logical: keep the selected columns. Default is FALSE.
res: The fitted robust PCA model, stored once the step has been trained.
columns: The names of the selected columns, 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

The robust PCA model is estimated on the training data when the recipe is prepped, and new data are projected onto it when they are baked. The new columns are named with prefix followed by the component number. With distances = TRUE, two more columns hold the score distance (<prefix>_SD) and orthogonal distance (<prefix>_OD) of each observation, which can be used to screen outliers. The selected columns are removed unless keep_original_cols = TRUE.

Tuning

num_comp can be tuned with tune::tune(), using dials::num_comp() with values 1 to 4.

Tidying

tidy() returns the loadings as a tibble with columns terms, value, component and id.

Examples

library(recipes)
data(forageLIBS)
# the 380-430 nm window (Ca II H and K lines)
wl <- suppressWarnings(as.numeric(names(forageLIBS)))
spectra <- forageLIBS[which(wl > 380 & wl < 430)]
set.seed(1)
rec <- recipe(~ ., data = spectra) |>
  step_robpca(all_predictors(), num_comp = 3, distances = TRUE)
bake(prep(rec), new_data = NULL)
#> # A tibble: 368 × 5
#>       RPC1    RPC2   RPC3 RPC_SD RPC_OD
#>      <dbl>   <dbl>  <dbl>  <dbl>  <dbl>
#>  1  15595.   -848. -7049.  1.23   4585.
#>  2  22905.   6593.  2477.  1.26  10757.
#>  3  29122.   -938.  1470.  1.06   8361.
#>  4  51124. -16874. 10181.  3.30   8297.
#>  5 -29159.   2879.   458.  1.10   9433.
#>  6  15121.   3329. -1164.  0.718  5557.
#>  7  53124. -20580.  4911.  3.42  17121.
#>  8  -6326.   5495. -6644.  1.29   5966.
#>  9 -27029.   9313.  3557.  1.66   8335.
#> 10  33735.   3858. 10648.  2.11   6894.
#> # ℹ 358 more rows