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MacroPCA Recipe Step

Source: R/recipe_steps_robust.R
step_macropca.Rd

step_macropca() creates a specification of a recipe step that converts the selected variables into robust principal component scores with macropca(), which handles cellwise outliers and missing values as well as outlying observations.

Usage

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

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 macropca(), such as alpha or MacroPCA parameters.

prefix

The prefix of the new column names. Default is "MPC".

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

As step_robpca(). Missing values are allowed in the selected columns, both when the recipe is prepped and when it is baked.

See also

macropca(), step_robpca()

Examples

data(forageLIBS)
# potassium and the K I resonance lines
wl <- suppressWarnings(as.numeric(names(forageLIBS)))
dat <- forageLIBS[c(which(names(forageLIBS) == "K"), which(wl > 760 & wl < 780))]
set.seed(1)
rec <- recipes::recipe(K ~ ., data = dat) |>
  step_macropca(recipes::all_predictors(), num_comp = 2)
recipes::bake(recipes::prep(rec), new_data = NULL)
#> # A tibble: 368 × 3
#>        K   MPC1     MPC2
#>    <dbl>  <dbl>    <dbl>
#>  1  3.68 33973.  -2430. 
#>  2  2.52 16119.   1776. 
#>  3  2.45 28441. -13594. 
#>  4  2.3  27116. -10038. 
#>  5  2.87 -8891.   1166. 
#>  6  2.16  5785.   2203. 
#>  7  2.94 49413. -15087. 
#>  8  2.43  9682.   3483. 
#>  9  1.81 -9352.   2485. 
#> 10  2.07 26759.    -59.2
#> # ℹ 358 more rows