cellPCA Recipe Step

step_cellpca() creates a specification of a recipe step that converts the selected variables into robust principal component scores with cellpca(), which weights the outlying cells and observations and handles missing values.

Usage

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

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 cellpca(), such as alpha, maxiter or tol.
prefix: The prefix of the new column names. Default is "CPC".
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. New observations are projected by the robust regression of their observed cells on the loadings, so that their outlying cells do not distort their scores.

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[1:300, ]) |>
  step_cellpca(recipes::all_predictors(), num_comp = 2, distances = TRUE,
               options = list(od_cutoff = "chisq"))
recipes::bake(recipes::prep(rec), new_data = dat[301:368, ])
#> # A tibble: 68 × 5
#>        K    CPC1   CPC2 CPC_SD CPC_OD
#>    <dbl>   <dbl>  <dbl>  <dbl>  <dbl>
#>  1  2.25 -17363.  375.   0.920  17.7 
#>  2  1.91 -27529. -472.   1.82   32.3 
#>  3  2.61   1731. 1248.   0.831  13.8 
#>  4  2.11 -19592. -134.   1.03   14.6 
#>  5  2.17 -22224.  -10.4  1.16   15.0 
#>  6  1.61   2360. 2403.   2.02   15.6 
#>  7  1.46 -23785.  257.   1.24    9.53
#>  8  1.87 -18414.  266.   0.957  10.3 
#>  9  2.84 -22866.  565.   1.30   27.6 
#> 10  2.32 -12227.  504.   0.638   8.68
#> # ℹ 58 more rows

Usage

Arguments

Value

Details

See also

Examples