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Robust Sparse PCA (ROSPCA) Recipe Step

Source: R/recipe_steps_robust.R
step_rospca.Rd

step_rospca() creates a specification of a recipe step that converts the selected variables into robust sparse principal component scores, with rospca().

Usage

step_rospca(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  num_comp = 2,
  lambda = 1,
  options = list(),
  prefix = "RSPC",
  distances = FALSE,
  keep_original_cols = FALSE,
  res = NULL,
  columns = NULL,
  skip = FALSE,
  id = recipes::rand_id("rospca")
)

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.

lambda

The sparsity parameter of rospca(). Default is 1.

options

A list of further arguments passed to rospca(), such as alpha, stand or ndir.

prefix

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

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(), with sparse loadings. num_comp and lambda can be tuned with tune::tune(); lambda uses dials::penalty() with a range of 0.01 to 100.

See also

rospca(), 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_rospca(recipes::all_predictors(), num_comp = 2, distances = TRUE)
recipes::bake(recipes::prep(rec), new_data = NULL)
#> # A tibble: 368 × 5
#>        K RSPC1    RSPC2 RSPC_SD RSPC_OD
#>    <dbl> <dbl>    <dbl>   <dbl>   <dbl>
#>  1  3.68 21.1   -7.40     2.76     8.53
#>  2  2.52 12.8   -0.0993   1.14     5.92
#>  3  2.45 13.5  -17.6      4.97     7.44
#>  4  2.3   7.94 -12.5      3.49     5.68
#>  5  2.87 -4.68   2.84     0.880    5.75
#>  6  2.16  7.41   2.29     0.909    5.92
#>  7  2.94 38.0  -23.8      7.32    15.7 
#>  8  2.43  5.27   2.19     0.759    5.91
#>  9  1.81 -3.99   3.60     1.05     6.13
#> 10  2.07 20.2   -1.60     1.85     6.09
#> # ℹ 358 more rows