Orthogonal Signal Correction Recipe Step

step_osc() creates a specification of a recipe step that removes the variation of the selected predictors that is orthogonal to the outcome, with osc(). The filter is estimated on the training data when the recipe is prepped and applied to new data when it is baked.

Usage

step_osc(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  outcome = NULL,
  method = "sjoblom",
  num_comp = 2,
  options = list(),
  res = NULL,
  columns = NULL,
  skip = FALSE,
  id = recipes::rand_id("osc")
)

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: Not used by this step, since no new variables are created.
trained: A logical indicating whether the step has been trained.
outcome: The outcome variable, as a bare name or a selector. If NULL (default), the single outcome of the recipe is used.
method: The OSC algorithm: "wold", "sjoblom" (default) or "fearn". See osc().
num_comp: The number of orthogonal components to remove.
options: A list of further arguments passed to the underlying function, such as scale, tol or max.iter for osc().
res: The fitted filter, stored once the step has been trained.
columns: The names of the selected predictors, 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 selected columns are replaced by the corrected values, which are centered (and scaled, if options = list(scale = TRUE)). Because the filter uses the outcome, it must be estimated on training data only. Within a workflows::workflow() and tune::tune_grid(), this happens automatically for every resample. The outcome is not needed when new data are baked.

The related steps step_direct_orthogonal(), step_direct_osc(), step_projected_osc(), step_opls() and step_o2pls() (which also handles several outcomes) remove response-orthogonal variation with other algorithms. step_epo() and step_glsw() remove variation described by external clutter, and step_y_gradient_glsw() down-weights variation between samples with similar outcomes.

Tuning

num_comp can be tuned with tune::tune(); its default range is dials::num_comp() with values 1 to 4. When the model also has a num_comp argument (for example, parsnip::pls()), give the step's parameter its own id, such as num_comp = tune("filter_comp"), so that the two can be tuned together:

rec <- recipe(K ~ ., data = spectra) |>
  step_osc(all_predictors(), method = "fearn", num_comp = tune("filter_comp"))
model <- parsnip::pls(num_comp = tune()) |>
  set_mode("regression") |>
  set_engine("mixOmics", scale = FALSE)  # center only, like pls::plsr()
wf <- workflow(rec, model)
tune_grid(wf, resamples = group_vfold_cv(spectra, group = Sample), grid = 9)

Tidying

tidy() returns a tibble with columns terms (the selected predictors), num_comp and id.

Author

Christian L. Goueguel

Examples

library(recipes)
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))]
rec <- recipe(K ~ ., data = dat[1:300, ]) |>
  step_osc(all_predictors(), method = "fearn", num_comp = 2)
prepped <- prep(rec)
tidy(prepped, number = 1)
#> # A tibble: 245 × 3
#>    terms       num_comp id       
#>    <chr>          <dbl> <chr>    
#>  1 760.0161416        2 osc_yTKKH
#>  2 760.1001689        2 osc_yTKKH
#>  3 760.1841961        2 osc_yTKKH
#>  4 760.2682233        2 osc_yTKKH
#>  5 760.3522506        2 osc_yTKKH
#>  6 760.4362778        2 osc_yTKKH
#>  7 760.520305         2 osc_yTKKH
#>  8 760.6043323        2 osc_yTKKH
#>  9 760.6883595        2 osc_yTKKH
#> 10 760.7723867        2 osc_yTKKH
#> # ℹ 235 more rows
# new spectra are corrected with the filter estimated on the training data
bake(prepped, new_data = dat[301:368, -1])
#> # A tibble: 68 × 245
#>    `760.0161416` `760.1001689` `760.1841961` `760.2682233` `760.3522506`
#>            <dbl>         <dbl>         <dbl>         <dbl>         <dbl>
#>  1        -46.5         -67.3          -95.9        -89.7         -97.1 
#>  2        -57.4        -134.          -112.         -97.1        -109.  
#>  3        -40.1          -3.71          20.9        -31.1         -45.4 
#>  4        -81.2         -67.2          -38.3        -79.2         -89.5 
#>  5       -111.          -89.4          -62.1        -18.4         -25.5 
#>  6         21.5           4.08          13.9         -6.85        -41.7 
#>  7        -58.0         -31.3          -39.1        -72.1         -96.3 
#>  8         -5.23        -68.6          -72.9        -54.9         -85.3 
#>  9        -88.8         -91.0          -96.8        -59.1         -86.1 
#> 10        -24.9          -9.84         -40.0         -4.57          6.69
#> # ℹ 58 more rows
#> # ℹ 240 more variables: `760.4362778` <dbl>, `760.520305` <dbl>,
#> #   `760.6043323` <dbl>, `760.6883595` <dbl>, `760.7723867` <dbl>,
#> #   `760.856414` <dbl>, `760.9404412` <dbl>, `761.0244684` <dbl>,
#> #   `761.1084957` <dbl>, `761.1925229` <dbl>, `761.2765501` <dbl>,
#> #   `761.3605774` <dbl>, `761.4446046` <dbl>, `761.5286319` <dbl>,
#> #   `761.6126591` <dbl>, `761.6966863` <dbl>, `761.7807136` <dbl>, …