Direct Orthogonal Signal Correction Recipe Step

step_direct_osc() creates a specification of a recipe step that removes response-orthogonal variation from the selected predictors with direct_osc().

Usage

step_direct_osc(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  outcome = NULL,
  num_comp = 2,
  options = list(),
  res = NULL,
  columns = NULL,
  skip = FALSE,
  id = recipes::rand_id("direct_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.
num_comp: The number of orthogonal components to remove.
options: A list of further arguments passed to direct_osc(), such as scale or tol.
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.

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))]
rec <- recipes::recipe(K ~ ., data = dat[1:300, ]) |>
  step_direct_osc(recipes::all_predictors(), num_comp = 2)
prepped <- recipes::prep(rec)
recipes::bake(prepped, new_data = dat[301:368, ])
#> # A tibble: 68 × 246
#>    `760.0161416` `760.1001689` `760.1841961` `760.2682233` `760.3522506`
#>            <dbl>         <dbl>         <dbl>         <dbl>         <dbl>
#>  1          1.77         -24.5         -45.6        -45.5         -50.3 
#>  2        -25.9         -110.          -76.7        -71.0         -79.9 
#>  3         11.5           46.3          71.5         19.3           6.38
#>  4        -26.4          -17.1          17.7        -27.9         -35.6 
#>  5        -69.1          -51.7         -18.3         20.5          15.6 
#>  6         64.9           48.7          54.5         37.5           2.97
#>  7         -2.95          22.3          14.8        -18.1         -40.8 
#>  8         14.0          -51.6         -52.8        -37.3         -66.7 
#>  9        -20.9          -32.1         -25.2          2.06        -20.8 
#> 10        -35.2          -21.5         -48.9        -15.9          -4.34
#> # ℹ 58 more rows
#> # ℹ 241 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>, …

Usage

Arguments

Value

Details

See also

Examples