Orthogonal Projections to Latent Structures (OPLS) Recipe Step

step_opls() creates a specification of a recipe step that removes num_comp orthogonal components from the selected predictors with the OPLS model of opls(): the selected columns are replaced by the OPLS-filtered data.

Usage

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

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 opls(): center and scale.
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 filtered data are the same as those of step_projected_osc() with the same num_comp. For Pareto scaling, add step_pareto_scale() before this step. The model is fitted without cross-validation or permutation test, which do not change the filter. The number of orthogonal components is not selected automatically: tune num_comp instead. As for step_osc(), the filter uses the outcome and is estimated on the training data only; the outcome is not needed when new data are baked.

Tuning

num_comp can be tuned with tune::tune(); its default range is dials::num_comp() with values 1 to 4.

Tidying

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

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))]
# Pareto scaling, then the OPLS filter
rec <- recipes::recipe(K ~ ., data = dat[1:300, ]) |>
  step_pareto_scale(recipes::all_predictors()) |>
  step_opls(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         0.374        -1.95         -5.28          -5.06        -5.87 
#>  2         6.18         -3.12          0.221          1.04        -0.104
#>  3         1.35          5.89          9.26           2.25         0.598
#>  4        -3.04         -0.904         2.97          -2.78        -3.96 
#>  5        -7.07         -3.95         -0.316          4.64         3.85 
#>  6         1.70         -0.558         0.631         -1.89        -6.36 
#>  7        -3.61          0.320        -0.649         -5.13        -8.26 
#>  8         1.97         -5.57         -6.05          -4.06        -7.92 
#>  9        -1.81         -1.80         -2.12           1.83        -1.34 
#> 10        -5.98         -3.81         -7.68          -3.18        -1.88 
#> # 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>, …