y-Gradient Generalized Least Squares Weighting Recipe Step

step_y_gradient_glsw() creates a specification of a recipe step that down-weights variation between samples with similar outcomes, with the filter of y_gradient_glsw().

Usage

step_y_gradient_glsw(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  outcome = NULL,
  alpha = 0.01,
  window = 5,
  res = NULL,
  columns = NULL,
  skip = FALSE,
  id = recipes::rand_id("y_gradient_glsw")
)

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.
alpha: A positive number: the weighting parameter, relative to the largest eigenvalue of the clutter. Default is 0.01.
window: An odd integer giving the width of the Savitzky-Golay window used to compute the gradients. Default is 5.
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

As in step_glsw(), alpha is relative to the largest eigenvalue of the (weighted) gradient matrix, and the filter is stored in factored form. The filter uses the outcome, so it is estimated on training data only; the outcome is not needed when new data are baked. The selected columns are replaced by the filtered values (not centered). alpha can be tuned with tune::tune(), using glsw_alpha(). tidy() returns the selected terms, alpha (relative) 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))]
rec <- recipes::recipe(K ~ ., data = dat[1:300, ]) |>
  step_y_gradient_glsw(recipes::all_predictors(), alpha = 0.01)
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          707.          739.          718.          684.          669.
#>  2          713.          688.          721.          693.          673.
#>  3          686.          778.          803.          716.          694.
#>  4          682.          749.          785.          704.          686.
#>  5          656.          731.          766.          769.          755.
#>  6          753.          794.          802.          747.          705.
#>  7          725.          804.          802.          731.          701.
#>  8          756.          746.          748.          727.          689.
#>  9          670.          719.          724.          719.          684.
#> 10          719.          788.          763.          760.          764.
#> # 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>, …