Multiplicative Scatter Correction Recipe Step

step_msc() creates a specification of a recipe step that corrects each spectrum for multiplicative and additive effects relative to a reference spectrum, with msc().

Usage

step_msc(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  robust = TRUE,
  drop.offset = TRUE,
  window = NULL,
  reference = NULL,
  columns = NULL,
  skip = FALSE,
  id = recipes::rand_id("msc")
)

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.
robust: A logical: use the median (TRUE, default) or the mean of the training spectra as the reference.
drop.offset: A logical: remove the additive offset (TRUE, default) or only the multiplicative effect.
window: An optional list of column index vectors (within the selected columns) for piecewise MSC. See msc().
reference: The reference spectrum, 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

The reference spectrum (the median or mean of the training spectra) is estimated when the recipe is prepped, and new spectra are corrected against it when they are baked. The selected columns form one spectrum per row. They are replaced by the corrected values. tidy() returns the selected terms, the reference value of each (NA before the step is trained) 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_msc(recipes::all_predictors())
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         1206.         1198.         1163.         1155.         1132.
#>  2         1308.         1227.         1260.         1257.         1225.
#>  3         1147.         1198.         1217.         1156.         1131.
#>  4         1149.         1186.         1218.         1154.         1127.
#>  5         1131.         1179.         1211.         1249.         1226.
#>  6         1201.         1195.         1196.         1172.         1127.
#>  7         1130.         1184.         1167.         1117.         1073.
#>  8         1221.         1163.         1155.         1163.         1113.
#>  9         1213.         1230.         1226.         1253.         1203.
#> 10         1145.         1179.         1142.         1170.         1170.
#> # 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>, …