Extended Multiplicative Signal Correction Recipe Step

step_emsc() creates a specification of a recipe step that corrects each spectrum for multiplicative effects, a polynomial baseline and, optionally, known interferents, with emsc().

Usage

step_emsc(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  degree = 2,
  interferents = NULL,
  wavelength = NULL,
  robust = TRUE,
  res = NULL,
  columns = NULL,
  skip = FALSE,
  id = recipes::rand_id("emsc")
)

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.
degree: The degree of the polynomial baseline. Default is 2.
interferents: An optional numeric matrix or data frame of interferent spectra, one per row. If it has column names, the columns matching the selected predictors are used; otherwise it must have one column per selected predictor, in the same order.
wavelength: An optional numeric vector of wavelengths, one per selected column, used to build the polynomials. If NULL, the column names are used when they are numeric, and the column positions otherwise.
robust: A logical: use the median (TRUE, default) or the mean of the training spectra as the reference.
res: The fitted EMSC model, 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 is estimated from the training spectra when the recipe is prepped, and new spectra are corrected with the same reference, polynomials and interferents when they are baked. The selected columns form one spectrum per row and are replaced by the corrected values.

degree can be tuned with tune::tune(), using dials::degree_int() with values 0 to 4. 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_emsc(recipes::all_predictors(), degree = 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         1147.         1146.         1117.         1115.         1098.
#>  2         1094.         1024.         1068.         1076.         1055.
#>  3         1213.         1263.         1282.         1221.         1195.
#>  4         1064.         1109.         1150.         1093.         1075.
#>  5         1027.         1084.         1126.         1174.         1160.
#>  6         1183.         1182.         1188.         1168.         1127.
#>  7         1009.         1077.         1074.         1038.         1007.
#>  8         1136.         1088.         1089.         1106.         1065.
#>  9         1110.         1133.         1137.         1170.         1127.
#> 10         1128.         1168.         1136.         1169.         1175.
#> # 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>, …