Custom Baseline Correction with User-Provided Function

step_measure_baseline_custom() creates a specification of a recipe step that applies a user-provided function for baseline correction. This allows for flexible, custom baseline estimation algorithms.

Usage

step_measure_baseline_custom(
  recipe,
  .fn,
  ...,
  subtract = TRUE,
  measures = NULL,
  tunable = NULL,
  role = NA,
  trained = FALSE,
  skip = FALSE,
  id = recipes::rand_id("measure_baseline_custom")
)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.

.fn

A function or formula for baseline estimation. The function should accept a measure_tbl (tibble with location and value columns) and return a numeric vector of baseline values with the same length as the input. Formulas are converted to functions via rlang::as_function(), where .x represents the measure_tbl.

...

Additional arguments passed to .fn. These are captured as quosures and evaluated at bake time.

subtract

If TRUE (default), the baseline is subtracted from the signal. If FALSE, the baseline values replace the original values (useful for extracting baselines).

measures

An optional character vector of measure column names to process. If NULL (the default), all measure columns (columns with class measure_list) will be processed.

tunable

An optional named list specifying which arguments in ... are tunable. Each element should be a list with pkg, fun, and optionally range. See Details.

role

Not used by this step since no new variables are created.

trained

A logical to indicate if the quantities for preprocessing have been estimated.

skip

A logical. Should the step be skipped when the recipe is baked?

id

A character string that is unique to this step to identify it.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Details

This step allows you to use any baseline estimation algorithm by providing a custom function. The function receives a measure_tbl object (a tibble with location and value columns) and should return a numeric vector of the estimated baseline values.

Function Contract

Your function should:

• Accept a measure_tbl as its first argument

• Return a numeric vector of the same length as nrow(measure_tbl)

• Handle NA values appropriately

Formula Interface

You can use a formula instead of a function. The formula is converted to a function where .x represents the measure_tbl:

# These are equivalent:
step_measure_baseline_custom(.fn = function(x) mean(x$value))
step_measure_baseline_custom(.fn = ~ mean(.x$value))

Tunability

To make parameters tunable with dials, provide a tunable argument:

step_measure_baseline_custom(
  .fn = ~ stats::loess(.x$value ~ .x$location, span = span)$fitted,
  span = 0.5,
  tunable = list(
    span = list(pkg = "dials", fun = "degree", range = c(0.1, 0.9))
  )
)

No selectors should be supplied to this step function. The data should be in the internal format produced by step_measure_input_wide() or step_measure_input_long().

Tidying

When you tidy() this step, a tibble with columns terms, subtract, and id is returned.

See also

step_measure_baseline_als(), step_measure_baseline_poly() for built-in baseline correction methods.

Other measure-baseline: step_measure_baseline_airpls(), step_measure_baseline_als(), step_measure_baseline_arpls(), step_measure_baseline_auto(), step_measure_baseline_gpc(), step_measure_baseline_minima(), step_measure_baseline_morph(), step_measure_baseline_poly(), step_measure_baseline_py(), step_measure_baseline_rf(), step_measure_baseline_rolling(), step_measure_baseline_snip(), step_measure_baseline_tophat(), step_measure_detrend()

Examples

library(recipes)

# Simple polynomial baseline using a function
poly_baseline <- function(x) {
  fit <- lm(x$value ~ poly(x$location, 2))
  predict(fit)
}

rec <- recipe(water + fat + protein ~ ., data = meats_long) |>
  update_role(id, new_role = "id") |>
  step_measure_input_long(transmittance, location = vars(channel)) |>
  step_measure_baseline_custom(.fn = poly_baseline) |>
  prep()

bake(rec, new_data = NULL)
#> # A tibble: 215 × 5
#>       id water   fat protein .measures
#>    <int> <dbl> <dbl>   <dbl>    <meas>
#>  1     1  60.5  22.5    16.7 [100 × 2]
#>  2     2  46    40.1    13.5 [100 × 2]
#>  3     3  71     8.4    20.5 [100 × 2]
#>  4     4  72.8   5.9    20.7 [100 × 2]
#>  5     5  58.3  25.5    15.5 [100 × 2]
#>  6     6  44    42.7    13.7 [100 × 2]
#>  7     7  44    42.7    13.7 [100 × 2]
#>  8     8  69.3  10.6    19.3 [100 × 2]
#>  9     9  61.4  19.9    17.7 [100 × 2]
#> 10    10  61.4  19.9    17.7 [100 × 2]
#> # ℹ 205 more rows

# Using formula interface with additional parameters
rec2 <- recipe(water + fat + protein ~ ., data = meats_long) |>
  update_role(id, new_role = "id") |>
  step_measure_input_long(transmittance, location = vars(channel)) |>
  step_measure_baseline_custom(
    .fn = ~ stats::loess(.x$value ~ .x$location, span = span)$fitted,
    span = 0.5
  ) |>
  prep()