Adaptive Smoothness Penalized Least Squares Baseline

step_measure_baseline_aspls() creates a specification of a recipe step that applies Adaptive Smoothness Penalized Least Squares baseline correction.

Usage

step_measure_baseline_aspls(
  recipe,
  measures = NULL,
  lambda = 1e+06,
  alpha = 0.5,
  max_iter = 50L,
  tol = 1e-05,
  role = NA,
  trained = FALSE,
  skip = FALSE,
  id = recipes::rand_id("measure_baseline_aspls")
)

Arguments

recipe

A recipe object.

measures

An optional character vector of measure column names.

lambda

Base smoothness parameter. Default is 1e6.

alpha

Adaptive weight parameter controlling smoothness adaptation (0 = no adaptation, 1 = maximum adaptation). Higher values cause regions with larger residuals to receive higher smoothness penalties. Note that adaptation is applied globally via an averaged lambda. Default is 0.5. Tunable via baseline_alpha().

max_iter

Maximum number of iterations. Default is 50.

tol

Convergence tolerance. Default is 1e-5.

role

Not used.

trained

Logical indicating if the step has been trained.

skip

Logical. Should the step be skipped when baking?

id

Unique step identifier.

Value

An updated recipe with the new step added.

Details

aspls adapts the smoothness parameter based on the signal properties. The algorithm computes a local smoothness weight based on residual magnitude, then uses the global average as the effective lambda. This provides some adaptation to peak intensity while maintaining computational efficiency.

This method is particularly effective for:

• Signals with varying peak widths

• Data with both sharp peaks and gradual baseline changes

• Chromatography with complex baselines

See also

Other measure-baseline: step_measure_baseline_airpls(), step_measure_baseline_als(), step_measure_baseline_arpls(), step_measure_baseline_auto(), step_measure_baseline_custom(), step_measure_baseline_fastchrom(), step_measure_baseline_gpc(), step_measure_baseline_iarpls(), step_measure_baseline_minima(), step_measure_baseline_morph(), step_measure_baseline_morphological(), 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)

# \donttest{
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_aspls(lambda = 1e6, alpha = 0.5) |>
  prep()

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