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MCR-ALS Decomposition for Multi-Dimensional Data

Source: R/step_mcr_als.R
step_measure_mcr_als.Rd

step_measure_mcr_als() creates a specification of a recipe step that applies Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS) to multi-dimensional measurement data.

Usage

step_measure_mcr_als(
  recipe,
  ...,
  n_components = 3L,
  max_iter = 500L,
  tol = 1e-06,
  non_negativity = TRUE,
  unimodality = FALSE,
  prefix = "mcr_",
  role = "predictor",
  trained = FALSE,
  skip = FALSE,
  id = recipes::rand_id("measure_mcr_als")
)

Arguments

recipe

A recipe object.

...

One or more selector functions to choose measure columns. If empty, all nD measure columns are used.

n_components

Number of components to extract. Default is 3.

max_iter

Maximum number of iterations. Default is 500.

tol

Convergence tolerance. Default is 1e-6.

non_negativity

Logical. Should non-negativity constraints be applied? Default is TRUE.

unimodality

Logical. Should unimodality constraints be applied? Default is FALSE.

prefix

Prefix for output column names. Default is "mcr_".

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

MCR-ALS is a powerful technique for resolving mixtures into pure component contributions. It's particularly useful for:

  • Chromatographic data (time x wavelength)

  • Spectroscopic mixtures

  • Process analytical data

Unlike PARAFAC, MCR-ALS is a bilinear method that works on 2D data (samples unfolded if 3D). It allows flexible constraints like non-negativity and unimodality.

Experimental Status

This step is experimental and its API may change in future versions.

Requirements

  • Input must be measure_nd_list with 2 dimensions

  • All samples must have the same grid (regular, aligned)

Note

This is an experimental feature. The implementation uses a simple ALS algorithm without advanced constraints. For production use, consider using dedicated MCR-ALS packages.

See also

step_measure_parafac() for PARAFAC decomposition

Other measure-multiway: step_measure_parafac(), step_measure_tucker()

Examples

if (FALSE) { # \dontrun{
library(recipes)

# After ingesting chromatographic data
rec <- recipe(concentration ~ ., data = chrom_data) |>
  step_measure_input_long(
    absorbance,
    location = vars(time, wavelength)
  ) |>
  step_measure_mcr_als(n_components = 3) |>
  prep()

bake(rec, new_data = NULL)
} # }