Calculate Molecular Weight Fractions for SEC/GPC

step_measure_mw_fractions() creates a specification of a recipe step that calculates weight fractions above and below specified molecular weight cutoffs.

This step has been superseded by measure.sec::step_sec_mw_fractions(). For new code, we recommend using the measure.sec package which provides more complete SEC/GPC analysis functionality.

Usage

step_measure_mw_fractions(
  recipe,
  measures = NULL,
  cutoffs = c(1000, 10000, 1e+05),
  calibration = NULL,
  integration_range = NULL,
  prefix = "frac_",
  role = "predictor",
  trained = FALSE,
  skip = FALSE,
  id = recipes::rand_id("measure_mw_fractions")
)

Arguments

recipe

A recipe object.

measures

An optional character vector of measure column names.

cutoffs

Numeric vector of MW cutoff values. For each cutoff, the step calculates the weight fraction below and above that value.

calibration

Calibration method for converting x-axis to log(MW). See step_measure_mw_averages() for details.

integration_range

Optional numeric vector c(min, max) specifying the x-axis range for integration. If NULL, uses full range.

prefix

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

role

Role for generated columns. Default is "predictor".

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

For each cutoff value C, this step calculates:

• frac_below_C: Weight fraction with MW < C

• frac_above_C: Weight fraction with MW >= C

These fractions sum to 1.0 and are useful for characterizing polymer distributions. Common cutoffs include:

• 1000 Da for oligomer content

• 10000 Da for low MW fraction

• 100000 Da for high MW fraction

See also

Other measure-chromatography: step_measure_mw_averages(), step_measure_mw_distribution(), step_measure_peaks_deconvolve()

Examples

library(recipes)

# Calculate fractions at multiple cutoffs
# rec <- recipe(~., data = gpc_data) |>
#   step_measure_input_wide(starts_with("signal_")) |>
#   step_measure_baseline_als() |>
#   step_measure_mw_fractions(cutoffs = c(1000, 10000, 100000)) |>
#   prep()