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QC Bracketing Interpolation

Source: R/drift-correction.R
step_measure_qc_bracket.Rd

step_measure_qc_bracket() creates a specification of a recipe step that corrects for drift using linear interpolation between bracketing QC or reference samples. This is a simple, intuitive method where each sample is corrected based on the two nearest QC samples.

Usage

step_measure_qc_bracket(
  recipe,
  ...,
  run_order_col = "run_order",
  sample_type_col = "sample_type",
  qc_type = "qc",
  apply_to = c("all", "unknown"),
  extrapolate = TRUE,
  min_qc = 2,
  role = NA,
  trained = FALSE,
  skip = FALSE,
  id = recipes::rand_id("measure_qc_bracket")
)

Arguments

recipe

A recipe object.

...

One or more selector functions to choose feature columns. For feature-level data, select the numeric response columns. For curve-level data with .measures, leave empty to apply to all locations.

run_order_col

Name of the column containing run order (injection sequence). Must be numeric/integer.

sample_type_col

Name of the column containing sample type.

qc_type

Value(s) in sample_type_col that identify QC samples to use for drift modeling. Default is "qc".

apply_to

Which samples to apply correction to:

  • "all" (default): Correct all samples

  • "unknown": Only correct unknown samples

extrapolate

Logical. Should correction be extrapolated for samples before the first or after the last QC? Default is TRUE. If FALSE, those samples use the nearest QC's correction factor.

min_qc

Minimum number of QC samples required. Default is 5.

role

Not used by this step.

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

How It Works

For each sample at run order t:

  1. Find the nearest QC samples before (t1) and after (t2)

  2. Calculate correction factors at t1 and t2 (target / observed)

  3. Linearly interpolate the correction factor for t

  4. Apply the interpolated correction

This method is commonly used in clinical and bioanalytical laboratories where QC samples are injected at regular intervals throughout the run.

When to Use

  • Regular QC injection intervals

  • Short analytical runs

  • When you want simple, transparent corrections

  • Regulatory environments where interpretability is important

See also

Other drift-correction: step_measure_drift_linear(), step_measure_drift_qc_loess(), step_measure_drift_spline()

Examples

library(recipes)

# Data with QC samples at regular intervals
data <- data.frame(
  sample_id = paste0("S", 1:15),
  sample_type = c("qc", rep("unknown", 4), "qc", rep("unknown", 4), "qc",
                  rep("unknown", 3), "qc"),
  run_order = 1:15,
  feature1 = c(100, 101, 103, 105, 107, 105, 107, 109, 111, 113,
               110, 112, 114, 116, 115)  # Drift pattern
)

rec <- recipe(~ ., data = data) |>
  update_role(sample_id, new_role = "id") |>
  step_measure_qc_bracket(feature1) |>
  prep()

corrected <- bake(rec, new_data = NULL)