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Linear Drift Correction

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

step_measure_drift_linear() creates a specification of a recipe step that corrects for linear signal drift across run order using QC or reference samples. This is a simpler alternative to LOESS when drift is approximately linear.

Usage

step_measure_drift_linear(
  recipe,
  ...,
  run_order_col = "run_order",
  sample_type_col = "sample_type",
  qc_type = "qc",
  apply_to = c("all", "unknown"),
  min_qc = 3,
  role = NA,
  trained = FALSE,
  skip = FALSE,
  id = recipes::rand_id("measure_drift_linear")
)

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

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

  1. During prep(): A linear regression is fit to QC sample responses vs run order for each feature.

  2. During bake(): Correction factors are calculated as: correction = median(QC_responses) / predicted_value

    Each sample's response is multiplied by the correction factor.

When to Use

Use linear drift correction when:

  • Drift is approximately linear over the run

  • You have fewer QC samples (requires at least 3)

  • You want a more conservative correction

For non-linear drift patterns, use step_measure_drift_qc_loess() or step_measure_drift_spline().

See also

step_measure_drift_qc_loess() for LOESS-based correction, step_measure_drift_spline() for spline-based correction.

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

Examples

library(recipes)

# Data with linear drift
data <- data.frame(
  sample_id = paste0("S", 1:20),
  sample_type = rep(c("qc", "unknown", "unknown", "unknown", "qc"), 4),
  run_order = 1:20,
  feature1 = 100 + (1:20) * 0.5 + rnorm(20, sd = 2)
)

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

corrected <- bake(rec, new_data = NULL)