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Get calibration metrics

Source: R/calc-calibrations.R
get_calibration_metrics.Rd

Extracts calibration fit metrics from a MRMhubExperiment object.

Usage

get_calibration_metrics(
  data = NULL,
  with_lod = TRUE,
  with_loq = TRUE,
  with_coefficients = TRUE,
  with_sigma = TRUE
)

Arguments

data

A MRMhubExperiment object with QC metrics.

with_lod

Whether to include LoD in output. Default is TRUE.

with_loq

Whether to include LoQ in output. Default is TRUE.

with_coefficients

Whether to include regression coefficients. Default is TRUE.

with_sigma

Whether to include sigma in output. Default is TRUE.

Value

A tibble with exported calibration metrics.

Details

Requires prior computation of regression results using calc_calibration_results(). See its documentation for details.

Returned Details and Metrics

  • feature_id: Feature identifier.

  • is_quantifier: Logical, indicates if the feature is a quantifier.

  • fit_model: Regression model used for fitting.

  • weighting: Weighting method used in fitting.

  • lowest_cal: Lowest nonzero calibration concentration.

  • highest_cal: Highest calibration concentration.

  • r.squared: R-squared value, indicating goodness of fit. For a weighted fit this is the weighted coefficient of determination (computed from weighted sums of squares), matching the value reported by vendor software such as Agilent MassHunter for the same weighted curve.

  • coef_a: Intercept of the regression line

  • coef_b: Slope of the regression line in linear models, or coefficient of the linear term (x) in quadratic models.

  • coef_c: Coefficient of the quadratic term (x^2) in quadratic models. Returns NA for linear models.

  • sigma: Standard deviation of residuals.

  • reg_failed: TRUE if regression fitting failed.

  • LoD = 3.3× the sample standard error of residuals / slope of the regression (see Notes).

  • LoQ = 10× the sample standard error of residuals / slope of the regression (see Notes).

Note: LoD/LoQ follow the ICH Q2(R1/R2) approach (3.3 sigma / S and 10 sigma / S). The slope S is the slope of the calibration curve at zero concentration (the linear coefficient coef_b); for a quadratic fit the quadratic term does not contribute to this slope. The response sigma is selectable in calc_calibration_results() via lod_sigma (residual standard error, the default, or the standard error of the intercept); the sigma column reported here is always the residual standard error.

For a weighted fit (1/x, 1/x^2, 1/sqrt(x)) sigma is R's weighted residual standard error, which is not on the raw response scale that the ICH 3.3 sigma / S formula assumes, so the reported LoD/LoQ are approximate (typically slightly optimistic for 1/x). Use fit_weighting = "none" if you require the strict ICH response-scale Sy/x; the back-calculated concentrations themselves are unaffected.