Asymmetric Least Squares

Baseline correction based on asymmetric least squares (ALS) algorithm as proposed by Eilers et al. (2005).

Usage

whittaker(x, lambda = 1000, p = 0.001, max.iter = 10)

Arguments

x

A numeric matrix or data frame.

lambda

A numeric value specifying the smoothing parameter, which controls the amount of curvature allowed for the baseline. The smaller the lambda, the more curvature in the baseline fitting. Default is 1000.

p

A numeric value specifying the extent of asymmetry required of the fit. Larger values allow more negative-going regions. Smaller values disallow negative-going regions. p must be between 0 and 1. Default is 0.001.

max.iter

Maximum number of iterations for the algorithm. Default is 10.

Value

A list containing two tibbles:

• correction: The baseline-corrected spectral matrix.

• background: The fitted background emission.

Details

The function applies Eilers' method based on a Whittaker filter. The algorithm estimates a baseline curve by minimizing the asymmetric least squares criterion, which allows for different weights for positive and negative residuals. The resulting baseline curve is subtracted from the input data, providing a baseline-corrected version.

References

• Eilers, P.H.C., Boelens, H.F.M., (2005). Baseline correction with asymmetric least squares smoothing. Leiden University Medical Centre report.

Author

Christian L. Goueguel