Baseline correction based on asymmetrically reweighted penalized least squares smoothing algorithm as proposed by Baek et al. (2015).
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.
- ratio
A numeric value specifying the convergence ratio for the iterative algorithm. The algorithm stops when the relative change in the weights is less than this ratio. Typical values are between 0.01 and 0.1. Default is 0.05.
- 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 algorithm estimates a baseline curve by iteratively updating weights based on the residuals and minimizing a penalized least squares criterion. The resulting baseline curve is subtracted from the input data, providing a baseline-corrected version.