data: either a vector or a data matrix containing spectra as rows
lambda
smoothing parameter (generally 1e5 - 1e8)
p
asymmetry parameter
eps
numerical precision for convergence
maxit
max number of iterations. If no convergence is reached, a
warning is issued.
Value
An estimated baseline
Details
Asymmetric least squares (not to be confused with alternating
least squares) assigns different weights to the data points that are
above and below an iteratively estimated trendline, respectively. In
this case, the asymmetry parameter p (0 <= p <= 1) is the weight for
points above the trendline, whereas 1-p is the weight for points below
it. Naturally, p should be small for estimating baselines. The
parameter lambda controls the amount of smoothing: the larger it is,
the smoother the trendline will be.
Boelens, H.F.M., Eilers, P.H.C., Hankemeier, T. (2005) "Sign constraints improve the detection of differences between complex spectral data sets: LC-IR as an example", Analytical Chemistry, 77, 7998 -- 8007.