baseline (version 1.3-1)

baseline.als: Asymmetric Least Squares

Description

Baseline correction by 2nd derivative constrained weighted regression. Original algorithm proposed by Paul H. C. Eilers and Hans F.M. Boelens

Usage

baseline.als(spectra, lambda = 6, p = 0.05, maxit = 20)

Arguments

spectra

Matrix with spectra in rows

lambda

2nd derivative constraint

p

Weighting of positive residuals

maxit

Maximum number of iterations

Value

baseline

Matrix of baselines corresponding to spectra spectra

corrected

Matrix of baseline corrected spectra

wgts

Matrix of final regression weights

Details

Iterative algorithm applying 2nd derivative constraints. Weights from previous iteration is p for positive residuals and 1-p for negative residuals.

References

Paul H. C. Eilers and Hans F.M. Boelens: Baseline Correction with Asymmetric Least Squares Smoothing

Examples

# NOT RUN {
data(milk)
bc.als <- baseline(milk$spectra[1,, drop=FALSE], lambda=10, method='als')
# }
# NOT RUN {
plot(bc.als)
# }