baseline.rfbaseline: Robust Baseline Estimation

In baseline: Baseline Correction of Spectra

Robust Baseline Estimation

Description

Wrapper for Andreas F. Ruckstuhl, Matthew P. Jacobson, Robert W. Field, James A. Dodd's algorithm based on LOWESS and weighted regression

Usage

baseline.rfbaseline(spectra, span = 2/3, NoXP = NULL, maxit = c(2, 2),
  b = 3.5, weight = NULL, Scale = function(r) median(abs(r))/0.6745,
  delta = NULL, SORT = FALSE, DOT = FALSE, init = NULL)

Arguments

spectra	Matrix with spectra in rows
span	Amount of smoothing (by fraction of points)
NoXP	Amount of smoothing (by number of points)
maxit	Maximum number of iterations in robust fit
b	Tuning constant in the biweight function
weight	Optional weights to be given to individual observations
Scale	S function specifying how to calculate the scale of the residuals
delta	Nonnegative parameter which may be used to save computation. (See rfbaseline
SORT	Boolean variable indicating whether x data must be sorted.
DOT	Disregard outliers totally (boolean)
init	Values of initial fit

Details

Most of the code is the original code as given by the authors. The ability to sort by X-values has been removed and ability to handle multiple spectra has been added

Value

baseline	Matrix of baselines corresponding to spectra spectra
corrected	Matrix of baseline corrected spectra

Author(s)

Kristian Hovde Liland and Bjørn-Helge Mevik

References

Andreas F. Ruckstuhl, Matthew P. Jacobson, Robert W. Field, James A. Dodd: Baseline subtraction using robust local regression estimation

Examples

data(milk)
bc.rbe <- baseline(milk$spectra[1,, drop=FALSE], method='rfbaseline',
  span=NULL, NoXP=1000)
## Not run: 
	plot(bc.rbe)

## End(Not run)

baseline documentation built on Nov. 18, 2023, 5:14 p.m.