baselines: Polynomial Baseline Fitting These functions fit polynomal...
In hyperSpec: Work with Hyperspectral Data, i.e. Spectra + Meta Information (Spatial, Time, Concentration, ...)

Description Usage Arguments Details Value Author(s) See Also Examples

Both functions fit polynomials to be used as baselines. If apply.to is NULL, a hyperSpec object with the polynomial coefficients is returned, otherwise the polynomials are evaluated on the spectral range of apply.to.

spc.fit.poly(
  fit.to,
  apply.to = NULL,
  poly.order = 1,
  offset.wl = !(is.null(apply.to))
)

spc.fit.poly.below(
  fit.to,
  apply.to = fit.to,
  poly.order = 1,
  npts.min = max(round(nwl(fit.to) * 0.05), 3 * (poly.order + 1)),
  noise = 0,
  offset.wl = FALSE,
  max.iter = nwl(fit.to),
  stop.on.increase = FALSE,
  debuglevel = hy.getOption("debuglevel")
)

`fit.to`	`hyperSpec` object on which the baselines are fitted
`apply.to`	`hyperSpec` object on which the baselines are evaluted If `NULL`, a `hyperSpec` object containing the polynomial coefficients rather than evaluted baselines is returned.
`poly.order`	order of the polynomial to be used
`offset.wl`	should the wavelength range be mapped to -> [0, delta wl]? This enhances numerical stability.
`npts.min`	minimal number of points used for fitting the polynomial
`noise`	noise level to be considered during the fit. It may be given as one value for all the spectra, or for each spectrum separately.
`max.iter`	stop at the latest after so many iterations.
`stop.on.increase`	additional stopping rule: stop if the number of support points would increase, regardless whether npts.min was reached or not.
`debuglevel`	additional output: `1` shows `npts.min`, `2` plots support points for the final baseline of 1st spectrum, `3` plots support points for 1st spectrum, `4` plots support points for all spectra.

spc.fit.poly calculates the least squares fit of order poly.order to the complete spectra given in fit.to. Thus fit.to needs to be cut appropriately.

hyperspec object containing the baselines in the spectra matrix, either as polynomial coefficients or as polynomials evaluted on the spectral range of apply.to

C. Beleites

vignette ("baseline", package = "hyperSpec")

see options for more on debuglevel

## Not run: vignette ("baseline", package = "hyperSpec")

spc <- chondro [1 : 10]
baselines <- spc.fit.poly(spc [,, c (625 ~ 640, 1785 ~ 1800)], spc)
plot(spc - baselines)


baselines <- spc.fit.poly.below (spc)
plot (spc - baselines)

spc.fit.poly.below(chondro [1:3], debuglevel = 1)
spc.fit.poly.below(chondro [1:3], debuglevel = 2)
spc.fit.poly.below(chondro [1:3], debuglevel = 3, noise = sqrt (rowMeans (chondro [[1:3]])))