Baseline estimation with asymmetric least squares smoothing.

## Usage

``````baseline_asls(x, y, ...)

# S4 method for class 'numeric,numeric'
baseline_asls(x, y, p = 0.01, lambda = 10^4, stop = 100)

# S4 method for class 'ANY,missing'
baseline_asls(x, p = 0.01, lambda = 10^4, stop = 100)``````

## Arguments

x, y

A `numeric` vector. If `y` is missing, an attempt is made to interpret `x` in a suitable way (see `grDevices::xy.coords()`).

...

Currently not used.

p

A length-one `numeric` vector giving the asymmetry (\(0.001 < p < 0.1\) is a good choice for a signal with positive peaks).

lambda

A length-one `numeric` vector giving the smoothing parameter.

stop

An `integer` giving the stopping rule (i.e. maximum number of iterations).

## Value

Returns a `list` with two components `x` and `y`.

## References

Eilers, P. H. C. & Boelens, H. F. M. (2005). Baseline Correction with Asymmetric Least Squares Smoothing.

`signal_correct()`

Other baseline estimation methods: `baseline_linear()`, `baseline_peakfilling()`, `baseline_polynomial()`, `baseline_rollingball()`, `baseline_rubberband()`, `baseline_snip()`

## Author

P. H. C. Eilers and H. F. M. Boelens (original Matlab code)

## Examples

``````## X-ray diffraction
data("XRD")

## Subset from 20 to 70 degrees
XRD <- signal_select(XRD, from = 20, to = 70)

## Plot spectrum
plot(XRD, type = "l", xlab = expression(2*theta), ylab = "Count")

## Polynomial baseline
baseline <- baseline_asls(XRD, p = 0.005, lambda = 10^7)

lines(baseline, type = "l", col = "red")

``````