Smoothing of data for RLum-class objects

The function provides a generalised access point for specific RLum objects. Depending on the input object, the corresponding function will be selected. The smoothing is performed in the internal function .smoothing().

Usage

smooth_RLum(object, ...)

# S4 method for class 'list'
smooth_RLum(object, ...)

# S4 method for class 'RLum.Analysis'
smooth_RLum(object, ...)

# S4 method for class 'RLum.Data.Curve'
smooth_RLum(
  object,
  k = NULL,
  fill = NA,
  align = "right",
  method = "mean",
  p_acceptance = 1e-07
)

Arguments

object: RLum (required): S4 object of class RLum
...: further arguments passed to the specific class method
k: smooth_RLum; integer (with default): window for the rolling mean or median. If NULL, this set automatically (ignored if method = "Carter_etal_2018").
fill: smooth_RLum; numeric (with default): value used to pad the result so to have the same length as the input.
align: smooth_RLum; character (with default): one of "right", "center" or "left", specifying whether the index of the result should be right-aligned (default), centred, or left-aligned compared to the rolling window of observations (ignored if method = "Carter_etal_2018").
method: smooth_RLum; character (with default): smoothing method to be applied: one of "mean", "median" or "Carter_etal_2018".
p_acceptance: smooth_RLum; numeric (with default): probability threshold of accepting a value to be a sample from a Poisson distribution (only used for method = "Carter_etal_2018"). Values that have a Poisson probability below the threshold are replaced by the average over the four neighbouring values.

Value

An object of the same type as the input object provided.

Functions

smooth_RLum(list): Returns a list of RLum objects that had been passed to smooth_RLum
smooth_RLum(RLum.Analysis): Smoothing of RLum.Data records contained in the input object.
smooth_RLum(RLum.Data.Curve): Smoothing of RLum.Data.Curve objects using a rolling mean or median. For methods "mean" and "median", smoothing is performed by rolling mean and rolling median with window of size k. Method "Carter_etal_2018" implements a Poisson smoother for dark-background signals measured by a photomultiplier tube.

Note

Currently only RLum objects of class RLum.Data.Curve and RLum.Analysis (with curve data) are supported.

Function version

0.1.0

How to cite

Kreutzer, S., 2025. smooth_RLum(): Smoothing of data for RLum-class objects. Function version 0.1.0. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Philippe, A., Riedesel, S., Autzen, M., Mittelstrass, D., Gray, H.J., Galharret, J., Colombo, M., Steinbuch, L., Boer, A.d., 2025. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 1.1.1. https://r-lum.github.io/Luminescence/

References

Carter, J., Cresswell, A.J., Kinnaird, T.C., Carmichael, L.A., Murphy, S. & Sanderson, D.C.W., 2018. Non-Poisson variations in photomultipliers and implications for luminescence dating. Radiation Measurements 120, 267-273. doi:10.1016/j.radmeas.2018.05.010

Author

Sebastian Kreutzer, Institute of Geography, Heidelberg University (Germany) , RLum Developer Team

Examples


## load example data
data(ExampleData.CW_OSL_Curve, envir = environment())

## create RLum.Data.Curve object from this example
curve <-
  set_RLum(
      class = "RLum.Data.Curve",
      recordType = "OSL",
      data = as.matrix(ExampleData.CW_OSL_Curve)
  )

## plot data without and with smoothing
plot_RLum(curve)

plot_RLum(smooth_RLum(curve))