numOSL (version 2.0)

lsNORM: Regenerative dose signal data optimization using the least-squares normalisation (LS-normalisation) procedure

Description

Optimizing regenerative dose signal data according to the least-squares normalisation (LS-normalisation) procedure using an iterative scaling and fitting procedure proposed by Li et al. (2016).

Usage

lsNORM(Data, model = "gok", origin = FALSE, maxiter = 10, weight = TRUE, plot = TRUE)

Arguments

Data

matrix(required): data used for performing the LS-normalisation procedure, it should contain five columns (i.e., Grain.NO, SAR.Cycle, Dose, Signal, and Signal.Err), see SARdata for details

model

character(with default): model used for growth curve fitting, see fitGrowth for available models

origin

logical(optional): force the growth curve to pass the origin or not

maxiter

integer(with default): allowed maximum number of iterations during the LS-normalisation optimization process

weight

logical(with default): fit the growth curve using a weighted or an un-weighted procedure, see function fitGrowth for details

plot

logical(with default): plot the output or not

Value

Return a list that contains the following elements:

optData

regenerative dose signals optimized using the LS-normalisation procedure

sf

total scaling factor for regenerative dose signals for each aliquot (grain)

iter

number of iterations required

LMpars1

optimized parameters for the fitted growth curve using original regenerative dose signals

value1

minimized objective for the fitted growth curve using original regenerative dose signals

LMpars2

optimized parameters for the fitted growth curve using LS-normalised regenerative dose signals

value2

minimized objective for the fitted growth curve using LS-normalised regenerative dose signals

Details

Function lsNORM is used for optimizing regenerative dose signal data from a number of grains (aliquots) according to the least-squares normalisation (LS-normalisation) procedure outlined by Li et al. (2016) using an iterative scaling and fitting procedure. The LS-normalisation procedure for growth curve optimization involves the following steps: (1) Fit standardised regenerative dose signals from all of the aliquots; (2) Re-scale the individual growth curve from each aliquot using a scaling factor. The scaling factor for each aliquot is determined in a way such that the sum of squared residuals is minimized. Each aliquots is treated individually, and different scaling factors are calculated for different aliquots. (3) Iterate the fitting (step 1) and re-scaling (step 2). The iterative procedure is performed repeatedly until there is negligible change in the relative standard deviation of the normalised growth curve data.

References

Li B, Jacobs Z, Roberts RG, 2016. Investigation of the applicability of standardised growth curves for OSL dating of quartz from Haua Fteah cave, Libya. Quaternary Geochronology, 35: 1-15.

See Also

fitGrowth; calSGCED

Examples

  data(SARdata)
  # Use only the first five aliquots of SARdata.
  Data <- SARdata[1:40,]
  lsNORM(Data, model="gok")