calc_EED_Model: Modelling Exponential Exposure Distribution

In Luminescence: Comprehensive Luminescence Dating Data Analysis

Modelling Exponential Exposure Distribution

Description

Modelling incomplete and heterogeneous bleaching of mobile grains partially exposed to the light, an implementation of the EED model proposed by Guibert et al. (2019).

Usage

calc_EED_Model(
  data,
  D0 = 120L,
  expected_dose,
  MinIndivDose = NULL,
  MaxIndivDose = NULL,
  kappa = NULL,
  sigma_distr = NULL,
  n.simul = 5000L,
  n.minSimExp = 50L,
  sample_name = "",
  method_control = list(),
  verbose = TRUE,
  plot = TRUE,
  ...
)

Arguments

data	data.frame (required): input data consisting of two columns, the De and the SE(De). Values are expected in Gy
D0	integer (with default): D0 value (in Gy), defining the characterisation behaviour of the quartz.
expected_dose	numeric (required): expected equivalent dose
MinIndivDose	numeric (with default): value specifying the minimum dose taken into account for the plateau. NULL applies all values.
MaxIndivDose	numeric (with default): value specifying the maximum dose taken into account for the plateau. NULL applies all values.
kappa	numeric (optional): positive dimensionless exposure parameter characterising the bleaching state of the grains. Low values (< 10) indicate poor bleaching
sigma_distr	numeric (optional): positive dose rate parameter, representing the dose variability to which the grains were exposed ##TODO perhaps it should be renamed
n.simul	integer (with default): number of simulations
n.minSimExp	integer (with default): number of MC runs for calculating the uncertainty contribution from the sampling
sample_name	character (with default): name of the sample
method_control	list (with default): additional deep control parameters, parameters need to be provided as named list, see details
verbose	logical (with default): enable/disable output to the terminal.
plot	logical (with default): enable/disable the plot output.
...	further parameters that can be passed to better control the plot output. Support arguments are xlab, xlim.

Details

The function is an implementation and enhancement of the scripts used in Guibert et al. (2019). The implementation supports a semi-automated estimation of the parameters kappa and sigma_distr. If set to NULL, a surface interpolation is used to estimated those values.

Method control parameters

ARGUMENT	FUNCTION	DEFAULT	DESCRIPTION
lower	-	c(0.1,0,0)	set lower bounds for kappa, sigma, and the expected De in auto mode
upper	-	c(1000,2)	set upper bounds for kappa, sigma, and the expected De in auto mode
iter_max	-	1000	maximum number for iterations for used to find kappa and sigma
trace	-	FALSE	enable/disable terminal trace mode; overwritten by global argument verbose
trace_plot	-	FALSE	enable/disable additional trace plot output; overwritten by global argument verbose

Function version

0.1.0

How to cite

Guibert, P., Kreutzer, S., 2025. calc_EED_Model(): Modelling Exponential Exposure Distribution. 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 1.0.1. https://r-lum.github.io/Luminescence/

Author(s)

Pierre Guibert, IRAMAT-CRP2A, UMR 5060, Université Bordeaux Montaigne (France), Sebastian Kreutzer, Geography & Earth Sciences, Aberystwyth University (United Kingdom) , RLum Developer Team

References

Guibert, P., Christophe, C., Urbanova, P., Guérin, G., Blain, S., 2017. Modelling incomplete and heterogeneous bleaching of mobile grains partially exposed to the light - Towards a new tool for single grain OSL dating of poorly bleached mortars. Radiation Measurements 107, 48–57. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.radmeas.2017.10.003")}

See Also

RLum.Results, calc_MinDose, calc_FuchsLang2001, calc_IEU, calc_FiniteMixture

Examples

data(ExampleData.MortarData, envir = environment())
calc_EED_Model(
 data = MortarData,
 kappa = 14,
 sigma_distr = 0.37,
 expected_dose = 11.7)

## automated estimation of
## sigma_distribution and
## kappa
## Not run: 
 calc_EED_Model(
 data = MortarData,
 kappa = NULL,
 sigma_distr = NULL,
 expected_dose = 11.7)

## End(Not run)

Luminescence documentation built on April 3, 2025, 7:52 p.m.