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Modelling Exponential Exposure Distribution

Source: R/calc_EED_Model.R
calc_EED_Model.Rd

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

ARGUMENTFUNCTIONDEFAULTDESCRIPTION
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-1000maximum number for iterations for used to find kappa and sigma
trace-FALSEenable/disable terminal trace mode; overwritten by global argument verbose
trace_plot-FALSEenable/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 1.1.1. https://r-lum.github.io/Luminescence/

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. doi:10.1016/j.radmeas.2017.10.003

See also

RLum.Results, calc_MinDose, calc_FuchsLang2001, calc_IEU, calc_FiniteMixture

Author

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

Examples


data(ExampleData.MortarData, envir = environment())
calc_EED_Model(
 data = MortarData,
 kappa = 14,
 sigma_distr = 0.37,
 expected_dose = 11.7)
#> 
#> [calc_EED_Model()]
#> 
#> ------------------------------------ 
#> 
#>  Maximal Individual Equivalent Dose integrated:  20.93 Gy
#>  Averaged Corrected Equivalent Dose:  11.42 ± 0.67 Gy


#> 
#>  [RLum.Results-class]
#> 	 originator: calc_EED_Model()
#> 	 data: 1
#>  	 .. $M_Data : matrix
#> 	 additional info elements:  1 

## automated estimation of
## sigma_distribution and
## kappa
if (FALSE) { # \dontrun{
 calc_EED_Model(
 data = MortarData,
 kappa = NULL,
 sigma_distr = NULL,
 expected_dose = 11.7)
} # }