calc_EED_Model {Luminescence} | R Documentation |
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. |
MaxIndivDose |
numeric (with default): value specifying the maximum dose taken into
account for the plateau. |
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 |
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 1.1.0. 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. 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)