model_DoseRate: Model dose rate evolution in carbonate-rich samples

View source: R/model_DoseRate.R

model_DoseRateR Documentation

Model dose rate evolution in carbonate-rich samples


This function models the dose rate evolution in carbonate enrich environments. For the calculation internal functions are called.


  DR_conv_factors = NULL,
  length_step = 1L,
  max_time = 500L,
  n.MC = 100,
  method_control = list(),
  txtProgressBar = TRUE,
  verbose = TRUE,
  plot = TRUE,
  par_local = TRUE,



data.frame (required): input data following the structure given in the example data set data(Example_Data). The input data.frame should have at least one row (i.e. values for one sample). For multiple rows the function is automatically re-called.


character (optional): applied dose rate conversion factors, allowed input values are "Carb2007", "Adamiec_Aitken_1998", "Guerin_et_al_2011", "Liritzis_et_al_2013". NULL triggers the default, which is "Carb2007"


numeric (with default): step length used for the calculation


numeric (with default): maximum temporal search range


numeric (with default): number of Monte Carlo runs used for the error calculation


(optional): additional arguments that can be provided to the control the the modelling. See details for further information.


logical (with default): enables/disables the txtProgressBar for the MC runs


logical (with default): enables/disables verbose mode


logical (with default): enables/disables plot output


logical (with default): enables/disable local par settings, If set to FALSE all global par settings are accepted.


further arguments passed to the underlying plot functions, see also details for further information. Supported standard arguments are mfrow, xlim, xlab.


This function is the starting point for the dose rate modelling for carbonate enrich environments. It provides basically the same functionality as the original version of 'Carb', i.e. you should be also aware of the limitations of this modelling approach. In particular: The model assumes a linear carbonate mass increase due to post-depositional processes. Please read the references cited blow.

Uncertainty estimation

For estimating the uncertainties, Monte-Carlo (MC) simulation runs are used. For very small values (close to 0) this can, however, lead to edge effects (similar in 'Carb') since values below 0 are set to 0.


The function returns numerical and graphical output


  • A data.frame which is the combination of the input and values calculated by this function.


Upper plot: Dose rate evolution over time backwards. The solid black line is the calculation output, the grey shaded area indicates the 2-sigma error margins. The dashed blue line is an indicator of the quality of the error estimations based on Monte Carlo (MC) runs.The closer it follows the black line, the more reliable are the given error margins.

Lower plot: Totally absorbed dose over time. The plot is an representation of the 'new' age based on the carbonate modelling.

Function version


How to cite

Kreutzer, S., 2022. model_DoseRate(): Model dose rate evolution in carbonate-rich samples. Function version 0.2.1. In: Kreutzer, S., Nathan, R.P., Mauz, B., 2022. RCarb: Dose Rate Modelling of Carbonate-Rich Samples . R package version 0.1.5.


Sebastian Kreutzer, Geography & Earth Sciences, Aberystwyth University (United Kingdom); based on 'MATLAB' code given in file Carb_2007a.m of Carb


Mauz, B., Hoffmann, D., 2014. What to do when carbonate replaced water: Carb, the model for estimating the dose rate of carbonate-rich samples. Ancient TL 32, 24-32.

Nathan, R.P., Mauz, B., 2008. On the dose-rate estimate of carbonate-rich sediments for trapped charge dating. Radiation Measurements 43, 14-25. doi: 10.1016/j.radmeas.2007.12.012

Further reading

Nathan, R.P., 2010. Numerical modelling of environmental dose rate and its application to trapped-charge dating. DPhil thesis, St Hugh's College, Oxford.

Zimmerman, D.W., 1971. Thermoluminescent dating using fine grains from pottery. Archaeometry 13, 29–52.doi: 10.1111/j.1475-4754.1971.tb00028.x


##load example data
data("Example_Data", envir = environment())

##run the function for one sample from
##the dataset
data = Example_Data[14,],
n.MC = 2,
txtProgressBar = FALSE

RCarb documentation built on March 18, 2022, 7:50 p.m.