Luminescence (version 0.8.6)

calc_CommonDose: Apply the (un-)logged common age model after Galbraith et al. (1999) to a given De distribution


Function to calculate the common dose of a De distribution.


calc_CommonDose(data, sigmab, log = TRUE, ...)



'>RLum.Results or data.frame (required): for data.frame: two columns with De (data[,1]) and De error (data[,2])


numeric (with default): additional spread in De values. This value represents the expected overdispersion in the data should the sample be well-bleached (Cunningham & Walling 2012, p. 100). NOTE: For the logged model (log = TRUE) this value must be a fraction, e.g. 0.2 (= 20 %). If the un-logged model is used (log = FALSE), sigmab must be provided in the same absolute units of the De values (seconds or Gray).


logical (with default): fit the (un-)logged central age model to De data


currently not used.


Returns a terminal output. In addition an '>RLum.Results object is returned containing the following element:


data.frame summary of all relevant model results.


data.frame original input data


list used arguments


call the function call

The output should be accessed using the function get_RLum

Function version

0.1.1 (2018-01-21 17:22:38)

How to cite

Burow, C. (2018). calc_CommonDose(): Apply the (un-)logged common age model after Galbraith et al. (1999) to a given De distribution. Function version 0.1.1. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J. (2018). Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.8.6.


(Un-)logged model

When log = TRUE this function calculates the weighted mean of logarithmic De values. Each of the estimates is weighted by the inverse square of its relative standard error. The weighted mean is then transformed back to the dose scale (Galbraith & Roberts 2012, p. 14).

The log transformation is not applicable if the De estimates are close to zero or negative. In this case the un-logged model can be applied instead (log = FALSE). The weighted mean is then calculated using the un-logged estimates of De and their absolute standard error (Galbraith & Roberts 2012, p. 14).


Galbraith, R.F. & Laslett, G.M., 1993. Statistical models for mixed fission track ages. Nuclear Tracks Radiation Measurements 4, 459-470.

Galbraith, R.F., Roberts, R.G., Laslett, G.M., Yoshida, H. & Olley, J.M., 1999. Optical dating of single grains of quartz from Jinmium rock shelter, northern Australia. Part I: experimental design and statistical models. Archaeometry 41, 339-364.

Galbraith, R.F. & Roberts, R.G., 2012. Statistical aspects of equivalent dose and error calculation and display in OSL dating: An overview and some recommendations. Quaternary Geochronology 11, 1-27.

Further reading

Arnold, L.J. & Roberts, R.G., 2009. Stochastic modelling of multi-grain equivalent dose (De) distributions: Implications for OSL dating of sediment mixtures. Quaternary Geochronology 4, 204-230.

Bailey, R.M. & Arnold, L.J., 2006. Statistical modelling of single grain quartz De distributions and an assessment of procedures for estimating burial dose. Quaternary Science Reviews 25, 2475-2502.

Cunningham, A.C. & Wallinga, J., 2012. Realizing the potential of fluvial archives using robust OSL chronologies. Quaternary Geochronology 12, 98-106.

Rodnight, H., Duller, G.A.T., Wintle, A.G. & Tooth, S., 2006. Assessing the reproducibility and accuracy of optical dating of fluvial deposits. Quaternary Geochronology, 1 109-120.

Rodnight, H., 2008. How many equivalent dose values are needed to obtain a reproducible distribution?. Ancient TL 26, 3-10.

See Also

calc_CentralDose, calc_FiniteMixture, calc_FuchsLang2001, calc_MinDose


## load example data
data(ExampleData.DeValues, envir = environment())

## apply the common dose model

# }