Luminescence (version 0.8.6)

calc_AverageDose: Calculate the Average Dose and the dose rate dispersion


This functions calculates the Average Dose and their extrinsic dispersion and estimates the standard errors by bootstrapping based on the Average Dose Model by Guerin et al., 2017


calc_AverageDose(data, sigma_m = NULL, Nb_BE = 500, na.rm = TRUE,
  plot = TRUE, verbose = TRUE, ...)



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


numeric (required): the overdispersion resulting from a dose recovery experiment, i.e. when all grains have received the same dose. Indeed in such a case, any overdispersion (i.e. dispersion on top of analytical uncertainties) is, by definition, an unrecognised measurement uncertainty.


integer (with default): sample size used for the bootstrapping


logical (with default): exclude NA values from the data set prior to any further operation.


logical (with default): enables/disables plot output


logical (with default): enables/disables terminal output


further arguments that can be passed to graphics::hist. As three plots are returned all arguments need to be provided as list, e.g., main = list("Plot 1", "Plot 2", "Plot 3"). Note: not all arguments of hist are supported, but the output of hist is returned and can be used of own plots.

Further supported arguments: mtext (character), rug (TRUE/FALSE).


The function returns numerical output and an (optional) plot.

----------------------------------- [ NUMERICAL OUTPUT ] ----------------------------------- RLum.Results-object

slot: @data

[.. $summary : data.frame]

Column Type Description
AVERAGE_DOSE numeric the obtained averge dose
AVERAGE_DOSE.SE numeric the average dose error
SIGMA_D numeric sigma
SIGMA_D.SE numeric standard error of the sigma
IC_AVERAGE_DOSE.LEVEL character confidence level average dose
IC_AVERAGE_DOSE.LOWER character lower quantile of average dose
IC_AVERAGE_DOSE.UPPER character upper quantile of average dose
IC_SIGMA_D.LEVEL integer confidence level sigma
IC_SIGMA_D.LOWER character lower sigma quantile
IC_SIGMA_D.UPPER character upper sigma quantile

[.. $dstar : matrix]

Matrix with bootstrap values

[.. $hist : list]

Object as produced by the function histogram

------------------------ [ PLOT OUTPUT ] ------------------------

The function returns two different plot panels.

(1) An abanico plot with the dose values

(2) A histogram panel comprising 3 histograms with the equivalent dose and the bootstrapped average dose and the sigma values.

Function version

0.1.4 (2018-01-21 17:22:38)

How to cite

Christophe, C., Philippe, A., Guerin, G., Kreutzer, S. (2018). calc_AverageDose(): Calculate the Average Dose and the dose rate dispersion. Function version 0.1.4. 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.



The program requires the input of a known value of sigma_m, which corresponds to the intrinsic overdispersion, as determined by a dose recovery experiment. Then the dispersion in doses (sigma_d) will be that over and above sigma_m (and individual uncertainties sigma_wi).


Guerin, G., Christophe, C., Philippe, A., Murray, A.S., Thomsen, K.J., Tribolo, C., Urbanova, P., Jain, M., Guibert, P., Mercier, N., Kreutzer, S., Lahaye, C., 2017. Absorbed dose, equivalent dose, measured dose rates, and implications for OSL age estimates: Introducing the Average Dose Model. Quaternary Geochronology 1-32. doi:10.1016/j.quageo.2017.04.002

Further reading

Efron, B., Tibshirani, R., 1986. Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy. Statistical Science 1, 54-75.

See Also

read.table, graphics::hist


##Example 01 using package example data
##load example data
data(ExampleData.DeValues, envir = environment())

##calculate Average dose
##(use only the first 56 values here)
AD <- calc_AverageDose(ExampleData.DeValues$CA1[1:56,], sigma_m = 0.1)

##plot De and set Average dose as central value
 data = ExampleData.DeValues$CA1[1:56,],
 z.0 = AD$summary$AVERAGE_DOSE)

# }