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.
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[ 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
[.. $hist : list]
Object as produced by the function histogram
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[ PLOT OUTPUT ]
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The function returns two different plot panels.
(1) An abanico plot with the dose values
(2) A histogram panel comprising 3 histograms with the equivalent dose and the bootstrapped average dose and the sigma values.
0.1.4 (2018-01-21 17:22:38)
Christophe, C., Philippe, A., Guerin, G., Kreutzer, S. (2018). calc_AverageDose(): Calculate the Average Dose and the dose rate dispersion. Function version 0.1.4. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J. (2018). Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.8.6. https://CRAN.R-project.org/package=Luminescence
sigma_m
The program requires the input of a known value of sigma_m, which corresponds to the intrinsic overdispersion, as determined by a dose recovery experiment. Then the dispersion in doses (sigma_d) will be that over and above sigma_m (and individual uncertainties sigma_wi).
Guerin, G., Christophe, C., Philippe, A., Murray, A.S., Thomsen, K.J., Tribolo, C., Urbanova, P., Jain, M., Guibert, P., Mercier, N., Kreutzer, S., Lahaye, C., 2017. Absorbed dose, equivalent dose, measured dose rates, and implications for OSL age estimates: Introducing the Average Dose Model. Quaternary Geochronology 1-32. doi:10.1016/j.quageo.2017.04.002
Further reading
Efron, B., Tibshirani, R., 1986. Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy. Statistical Science 1, 54-75.
# NOT RUN {
##Example 01 using package example data
##load example data
data(ExampleData.DeValues, envir = environment())
##calculate Average dose
##(use only the first 56 values here)
AD <- calc_AverageDose(ExampleData.DeValues$CA1[1:56,], sigma_m = 0.1)
##plot De and set Average dose as central value
plot_AbanicoPlot(
data = ExampleData.DeValues$CA1[1:56,],
z.0 = AD$summary$AVERAGE_DOSE)
# }
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