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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 bootstrappinglogical (with default): exclude NA values
from the data set prior to any further operation.logical (with default): enables/disables plot outputlogical (with default): enables/disables terminal outputhist. 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).
sigma_mThe 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).
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.
read.table, 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$CA1[1:56,],
sigma_m = 0.1)
##plot De and set Average dose as central value
plot_AbanicoPlot(
data = ExampleData.DeValues$CA1[1:56,],
z.0 = AD$summary$Average_DOSE)
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