Luminescence (version 0.7.1)

calc_AverageDose: Calculate the Average Dose and the dose rate dispersion

Description

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., 2016

Usage

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

Arguments

data

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

sigma_m

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.

Nb_BE

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

na.rm

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

plot

logical (with default): enables/disables plot output

verbose

logical (with default): enables/disables terminal output

...

further arguments that can be passed to 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).

Function version

0.1.3 (2017-01-24 21:10:47)

How to cite

Christophe, C., Philippe, A., Guerin, G., Kreutzer, S. (2017). calc_AverageDose(): Calculate the Average Dose and the dose rate dispersion. Function version 0.1.3. In: Kreutzer, S., Dietze, M., Burow, C., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J. (2017). Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.7.1. https://CRAN.R-project.org/package=Luminescence

Details

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).

References

TODO: Add Guerin et al., 2016 once it has been published

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, hist

Examples

##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)