The error can be reduced and sample size increased for specific purpose.

tune_Data(data, decrease.error = 0, increase.data = 0)

Arguments

data

data.frame (required): input values, structure: data (values[,1]) and data error (values [,2]) are required

decrease.error

numeric: factor by which the error is decreased, ranges between 0 and 1.

increase.data

numeric: factor by which the error is decreased, ranges between 0 and Inf.

Value

Returns a data.frame with tuned values.

Note

You should not use this function to improve your poor data set!

Function version

0.5.0

Author

Michael Dietze, GFZ Potsdam (Germany) , RLum Developer Team

How to cite

Dietze, M., 2021. tune_Data(): Tune data for experimental purpose. Function version 0.5.0. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Riedesel, S., Autzen, M., Mittelstrass, D., Gray, H.J., 2021. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.9.11. https://CRAN.R-project.org/package=Luminescence

Examples

## load example data set data(ExampleData.DeValues, envir = environment()) x <- ExampleData.DeValues$CA1 ## plot original data plot_AbanicoPlot(data = x, summary = c("n", "mean"))
## decrease error by 10 % plot_AbanicoPlot(data = tune_Data(x, decrease.error = 0.1), summary = c("n", "mean"))
#> Warning: Dear kreutzer, these activities on your Darwin machine have been tracked and will be submitted to the R.Lum data base. Cheating does not pay off! [2021-04-29 11:15:48]
## increase sample size by 200 % #plot_AbanicoPlot(data = tune_Data(x, increase.data = 2) , # summary = c("n", "mean"))