Fit a dose response curve to ESR data and create a DE-DEmax Plot after Schellmann & Radtke (2001)

This function repeatedly fits a single saturating exponential to ESR data and calculates the De while removing the highest datapoint datapoint after each iteration. It then creates a DE-DEmax-Plot after Schellmann & Radtke (2001) where the DE is plotted against the number of datapoints used for fitting.

calc_DePlateau(input.data, min.DosePoints = 5, fit.weights = "equal",
  model = "EXP", mean.natural = FALSE, show.grid = TRUE,
  output.console = TRUE, ...)

Arguments

input.data	`data.frame` (required): data frame with two columns for x=Dose, y=ESR.intensity. Optional: a third column containing individual ESR intensity errors can be provided.
min.DosePoints	`integer` (with default): minimum number of datapoints used for fitting the single saturating exponential.
fit.weights	`logical` (with default): option whether the fitting is done with equal weights (`'equal'`) or weights proportional to intensity (`'prop'`). If individual ESR intensity errors are provided, these can be used as weights by using `'error'`.
model	`character` (with default): Currently implemented models: single-saturating exponential (`"EXP"`), linear (`"LIN"`).
mean.natural	`logical` (with default): If there are repeated measurements of the natural signal should the mean amplitude be used for fitting?
show.grid	`logical` (with default): show horizontal grid lines in plots (`TRUE/FALSE`)
output.console	`logical` (with default): plot console output (`TRUE/FALSE`)
...	further arguments passed to `plot` and `par`.

Value

Returns terminal output and a plot. In addition, a list is returned containing the following elements:

output

data frame containing the De (datapoints n, De, De.Error, max.Dose).

Details

Fitting methods

For fitting of the dose response curve the nls function with the port algorithm is used. A single saturating exponential in the form of $$y = a*(1-exp(-(x+c)/b))$$ is fitted to the data. Parameters b and c are approximated by a linear fit using lm.

Fit weighting

If 'equal' all datapoints are weighted equally. For 'prop' the datapoints are weighted proportionally by their respective ESR intensity: $$fit.weights = 1/intensity/(sum(1/intensity))$$ If individual errors on ESR intensity are available, choosing 'error' enables weighting in the form of: $$fit.weights = 1/error/(sum(1/error))$$

Note

Fitting of the dose response curve using fit_DRC is largely derived from the plot_GrowthCurve function of the 'Luminescence' package by Kreutzer et al. (2012).

Fitting methods
Currently, only fitting of a single saturating exponential is supported. Fitting of two exponentials or an exponential with a linear term may be implemented in a future release.

References

Galbraith, R.F. & Roberts, R.G., 2012. Statistical aspects of equivalent dose and error calculation and display in OSL dating: An overview and some recommendations. Quaternary Geochronology, 11, pp. 1-27.

Kreutzer, S., Schmidt, C., Fuchs, M.C., Dietze, M., Fischer, M., Fuchs, M., 2012. Introducing an R package for luminescence dating analysis. Ancient TL, 30 (1), pp 1-8.

Examples


# NOT RUN {
##load example data
data(ExampleData.De, envir = environment())

#calculate and plot De-Dmax Plateau
calc_DePlateau(input.data = ExampleData.De,
               min.DosePoints = 5,
               fit.weights = 'prop',
               show.grid = TRUE,
               output.console = FALSE)
# }