plot_RLumCarlo {RLumCarlo}

Plot RLumCarlo Monte-Carlo Simulation Results

Description

Visualise 'RLumCarlo' modelling results without extracting the values manually. Typically visualised are the averaged signal or the number of remaining electrons, with a polygon indicating modelling uncertainties.

Usage

plot_RLumCarlo(
  object,
  plot_value = "mean",
  plot_uncertainty = "range",
  FUN = NULL,
  norm = FALSE,
  add = FALSE,
  ...
)

Arguments

object

list of class RLumCarlo_Model_Output (required): input object to be plotted, usually the required input object is generated by one of the functions starting with run_. Alternatively a list of such objects can be provided.

plot_value

character (with default): type of curve value to be displayed. Allowed are mean (the default) and sum (meaningful if different systems are combined). NULL disables the value visualisation.

plot_uncertainty

character (with default): type of the displayed uncertainty. Allowed values are range, sd (standard deviation) and var (variance). NULL disables the uncertainty visualisation.

FUN

function (optional): own function that can be applied to the y-values before normalisation and plotting

norm

logical (with default): normalise curve to the highest intensity value

add

logical (with default): allows overplotting of results by adding curves to an existing plot. This argument is handled automatically if object is of type list

...

further argument, that can be passed to control the plot output largely following the argument names in graphics::plot.default. Currently supported are: xlab, ylab, xlim, ylim, main, lwd, type, pch, lty,col, grid, legend. The arguments lwd, type, pch, lty, col can be provided as a vector if object is a list

Details

For colouring the curves, the package khroma::khroma-package is used to provide colours that can be best distinguished, in particular by colour-blind users.

Value

This function returns a graphical output which is the visualisation of the modelling output.

Function version

0.1.0

How to cite

Kreutzer, S., Friedrich, J., 2022. plot_RLumCarlo(): Plot RLumCarlo Monte-Carlo Simulation Results. Function version 0.1.0. In: Friedrich, J., Kreutzer, S., Pagonis, V., Schmidt, C., 2022. RLumCarlo: Monte-Carlo Methods for Simulating Luminescence Phenomena. R package version 0.1.9. https://CRAN.R-project.org/package=RLumCarlo

Author(s)

Sebastian Kreutzer, Institute of Geography, Heidelberg University (Germany)
Johannes Friedrich, University of Bayreuth (Germany)

Examples

## plain plot
DELOC <- run_MC_TL_DELOC(
  s = 3.5e12,
  E = 1.45,
  R = 0.1,
  method = 'seq',
  clusters = 100,
  times = 150:350) %T>%
plot_RLumCarlo(legend = TRUE)

## TL with FUN to correct for thermal
## quenching
f <- function(x) x * 1/(1 + (2e+6 * exp(-0.55/(8.617e-5 * (DELOC$time + 273)))))
plot_RLumCarlo(
 object = DELOC,
 FUN = f)