OSLdecomposition (version 1.0.0)

plot_PhotoCrosssections: Plot comparison of CW-OSL component photoionisation cross sections of different models

Description

This function takes the output.complex = TRUE output of fit_OSLcurve and draws the photoionisation cross sections of different models in relation to each other. If a stimulation wavelength between 465 and 480 nm was chosen, the photoionisation cross sections are also set in relation to literature values from Singarayer and Bailey (2003), Jain et al. (2003) and Durcan and Duller (2011).

Usage

plot_PhotoCrosssections(
  fit.list,
  stimulation.intensity = NULL,
  stimulation.wavelength = NULL,
  K.selected = NULL,
  title = NULL,
  hide.plot = FALSE,
  filename = NULL
)

Value

An invisible ggplot2::ggplot object containing the diagram will returned. "Invisible" means, the no value will be returned (e.g. no console printout) if the function is not assigned to a variable via <-. If the function is assigned, the returned object can be further manipulated by ggplot2-package methods or manually drawn by various functions like for example gridExtra::grid.arrange.

Arguments

fit.list

list (required): Output object of fit_OSLcurve. The object must be created with the setting output.complex = TRUE.

stimulation.intensity

numeric (optional): Intensity of optical stimulation in mW / cm². Used to calculate the photoionisation cross sections. If not given, the input value for fit_OSLcurve is used

stimulation.wavelength

numeric (optional): Wavelength of optical stimulation in nm. Used to calculate the photoionisation cross sections. If not given, the input value for fit_OSLcurve is used

K.selected

numeric (optional): Draws a red rectangle around the K = K.selected row, thus highlighting the model of choice.

title

character (with default): Plot title. Set title = NULL for no title.

hide.plot

logical (with default): If true, plot is not drawn but can still be saved as file or caught by A <- plot_PhotoCrosssections(...). If caught, the plot can be drawn manually for example by using gridExtra::grid.arrange.

filename

character (optional): File name or path to save the plot as image. If just a file name is given, the image is saved in the working directory. The image type is chosen by the file ending. Both, vector images as well as pixel images are possible. Allowed are .pdf, .eps, .svg (vector graphics), .jpg, .png, .bmp (pixel graphics) and more, see ggplot2::ggsave.

Last updates

2020-11-04, DM: Added roxygen documentation

Author

Dirk Mittelstraß,

Please cite the package the following way:

Mittelstraß, D., Schmidt, C., Beyer, J., Heitmann, J. and Straessner, A.: R package OSLdecomposition: Automated identification and separation of quartz CW-OSL signal components, in preparation.

Details

The photoionisation cross section ranges of the reference components are defined as following:

Component	Lower limit (cm^2)	Upper limit (cm^2)
Ultrafast	1e-16	1e-15
Fast	1.9e-17	3.1e-17
Medium	3e-18	9e-18
Slow1	1e-18	1.85e-18
Slow2	1.1e-19	4e-19
Slow3	1e-20	4.67e-20
Slow4	1e-21	1e-20

References

Durcan, J.A., Duller, G.A.T., 2011. The fast ratio: A rapid measure for testing the dominance of the fast component in the initial OSL signal from quartz. Radiation Measurements 46, 1065–1072.

Jain, M., Murray, A.S., Bøtter-Jensen, L., 2003. Characterisation of blue-light stimulated luminescence components in different quartz samples: implications for dose measurement. Radiation Measurements 37, 441–449.

Singarayer, J.S., Bailey, R.M., 2003. Further investigations of the quartz optically stimulated luminescence components using linear modulation. Radiation Measurements, Proceedings of the 10th international Conference on Luminescence and Electron-Spin Resonance Dating (LED 2002) 37, 451–458.

See Also

fit_OSLcurve, RLum.OSL_global_fitting

Examples

# Set some arbitrary decay parameter for a dim CW-OSL measurement of quartz
name <- c("fast", "slow")
lambda <- c(2, 0.02)
n <- c(1e6, 5e7)

# Build a component table
components <- data.frame(name, lambda, n)

# Simulate the CW-OSL curve and add some signal noise
curve <- simulate_OSLcomponents(components, simulate.curve = TRUE, add.poisson.noise = TRUE)

# Perform nonlinear regression at the simulated curve
fit_results <- fit_OSLcurve(curve, K.max = 2, output.complex = TRUE)

# Plot the fitting iterations and set them into context
plot_PhotoCrosssections(fit_results)