Analyse post-IR IRSL measurement sequences

The function performs an analysis of post-IR IRSL sequences including curve fitting on RLum.Analysis objects.

Usage

analyse_pIRIRSequence(
  object,
  signal.integral.min,
  signal.integral.max,
  background.integral.min,
  background.integral.max,
  dose.points = NULL,
  sequence.structure = c("TL", "IR50", "pIRIR225"),
  plot = TRUE,
  plot.single = FALSE,
  ...
)

Arguments

object: RLum.Analysis or list of RLum.Analysis objects (required): input object containing data for analysis. If a list is provided the functions tries to iterate over the list.
signal.integral.min: integer (required): lower bound of the signal integral. Provide this value as vector for different integration limits for the different IRSL curves.
signal.integral.max: integer (required): upper bound of the signal integral. Provide this value as vector for different integration limits for the different IRSL curves.
background.integral.min: integer (required): lower bound of the background integral. Provide this value as vector for different integration limits for the different IRSL curves.
background.integral.max: integer (required): upper bound of the background integral. Provide this value as vector for different integration limits for the different IRSL curves.
dose.points: numeric (optional): a numeric vector containing the dose points values. Using this argument overwrites dose point values in the signal curves.
sequence.structure: vector character (with default): specifies the general sequence structure. Allowed values are "TL" and any "IR" combination (e.g., "IR50","pIRIR225"). Additionally a parameter "EXCLUDE" is allowed to exclude curves from the analysis (Note: If a preheat without PMT measurement is used, i.e. preheat as none TL, remove the TL step.)
plot: logical (with default): enables or disables plot output.
plot.single: logical (with default): single plot output (TRUE/FALSE) to allow for plotting the results in single plot windows. Requires plot = TRUE.
...: further arguments that will be passed to the function analyse_SAR.CWOSL and plot_GrowthCurve. Furthermore, the arguments main (headers), log (IRSL curves), cex (control the size) and mtext.outer (additional text on the plot area) can be passed to influence the plotting. If the input is list, main can be passed as vector or list.

Value

Plots (optional) and an RLum.Results object is returned containing the following elements:

DATA.OBJECT	TYPE	DESCRIPTION
`..$data` :	`data.frame`	Table with De values
`..$LnLxTnTx.table` :	`data.frame`	with the `LnLxTnTx` values
`..$rejection.criteria` :	data.frame	rejection criteria
`..$Formula` :	list	Function used for fitting of the dose response curve
`..$call` :	call	the original function call

The output should be accessed using the function get_RLum.

Details

To allow post-IR IRSL protocol (Thomsen et al., 2008) measurement analyses this function has been written as extended wrapper function for the function analyse_SAR.CWOSL, facilitating an entire sequence analysis in one run. With this, its functionality is strictly limited by the functionality of the function analyse_SAR.CWOSL.

Defining the sequence structure

The argument sequence.structure expects a shortened pattern of your sequence structure and was mainly introduced to ease the use of the function. For example: If your measurement data contains the following curves: TL, IRSL, IRSL, TL, IRSL, IRSL, the sequence pattern in sequence.structure becomes c('TL', 'IRSL', 'IRSL'). The second part of your sequence for one cycle should be similar and can be discarded. If this is not the case (e.g., additional hotbleach) such curves have to be removed before using the function.

If the input is a list

If the input is a list of RLum.Analysis-objects, every argument can be provided as list to allow for different sets of parameters for every single input element. For further information see analyse_SAR.CWOSL.

Note

Best graphical output can be achieved by using the function pdf with the following options:

pdf(file = "<YOUR FILENAME>", height = 15, width = 15)

Function version

0.2.4

How to cite

Kreutzer, S., 2024. analyse_pIRIRSequence(): Analyse post-IR IRSL measurement sequences. Function version 0.2.4. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Philippe, A., Riedesel, S., Autzen, M., Mittelstrass, D., Gray, H.J., Galharret, J., Colombo, M., 2024. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.9.26. https://r-lum.github.io/Luminescence/

References

Murray, A.S., Wintle, A.G., 2000. Luminescence dating of quartz using an improved single-aliquot regenerative-dose protocol. Radiation Measurements 32, 57-73. doi:10.1016/S1350-4487(99)00253-X

Thomsen, K.J., Murray, A.S., Jain, M., Boetter-Jensen, L., 2008. Laboratory fading rates of various luminescence signals from feldspar-rich sediment extracts. Radiation Measurements 43, 1474-1486. doi:10.1016/j.radmeas.2008.06.002

Author

Sebastian Kreutzer, Institute of Geography, Heidelberg University (Germany) , RLum Developer Team

Examples



### NOTE: For this example existing example data are used. These data are non pIRIR data.
###
##(1) Compile example data set based on existing example data (SAR quartz measurement)
##(a) Load example data
data(ExampleData.BINfileData, envir = environment())

##(b) Transform the values from the first position in a RLum.Analysis object
object <- Risoe.BINfileData2RLum.Analysis(CWOSL.SAR.Data, pos=1)

##(c) Grep curves and exclude the last two (one TL and one IRSL)
object <- get_RLum(object, record.id = c(-29,-30))

##(d) Define new sequence structure and set new RLum.Analysis object
sequence.structure  <- c(1,2,2,3,4,4)
sequence.structure <- as.vector(sapply(seq(0,length(object)-1,by = 4),
                                       function(x){sequence.structure + x}))

object <-  sapply(1:length(sequence.structure), function(x){

  object[[sequence.structure[x]]]

})

object <- set_RLum(class = "RLum.Analysis", records = object, protocol = "pIRIR")

##(2) Perform pIRIR analysis (for this example with quartz OSL data!)
## Note: output as single plots to avoid problems with this example
results <- analyse_pIRIRSequence(object,
     signal.integral.min = 1,
     signal.integral.max = 2,
     background.integral.min = 900,
     background.integral.max = 1000,
     fit.method = "EXP",
     sequence.structure = c("TL", "pseudoIRSL1", "pseudoIRSL2"),
     main = "Pseudo pIRIR data set based on quartz OSL",
     plot.single = TRUE)





#> [plot_GrowthCurve()] Fit: EXP (interpolation) | De = 1668.25 | D01 = 1982.76






#> [plot_GrowthCurve()] Fit: EXP (interpolation) | De = 1668.25 | D01 = 1982.76








##(3) Perform pIRIR analysis (for this example with quartz OSL data!)
## Alternative for PDF output, uncomment and complete for usage
if (FALSE) { # \dontrun{
tempfile <- tempfile(fileext = ".pdf")
pdf(file = tempfile, height = 15, width = 15)
  results <- analyse_pIRIRSequence(object,
         signal.integral.min = 1,
         signal.integral.max = 2,
         background.integral.min = 900,
         background.integral.max = 1000,
         fit.method = "EXP",
         main = "Pseudo pIRIR data set based on quartz OSL")

  dev.off()
} # }