Luminescence (version 0.4.1)

analyse_pIRIRSequence: Analyse post-IR IRSL sequences

Description

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, 
    sequence.structure = c("TL", "IR50", "pIRIR225"), plot = TRUE, 
    plot.single = FALSE, ...)

Arguments

object

RLum.Analysis(required): input object containing data for analysis

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",<

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

Value

• Plots (optional) and an RLum.Results object is returned containing the following elements:
• De.values
  data.frame containing De-values, De-error and further parameters
• .
• LnLxTnTx.values
  data.frame of all calculated Lx/Tx values including signal, background counts and the dose points.
• rejection.criteria
  data.frame with values that might by used as rejection criteria. NA is produced if no R0 dose point exists.
• The output should be accessed using the function get_RLum.Results.

Function version

0.1.3 (2015-01-20 09:12:19)

Details

To allow post-IR IRSL protocol (Thomsen et al., 2008) measurement analysis 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.

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

See Also

analyse_SAR.CWOSL, calc_OSLLxTxRatio, plot_GrowthCurve, RLum.Analysis, RLum.Results get_RLum.Results

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.Analysis(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.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)
    
                            
##(3) Perform pIRIR analysis (for this example with quartz OSL data!)
## Alternative for PDF output, uncomment and complete for usage
##
# pdf(file = "...", 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()