Last data update: 2014.03.03

 R: Analyse post-IR IRSL sequences
analyse_pIRIRSequenceR Documentation

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

Arguments

object

RLum.Analysis (required) or list of RLum.Analysis objects: input object containing data for analysis. If a list is provided the functions tries to iteratre 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 non 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

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.

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.

Value

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

DATA.OBJECT TYPE DESCRIPTION
..$De.values : data.frame Table with De values
..$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.

Function version

0.2.0 (2016-01-18 15:07:46)

Note

Best graphical output can be achieved by using the function pdf with the following options:
pdf(file = "...", height = 15, width = 15)

Author(s)

Sebastian Kreutzer, IRAMAT-CRP2A, Universite Bordeaux Montaigne (France)
R Luminescence Package Team

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

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)


##(3) Perform pIRIR analysis (for this example with quartz OSL data!)
## Alternative for PDF output, uncomment and complete for usage
## Not run: 
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()

## End(Not run)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(Luminescence)
Welcome to the R package Luminescence version 0.6.0 [Built: 2016-05-30 16:47:30 UTC]
An enthusiastic cabaret artist: 'Political elections are like brushing teeth: if you don't do it, things become brown.'
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/Luminescence/analyse_pIRIRSequence.Rd_%03d_medium.png", width=480, height=480)
> ### Name: analyse_pIRIRSequence
> ### Title: Analyse post-IR IRSL sequences
> ### Aliases: analyse_pIRIRSequence
> ### Keywords: datagen plot
> 
> ### ** 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 | De = 1668.25 | D01 = 1982.76
[plot_GrowthCurve()] Fit: EXP | 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
> ## Not run: 
> ##D pdf(file = "...", height = 15, width = 15)
> ##D   results <- analyse_pIRIRSequence(object,
> ##D          signal.integral.min = 1,
> ##D          signal.integral.max = 2,
> ##D          background.integral.min = 900,
> ##D          background.integral.max = 1000,
> ##D          fit.method = "EXP",
> ##D          main = "Pseudo pIRIR data set based on quartz OSL")
> ##D 
> ##D   dev.off()
> ## End(Not run)
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>


Created & Maintained by Osamu Ogasawara (osamu.ogasawara@gmail.com) and IMSBIO