analyse_baSAR function

Bayesian models (baSAR) applied on luminescence data

This function allows the application of Bayesian models on luminescence data, measured with the single-aliquot regenerative-dose (SAR, Murray and Wintle, 2000) protocol. In particular, it follows the idea proposed by Combès et al., 2015 of using an hierarchical model for estimating a central equivalent dose from a set of luminescence measurements. This function is (I) the adoption of this approach for the R environment and (II) an extension and a technical refinement of the published code.

analyse_baSAR(
  object,
  CSV_file = NULL,
  aliquot_range = NULL,
  source_doserate = NULL,
  signal.integral,
  signal.integral.Tx = NULL,
  background.integral,
  background.integral.Tx = NULL,
  irradiation_times = NULL,
  sigmab = 0,
  sig0 = 0.025,
  distribution = "cauchy",
  baSAR_model = NULL,
  n.MCMC = 1e+05,
  fit.method = "EXP",
  fit.force_through_origin = TRUE,
  fit.includingRepeatedRegPoints = TRUE,
  method_control = list(),
  digits = 3L,
  distribution_plot = "kde",
  plot = TRUE,
  plot_reduced = TRUE,
  plot_singlePanels = FALSE,
  verbose = TRUE,
  ...
)

Arguments

• object: Risoe.BINfileData , RLum.Results , list of RLum.Analysis , character or list (required ): input object used for the Bayesian analysis. If a character is provided the function assumes a file connection and tries to import a BIN/BINX-file using the provided path. If a list is provided the list can only contain either Risoe.BINfileData objects or characters providing a file connection. Mixing of both types is not allowed. If an RLum.Results

  is provided the function directly starts with the Bayesian Analysis (see details)

• CSV_file: character or data.frame (optional): if a character, it must be the path to a CSV file with data for the analysis. Either way, data should contain 3 columns: the name of the file, the disc position and the grain position (the last being 0 for multi-grain measurements).

• aliquot_range: numeric (optional): allows to limit the range of the aliquots used for the analysis. This argument has only an effect if the argument CSV_file is used or the input is the previous output (i.e. is RLum.Results ). In this case the new selection will add the aliquots to the removed aliquots table.

• source_doserate: numeric (required ): source dose rate of beta-source used for the measurement and its uncertainty in Gy/s, e.g., source_doserate = c(0.12, 0.04). Parameter can be provided as list, for the case that more than one BIN-file is provided, e.g., source_doserate = list(c(0.04, 0.004), c(0.05, 0.004)).

• signal.integral: vector (required ): vector with the limits for the signal integral used for the calculation, e.g., signal.integral = c(1:5). Ignored if object is an RLum.Results object. The parameter can be provided as list, see source_doserate.

• signal.integral.Tx: vector (optional): vector with the limits for the signal integral for the Tx curve. I f nothing is provided the value from signal.integral is used and it is ignored if object is an RLum.Results object. The parameter can be provided as list, see source_doserate.

• background.integral: vector (required ): vector with the bounds for the background integral. Ignored if object is an RLum.Results object. The parameter can be provided as list, see source_doserate.

• background.integral.Tx: vector (optional): vector with the limits for the background integral for the Tx curve. If nothing is provided the value from background.integral is used. Ignored if object is an RLum.Results object. The parameter can be provided as list, see source_doserate.

• irradiation_times: numeric (optional): if set this vector replaces all irradiation times for one aliquot and one cycle (Lx and Tx curves) and recycles it for all others cycles and aliquots. Please note that if this argument is used, for every(!) single curve in the dataset an irradiation time needs to be set.

• sigmab: numeric (with default): option to set a manual value for the overdispersion (for LnTx and TnTx), used for the Lx/Tx error calculation. The value should be provided as absolute squared count values, cf. calc_OSLLxTxRatio . The parameter can be provided as list, see source_doserate.

• sig0: numeric (with default): allow adding an extra component of error to the final Lx/Tx error value (e.g., instrumental error, see details is calc_OSLLxTxRatio ). The parameter can be provided as list, see source_doserate.

• distribution: character (with default): type of distribution that is used during Bayesian calculations for determining the Central dose and overdispersion values. Allowed inputs are "cauchy", "normal" and "log_normal".

• baSAR_model: character (optional): option to provide an own modified or new model for the Bayesian calculation (see details). If an own model is provided the argument distribution is ignored and set to 'user_defined'

• n.MCMC: integer (with default): number of iterations for the Markov chain Monte Carlo (MCMC) simulations

• fit.method: character (with default): equation used for the fitting of the dose-response curve using the function plot_GrowthCurve and then for the Bayesian modelling. Here supported methods: EXP, EXP+LIN and LIN

• fit.force_through_origin: logical (with default): force fitting through origin

• fit.includingRepeatedRegPoints: logical (with default): includes the recycling point (assumed to be measured during the last cycle)

• method_control: list (optional): named list of control parameters that can be directly passed to the Bayesian analysis, e.g., method_control = list(n.chains = 4). See details for further information

• digits: integer (with default): round output to the number of given digits

• distribution_plot: character (with default): sets the final distribution plot that shows equivalent doses obtained using the frequentist approach and sets in the central dose as comparison obtained using baSAR. Allowed input is 'abanico' or 'kde'. If set to NULL nothing is plotted.

• plot: logical (with default): enable/disable the plot output.

• plot_reduced: logical (with default): enable/disable the advanced plot output.

• plot_singlePanels: logical (with default): enable/disable single plots or plots arranged by analyse_baSAR.

• verbose: logical (with default): enable/disable output to the terminal.

• ...: parameters that can be passed to the function calc_OSLLxTxRatio

  (almost full support), data.table::fread (skip), read_BIN2R (n.records, position, duplicated.rm), see details.

Returns

Function returns results numerically and graphically:

---

[ NUMERICAL OUTPUT ]

---

‘RLum.Results’ -object

slot: @data

Element	Type	Description
$summary	data.frame	statistical summary, including the central dose
$mcmc	mcmc	coda::mcmc.list object including raw output
$models	character	implemented models used in the baSAR-model core
$input_object	data.frame	summarising table (same format as the XLS-file) including, e.g., Lx/Tx values
$removed_aliquots	data.frame	table with removed aliquots (e.g., NaN , or Inf Lx / Tx values). If nothing was removed NULL is returned

slot: @info

The original function call

---

[ PLOT OUTPUT ]

---

• (A) Ln/Tn curves with set integration limits,
• (B) trace plots are returned by the baSAR-model, showing the convergence of the parameters (trace) and the resulting kernel density plots. If plot_reduced = FALSE for every(!) dose a trace and a density plot is returned (this may take a long time),
• (C) dose plots showing the dose for every aliquot as boxplots and the marked HPD in within. If boxes are coloured 'orange' or 'red' the aliquot itself should be checked,
• (D) the dose response curve resulting from the monitoring of the Bayesian modelling are provided along with the Lx/Tx values and the HPD. Note: The amount for curves displayed is limited to 1000 (random choice) for performance reasons,
• (E) the final plot is the De distribution as calculated using the conventional (frequentist) approach and the central dose with the HPDs marked within. This figure is only provided for a comparison, no further statistical conclusion should be drawn from it.

Please note: If distribution was set to ‘log_normal’ the central doseis given as geometric mean!

Details

Internally the function consists of two parts: (I) The Bayesian core for the Bayesian calculations and applying the hierarchical model and (II) a data pre-processing part. The Bayesian core can be run independently, if the input data are sufficient (see below). The data pre-processing part was implemented to simplify the analysis for the user as all needed data pre-processing is done by the function, i.e. in theory it is enough to provide a BIN/BINX-file with the SAR measurement data. For the Bayesian analysis for each aliquot the following information are needed from the SAR analysis. LxTx, the LxTx error and the dose values for all regeneration points.

How is the systematic error contribution calculated?

Standard errors (so far) provided with the source dose rate are considered as systematic uncertainties and added to final central dose by:

$$systematic.error = 1/n \sum SE(source.doserate)$$ $$SE(central.dose.final) = \sqrt{SE(central.dose)^2 + systematic.error^2}$$

Please note that this approach is rather rough and can only be valid if the source dose rate errors, in case different readers had been used, are similar. In cases where more than one source dose rate is provided a warning is given.

Input / output scenarios

Various inputs are allowed for this function. Unfortunately this makes the function handling rather complex, but at the same time very powerful. Available scenarios:

(1) - ‘object’ is BIN-file or link to a BIN-file

Finally it does not matter how the information of the BIN/BINX file are provided. The function supports (a) either a path to a file or directory or a list of file names or paths or (b) a Risoe.BINfileData object or a list of these objects. The latter one can be produced by using the function read_BIN2R , but this function is called automatically if only a file name and/or a path is provided. In both cases it will become the data that can be used for the analysis.

[CSV_file = NULL]

If no CSV file (or data frame with the same format) is provided, the function runs an automatic process that consists of the following steps:

1. Select all valid aliquots using the function verify_SingleGrainData
2. Calculate Lx/Tx values using the function calc_OSLLxTxRatio
3. Calculate De values using the function plot_GrowthCurve

These proceeded data are subsequently used in for the Bayesian analysis

[CSV_file != NULL]

If a CSV file is provided (or a data.frame containing similar information) the pre-processing phase consists of the following steps:

1. Calculate Lx/Tx values using the function calc_OSLLxTxRatio
2. Calculate De values using the function plot_GrowthCurve

The CSV file should contain the BIN-file names and the aliquots selected for the further analysis. This allows a manual selection of input data, as the automatic selection by verify_SingleGrainData might be not totally sufficient.

(2) - ‘object’ RLum.Results object

If an RLum.Results object is provided as input and(!) this object was previously created by the function analyse_baSAR() itself, the pre-processing part is skipped and the function starts directly with the Bayesian analysis. This option is very powerful as it allows to change parameters for the Bayesian analysis without the need to repeat the data pre-processing. If furthermore the argument aliquot_range is set, aliquots can be manually excluded based on previous runs.

‘method_control’

These are arguments that can be passed directly to the Bayesian calculation core, supported arguments are:

Parameter	Type	Description
lower_centralD	numeric	sets the lower bound for the expected De range. Change it only if you know what you are doing!
upper_centralD	numeric	sets the upper bound for the expected De range. Change it only if you know what you are doing!
n.chains	integer	sets number of parallel chains for the model (default = 3) (cf. rjags::jags.model )
inits	list	option to set initialisation values (cf. rjags::jags.model )
thin	numeric	thinning interval for monitoring the Bayesian process (cf. rjags::jags.model )
variable.names	character	set the variables to be monitored during the MCMC run, default: 'central_D' , 'sigma_D' , 'D' , 'Q' , 'a' , 'b' , 'c' , 'g' . Note: only variables present in the model can be monitored.

User defined models

The function provides the option to modify and to define own models that can be used for the Bayesian calculation. In the case the user wants to modify a model, a new model can be piped into the function via the argument baSAR_model as character. The model has to be provided in the JAGS dialect of the BUGS language (cf. rjags::jags.model ) and parameter names given with the pre-defined names have to be respected, otherwise the function will break.

FAQ

Q: How can I set the seed for the random number generator (RNG)?

A: Use the argument method_control, e.g., for three MCMC chains (as it is the default):

method_control = list(
inits = list(
 list(.RNG.name = "base::Wichmann-Hill", .RNG.seed = 1),
 list(.RNG.name = "base::Wichmann-Hill", .RNG.seed = 2),
 list(.RNG.name = "base::Wichmann-Hill", .RNG.seed = 3)
))

This sets a reproducible set for every chain separately.

Q: How can I modify the output plots?

A: You can't, but you can use the function output to create own, modified plots.

Q: Can I change the boundaries for the central_D?

A: Yes, we made it possible, but we DO NOT recommend it, except you know what you are doing!

Example: method_control = list(lower_centralD = 10))

Q: The lines in the baSAR-model appear to be in a wrong logical order?

A: This is correct and allowed (cf. JAGS manual)

Additional arguments support via the ‘...’ argument

This list summarizes the additional arguments that can be passed to the internally used functions.

Supported argument	Corresponding function	Default	**Short description **
threshold	verify_SingleGrainData	30	change rejection threshold for curve selection
skip	data.table::fread	0	number of rows to be skipped during import
n.records	read_BIN2R	NULL	limit records during BIN-file import
duplicated.rm	read_BIN2R	TRUE	remove duplicated records in the BIN-file
pattern	read_BIN2R	TRUE	select BIN-file by name pattern
position	read_BIN2R	NULL	limit import to a specific position
background.count.distribution	calc_OSLLxTxRatio	"non-poisson"	set assumed count distribution
fit.weights	plot_GrowthCurve	TRUE	enable/disable fit weights
fit.bounds	plot_GrowthCurve	TRUE	enable/disable fit bounds
n.MC	plot_GrowthCurve	100	number of MC runs for error calculation
output.plot	plot_GrowthCurve	TRUE	enable/disable dose response curve plot
output.plotExtended	plot_GrowthCurve	TRUE	enable/disable extended dose response curve plot
recordType	get_RLum	c(OSL (UVVIS), irradiation (NA)	helps for the curve selection

Note

If you provide more than one BIN-file , it is strongly recommended to provide a list with the same number of elements for the following parameters:

source_doserate, signal.integral, signal.integral.Tx, background.integral, background.integral.Tx, sigmab, sig0.

Example for two BIN-files: source_doserate = list(c(0.04, 0.006), c(0.05, 0.006))

The function is currently limited to work with standard Risoe BIN-filesonly!

Function version

0.1.36

Examples

##(1) load package test data set
data(ExampleData.BINfileData, envir = environment())

##(2) selecting relevant curves, and limit dataset
CWOSL.SAR.Data <- subset(
  CWOSL.SAR.Data,
  subset = POSITION%in%c(1:3) & LTYPE == "OSL")

## Not run:

##(3) run analysis
##please not that the here selected parameters are
##choosen for performance, not for reliability
results <- analyse_baSAR(
  object = CWOSL.SAR.Data,
  source_doserate = c(0.04, 0.001),
  signal.integral = c(1:2),
  background.integral = c(80:100),
  fit.method = "LIN",
  plot = FALSE,
  n.MCMC = 200

)

print(results)

##CSV_file template
##copy and paste this the code below in the terminal
##you can further use the function write.csv() to export the example

CSV_file <-
structure(
list(
 BIN_FILE = NA_character_,
 DISC = NA_real_,
 GRAIN = NA_real_),
   .Names = c("BIN_FILE", "DISC", "GRAIN"),
   class = "data.frame",
   row.names = 1L
)
## End(Not run)

How to cite

Mercier, N., Kreutzer, S., 2025. analyse_baSAR(): Bayesian models (baSAR) applied on luminescence data. Function version 0.1.36. 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., Steinbuch, L., Boer, A.d., 2025. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 1.0.1. https://r-lum.github.io/Luminescence/

References

Combès, B., Philippe, A., Lanos, P., Mercier, N., Tribolo, C., Guerin, G., Guibert, P., Lahaye, C., 2015. A Bayesian central equivalent dose model for optically stimulated luminescence dating. Quaternary Geochronology 28, 62-70. doi:10.1016/j.quageo.2015.04.001

Mercier, N., Kreutzer, S., Christophe, C., Guerin, G., Guibert, P., Lahaye, C., Lanos, P., Philippe, A., Tribolo, C., 2016. Bayesian statistics in luminescence dating: The 'baSAR'-model and its implementation in the R package 'Luminescence'. Ancient TL 34, 14-21.

Further reading

Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., Rubin, D.B., 2013. Bayesian Data Analysis, Third Edition. CRC Press.

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

Plummer, M., 2017. JAGS Version 4.3.0 user manual. https://sourceforge.net/projects/mcmc-jags/files/Manuals/4.x/jags_user_manual.pdf/download

See Also

read_BIN2R , calc_OSLLxTxRatio , plot_GrowthCurve , data.table::fread , verify_SingleGrainData , rjags::jags.model , rjags::coda.samples , boxplot.default

Author(s)

Norbert Mercier, Archaésciences Bordeaux, CNRS-Université Bordeaux Montaigne (France)

Sebastian Kreutzer, Institute of Geography, Heidelberg University (Germany)

The underlying Bayesian model based on a contribution by Combès et al., 2015. , RLum Developer Team