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Bayesian analysis for the palaeodose estimation of various samples

Source: R/Palaeodose_Computation.R
Palaeodose_Computation.Rd

This function computes the palaeodose (in Gy) of one or various samples according to the model developed in Combes et al (2015), based on an output of Generate_DataFile or Generate_DataFile_MG or both of them using combine_DataFiles.
Samples, for which data is avalilable in several BIN files, can be analysed.
Single-grain or Multi-grain OSL measurements can be analysed simultaneouly.

Usage

Palaeodose_Computation(
  DATA,
  SampleNames,
  Nb_sample,
  BinPerSample = rep(1, Nb_sample),
  SavePdf = FALSE,
  OutputFileName = c("MCMCplot"),
  OutputFilePath = c(""),
  SaveEstimates = FALSE,
  OutputTableName = c("DATA"),
  OutputTablePath = c(""),
  LIN_fit = TRUE,
  Origin_fit = FALSE,
  distribution = c("cauchy"),
  Iter = 50000,
  t = 5,
  n.chains = 3
)

Arguments

DATA

list of objects: LT, sLT, ITimes, dLab, ddot_env, regDose, J, K, Nb_measurement, provided by Generate_DataFile or Generate_DataFile_MG. DATA contains information for more than one sample.

SampleNames

character vector: names of sample. The length of this vector is equal to Nb_sample.

Nb_sample

integer: number of samples.

BinPerSample

integer vector (with default): vector with the number of BIN files per sample. The length of this vector is equal to Nb_sample. BinPerSample[i] corresponds to the number of BIN files for the sample whose number ID is equal to i. For more information to fill this vector, we refer to details in Generate_DataFile or Generate_DataFile_MG.

SavePdf

boolean (with default): if TRUE save graph in pdf file named OutputFileName in folder OutputFilePath.

OutputFileName

character (with default): name of the pdf files that will be generated by the function.

OutputFilePath

character (with default): path to the pdf files that will be generated by the function.

SaveEstimates

boolean (with default): if TRUE save Bayes estimates and credible interval at level 68 in a csv table named OutputFileName in folder OutputFilePath.

OutputTableName

character (with default): name of the table that will be generated by the function if SaveEstimates=TRUE.

OutputTablePath

character (with default): path to the table that will be generated by the function if SaveEstimates=TRUE.

LIN_fit

logical (with default): if TRUE (default) allows a linear component, on top of the (default) saturating exponential curve, for the fitting of dose response curves. Please see details for more informations on the proposed dose response curves.

Origin_fit

logical (with default): if TRUE, forces the dose response curves to pass through the origin. Please see details for more informations on the proposed growth curves.

distribution

character (with default): type of distribution that defines how individual equivalent dose values are distributed around the palaeodose. Allowed inputs are "cauchy", "gaussian", "lognormal_A" and "lognormal_M".

Iter

integer (with default): number of iterations for the MCMC computation (for more information see [rjags::jags.model]).

t

integer (with default): 1 every t iterations of the MCMC is considered for sampling the posterior distribution (for more information see [rjags::jags.model]).

n.chains

integer (with default): number of independent chains for the model (for more information see [rjags::jags.model]).

Value

NUMERICAL OUTPUT

  1. A list containing the following objects:

    • Sampling that corresponds to a sample of the posterior distributions of palaeodose and equivalent dose dispersion parameters (both in Gy).

    • Model_GrowthCurve, stating which dose response fitting option was chosen;

    • Distribution, stating which distribution was chosen to model the dispersion of individual equivalent dose values around the palaeodose of the sample.

  2. The Gelman and Rubin test of convergency: prints the result of the Gelman and Rubin test of convergency for palaeodose and equivalent dose dispersion parameters for each sample. A result close to one is expected.
    In addition, the user must visually assess the convergency of the trajectories by looking at the pdf file generated by the function (see PLOT OUTPUT for more informations).
    If both convergencies (Gelman and Rubin test and plot checking) are satisfactory, the user can consider the printed estimates as valid. Otherwise, the user may try increasing the number of MCMC interations (Iter) to reach convergency.

  3. Credible intervals and Bayes estimates: prints the Bayes esitmates, the credible intervals at 95 the palaeodose and equivalent dose dispersion parameters for each sample.

PLOT OUTPUT

  1. MCMC trajectories A graph with the MCMC trajectories and posterior distributions of the palaeodose and equivalent dose dispersion parameters is displayed, there is one page per sample.
    The first line of the figure correponds to the palaeodose parameter and the second to the equivalent dose dispersion parameter. On each line, the plot on the left represents the MCMC trajectories, and the one on the right the posterior distribution of the parameter.

  2. Summary of palaeodose estimates: plot credible intervals and Bayes estimate of each sample palaeodose on a same graph.

To give result in a publication, we recommend to give the Bayes estimate of the parameters as well as the credible interval at 95

Details

** Option on growth curves **

As for Age_Computation and AgeS_Computation, the user can choose from 4 dose response curves:

  • Saturating exponential plus linear growth (PalaeodosesMultiBF_EXPLIN):

    for all x in IR+, f(x)=a(1-exp(-x/b))+cx+d; select

    • LIN_fit=TRUE

    • Origin_fit=FALSE

  • Saturating exponential growth (PalaeodosesMultiBF_EXP):

    for all x in IR+, f(x)=a(1-exp(-x/b))+d; select

    • LIN_fit=FALSE

    • Origin_fit=FALSE

  • Saturating exponential plus linear growth and fitting through the origin (PalaeodosesMultiBF_EXPLINZO):

    for all x in IR+, f(x)=a(1-exp(-x/b))+cx; select

    • LIN_fit=TRUE

    • Origin_fit=TRUE

  • Saturating exponential growth and fitting through the origin (PalaeodosesMultiBF_EXPZO):

    for all x in IR+, f(x)=a(1-exp(-x/b)); select

    • LIN_fit=FALSE

    • Origin_fit=TRUE

** Option on equivalent dose distribution around the palaeodose **

The use can choose between :

  • cauchy: a Cauchy distribution with location parameter equal to the palaeodose of the sample

  • gaussian: a Gaussian distribution with mean equal to the palaeodose of the sample

  • lognormal_A: a log-normal distribution with mean or Average equal to the palaeodose of the sample

  • lognormal_M: a log-normal distribution with Median equal to the palaeodose of the sample

Note

Please note that the initial values for all MCMC are currently all the same for all chains since we rely on the automatic initial value generation of JAGS. This is not optimal and will be changed in future. However, it does not affect the quality of the age estimates if the chains have converged.

How to cite

Christophe, C., Kreutzer, S., Philippe, A., Guérin, G., 2024. Palaeodose_Computation(): Bayesian analysis for the palaeodose estimation of various samples. In: Christophe, C., Philippe, A., Kreutzer, S., Guérin, G., Baumgarten, F.H., Frerebeau, N., 2024. BayLum: Chronological Bayesian Models Integrating Optically Stimulated. R package version 0.3.3.9000-13. https://CRAN.r-project.org/package=BayLum

References

Combes, 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

See also

Generate_DataFile, Generate_DataFile_MG, combine_DataFiles, [rjags::rjags-package], [plot_MCMC], Age_Computation, AgeS_Computation

Author

Claire Christophe, Sebastian Kreutzer, Anne Philippe, Guillaume Guérin

Examples

## Load data
data(DATA1,envir = environment())
## Palaeodose computation of samples GDB3
P=Palaeodose_Computation(DATA=DATA1,Nb_sample=1,SampleNames=c("GDB5"),Iter=100)
#> Compiling model graph
#>    Resolving undeclared variables
#>    Allocating nodes
#> Graph information:
#>    Observed stochastic nodes: 505
#>    Unobserved stochastic nodes: 608
#>    Total graph size: 7216
#> 
#> Initializing model
#> 
#> Warning: [plot_MCMC()] 'n.iter' out of range, reset to number of observations

#> 
#> 
#> >> Results of the Gelman and Rubin criterion of convergence <<
#> ----------------------------------------------
#>  Sample name:  GDB5 
#> ---------------------
#> 		 Point estimate Uppers confidence interval
#> D_GDB5 	 1.42 		 2.21 
#> sD_GDB5 	 1.3 		 1.89 
#> 
#> 
#> ---------------------------------------------------------------------------------------------------
#>  *** WARNING: The following information are only valid if the MCMC chains have converged  ***
#> ---------------------------------------------------------------------------------------------------
#> 
#> 
#> 
#> >> Bayes estimates of Age, Palaeodose and its dispersion for each sample and credible interval <<
#> ----------------------------------------------
#>  Sample name:  GDB5 
#> ---------------------
#> Parameter 	 Bayes estimate 	  Credible interval 
#> D_GDB5 	 106.198 
#> 						 lower bound 	 upper bound
#> 				 at level 95% 	 98 		 114 
#> 				 at level 68% 	 101 		 108 
#> 
#> Parameter 	 Bayes estimate 	  Credible interval 
#> sD_GDB5 	 15.621 
#> 						 lower bound 	 upper bound
#> 				 at level 95% 	 9 		 21 
#> 				 at level 68% 	 12 		 18 
#> 
#> ----------------------------------------------