First steps: age analysis of one sample

Import information from a BIN/BINX-file.

Let us consider the sample is named samp1, which is the example dataset coming with the package. All information related to this sample is stored in a subfolder called also samp1. To test the package example, first, we add the path of the example dataset to the object path.

path <- paste0(system.file("extdata/", package = "BayLum"), "/")

Please note that for your own dataset (i.e. not included in the package) you have to replace this call by something like:

path <- "Users/Master_of_luminescence/Documents/MyFamousOSLData"

In our example the folder contains the following subfolders and files:

1	FER1/bin.BIN
2	FER1/Disc.csv
3	FER1/DoseEnv.csv
4	FER1/DoseSource.csv
5	FER1/rule.csv
6	samp1/bin.BIN
7	samp1/DiscPos.csv
8	samp1/DoseEnv.csv
9	samp1/DoseSource.csv
10	samp1/rule.csv
11	samp2/bin.BIN
12	samp2/DiscPos.csv
13	samp2/DoseEnv.csv
14	samp2/DoseSource.csv
15	samp2/rule.csv

See “What are the required files in each subfolder?” in the manual of Generate_DataFile() function for the meaning of these files.

To import your data, simply call the function Generate_DataFile():

DATA1 <-
  Generate_DataFile(
  Path = path,
  FolderNames = "samp1",
  Nb_sample = 1,
  verbose = FALSE
  )

Remarks

Data import/export

The import may take a while, in particular for large BIN/BINX-files. This can become annoying if you want to play with the data. In such situations, it makes sense to save your imported data somewhere else before continuing.

To save the obove imported data on your hardrive use

save(DATA1, file = "YourPath/DATA1.RData")

To load the data use

load(DATA1, file = "YourPath/DATA1.RData")

Data structure

To see the overall structure of the data generated from the BIN/BINX-file and the associated CSV-files, the following call can be used:

str(DATA1)

List of 9
 $ LT            :List of 1
  ..$ : num [1, 1:7] 2.042 0.842 1.678 3.826 4.258 ...
 $ sLT           :List of 1
  ..$ : num [1, 1:7] 0.344 0.162 0.328 0.803 0.941 ...
 $ ITimes        :List of 1
  ..$ : num [1, 1:6] 15 30 60 100 0 15
 $ dLab          : num [1:2, 1] 1.53e-01 5.89e-05
 $ ddot_env      : num [1:2, 1] 2.512 0.0563
 $ regDose       :List of 1
  ..$ : num [1, 1:6] 2.3 4.6 9.21 15.35 0 ...
 $ J             : num 1
 $ K             : num 6
 $ Nb_measurement: num 16

It reveals that DATA1 is basically a list with 9 elements:

Element	Content
`DATA1$LT`	Lx/Tx values from each sample
`DATA1$sLT`	Lx/Tx error values from each sample
`DATA1$ITimes`	Irradiation times
`DATA1$dLab`	The lab dose rate
`DATA1$ddot_env`	The environmental dose rate and its variance
`DATA1$regDose`	The regenarated dose points
`DATA1$J`	The number of aliquots selected for each BIN-file
`DATA1$K`	The number of regenarted dose points
`DATA1$Nb_measurement`	The number of measurements per BIN-file

Visualise Lx/Tx values and dose points

To get an impression on how your data look like, you can visualise them by using the function LT_RegenDose():

LT_RegenDose(
  DATA = DATA1,
  Path = path,
  FolderNames = "samp1",
  SampleNames = "samp1",
  Nb_sample = 1,
  nrow = NULL
  )

Note that here we consider only one sample, and the name of the folder is the name of the sample. For that reason the argumetns were set to FolderNames = samp1 and SampleNames = samp1.

Generate data file from BIN/BINX-files of multi-grain OSL measurements

For a multi-grain OSL measurements, instead of Generate_DataFile(), the function Generate_DataFile_MG() should be used with similar parameters. The functions differ by their expectations: Disc.csv instead of DiscPos.csv file for Single-grain OSL Measurements. Please check type ?Generate_DataFile_MG for further information.

Age computation

To compute the age of the sample samp1, you can run the following code:

Age <- Age_Computation(
  DATA = DATA1,
  SampleName = "samp1",
  PriorAge = c(10, 100),
  distribution = "cauchy",
  LIN_fit = TRUE,
  Origin_fit = FALSE,
  Iter = 10000
  )

Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 6
   Unobserved stochastic nodes: 9
   Total graph size: 139

Initializing model



>> Sample name <<
----------------------------------------------
samp1

>> Results of the Gelman and Rubin criterion of convergence <<
----------------------------------------------
     Point estimate Uppers confidence interval
A    1.043       1.119 
D    1.044       1.123 
sD   1.04        1.071 


---------------------------------------------------------------------------------------------------
 *** WARNING: The following information are only valid if the MCMC chains have converged  ***
---------------------------------------------------------------------------------------------------

parameter    Bayes estimate       Credible interval 
----------------------------------------------
A        25.978 
                         lower bound     upper bound
                 at level 95%    10.01       75.969 
                 at level 68%    10.01       34.373 
----------------------------------------------
D        64.56 
                         lower bound     upper bound
                 at level 95%    21.273          191.953 
                 at level 68%    21.489          84.56 
----------------------------------------------
sD       41.399 
                         lower bound     upper bound
                 at level 95%    0.143       183.999 
                 at level 68%    0.719       38.81

This also works if DATA1 is the output of Generate_DataFile_MG().

Remark 1: MCMC trajectories

If MCMC trajectories did not converge, you can add more iteration with the parameter Iter in the function Age_Computation(), for example Iter = 20000 or Iter = 50000.
To increase the precision of prior distribution, if not specified before you can use the argument PriorAge. For example: PriorAge= c(0.01,10) for a young sample and PriorAge = c(10,100) for an old sample.
If the trajectories are still not convergering, you should whether the choice you made with the argument distribution and dose-response curves are meaningful.

Remark 2: `LIN_fit` and `Origin_fit`, dose-response curves option

By default, a saturating exponential plus linear dose response curve is expected. However, you choose other formula by changing arguments LIN_fit and Origin_fit in the function.

Remark 3: `distribution`, equivalent dose dispersion option

By default, a cauchy distribution is assumed, but you can choose another distribution by replacing the word cauchy by gaussian, lognormal_A or lognormal_M for the argument distribution.

The difference between the models: lognormal_A and lognormal_M is that the equivalent dose dispersion are distributed according to:

a log-normal distribution with mean or average equal to the palaeodose for the first model
a log-normal distribution with median equal to the palaeodose for the second model.

Remark 4: `SavePdf` and `SaveEstimates` option

These two arguments allow to save the results to files.

SavePdf = TRUE create a PDF-file with MCMC trajectories of parameters A (age), D (palaeodose), sD (equivalent doses dispersion). You have to specify OutputFileName and OutputFilePath to define name and path of the PDF-file.
SaveEstimates = TRUE saves a CSV-file containing the Bayes estimates, the credible interval at 68% and 95% and the Gelman and Rudin test of convergence of the parameters A, D, sD. For the export the arguments OutputTableName and OutputTablePath have to be specified.

Remark 4: `PriorAge` option

By default, an age between 0.01 ka and 100 ka is expected. If the user has more informations on the sample, PriorAge should be modified accordingly.

For example, if you know that the sample is an older, you can set PriorAge=c(10,120). In contrast, if you know that the sample is younger, you may want to set PriorAge=c(0.001,10). Ages of $<=0$ are not possible. The minimum bound is 0.001.

Please note that the setting of PriorAge is not trivial, wrongly set boundaries are likely biasing your results.

Multiple BIN/BINX-files for one sample

In the previous example we considered only the simplest case: one sample, and one BIN/BINX-file. However, ‘BayLum’ allows to process multiple BIN/BINX-files for one sample. To work with multiple BIN/BINX-files, the names of the subfolders need to beset in argument Names and both files need to be located unter the same Path.

For the case

Names <- c("samp1", "samp2")

the call Generate_DataFile() (or Generate_DataFile_MG()) becomes as follows:

##argument setting
nbsample <- 1
nbbinfile <- length(Names)
Binpersample <- c(length(Names))

##call data file generator
DATA_BF <- Generate_DataFile(
  Path = path,
  FolderNames = Names,
  Nb_sample = nbsample,
  Nb_binfile = nbbinfile,
  BinPerSample = Binpersample,
  verbose = FALSE
  )

##calculate the age
Age <- Age_Computation(
  DATA = DATA_BF,
  SampleName = Names,
  BinPerSample = Binpersample
  )

Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 12
   Unobserved stochastic nodes: 15
   Total graph size: 221

Initializing model



>> Sample name <<
----------------------------------------------
samp1 samp2

>> Results of the Gelman and Rubin criterion of convergence <<
----------------------------------------------
     Point estimate Uppers confidence interval
A    1.005       1.01 
D    1.005       1.011 
sD   1.046       1.063 


---------------------------------------------------------------------------------------------------
 *** WARNING: The following information are only valid if the MCMC chains have converged  ***
---------------------------------------------------------------------------------------------------

parameter    Bayes estimate       Credible interval 
----------------------------------------------
A        2.224 
                         lower bound     upper bound
                 at level 95%    0.04        3.324 
                 at level 68%    1.62        2.734 
----------------------------------------------
D        5.543 
                         lower bound     upper bound
                 at level 95%    0.129       7.906 
                 at level 68%    4.415       6.916 
----------------------------------------------
sD       0.867 
                         lower bound     upper bound
                 at level 95%    0.006       3.689 
                 at level 68%    0.006       0.826

Age analysis of various samples

Generate data file from BIN/BINX-files

The function Generate_DataFile() (or Generate_DataFile_MF()) can process multiple files simultaneously including multiple BIN/BINX-files per sample.

We assume that we are interested in two samples named: sample1 and sample2. In addition, we have two BIN/BINX-files for the first sample named: sample1-1 and sample1-2, and one BIN-file for the 2nd sample named sample2-1. In such case, we need three subfolders named sample1-1, sample1-2 and sample2-1; which each subfolder containing only one BIN-file named bin.BIN, and its associated files DiscPos.csv, DoseEnv.csv, DoseSourve.csv and rule.csv. All of these 3 subfolders must be located in path.

To fill the argument corectly BinPerSample: $binpersample=c(\underbrace{2}_{\text{sample 1: 2 bin files}},\underbrace{1}_{\text{sample 2: 1 bin file}})$

Names <-
  c("sample1-1", "sample1-2", "sample2-1") # give the name of the folder datat
  nbsample <- 2    # give the number of samples
  nbbinfile <- 3   # give the number of bin files
  DATA <- Generate_DataFile(
  Path = path,
  FolderNames = Names,
  Nb_sample = nbsample,
  Nb_binfile = nbbinfile,
  BinPerSample = binpersample
  )

Combine files using the function `combine_DataFiles()`

If the user has already saved informations imported with Generate_DataFile() function (or Generate_DataFile_MG() function) these data can be concatenate with the function combine_DataFiles().

For example, if DATA1 is the output of sample named “GDB3”, and DATA2 is the output of sample “GDB5”, both data can be merged with the following call:

data("DATA1", envir = environment())
data("DATA2", envir = environment())
DATA3 <- combine_DataFiles(L1 = DATA2, L2 = DATA1)
str(DATA3)

List of 9
 $ LT            :List of 2
  ..$ : num [1:188, 1:6] 4.54 2.73 2.54 2.27 1.48 ...
  ..$ : num [1:101, 1:6] 5.66 6.9 4.05 3.43 4.97 ...
 $ sLT           :List of 2
  ..$ : num [1:188, 1:6] 0.333 0.386 0.128 0.171 0.145 ...
  ..$ : num [1:101, 1:6] 0.373 0.315 0.245 0.181 0.246 ...
 $ ITimes        :List of 2
  ..$ : num [1:188, 1:5] 40 40 40 40 40 40 40 40 40 40 ...
  ..$ : num [1:101, 1:5] 160 160 160 160 160 160 160 160 160 160 ...
 $ dLab          : num [1:2, 1:2] 1.53e-01 5.89e-05 1.53e-01 5.89e-05
 $ ddot_env      : num [1:2, 1:2] 2.512 0.0563 2.26 0.0617
 $ regDose       :List of 2
  ..$ : num [1:188, 1:5] 6.14 6.14 6.14 6.14 6.14 6.14 6.14 6.14 6.14 6.14 ...
  ..$ : num [1:101, 1:5] 24.6 24.6 24.6 24.6 24.6 ...
 $ J             : num [1:2] 188 101
 $ K             : num [1:2] 5 5
 $ Nb_measurement: num [1:2] 14 14

The data structure should become as follows

2 lists (1 list per sample) for DATA$LT, DATA$sLT, DATA1$ITimes and DATA1$regDose
A matrix with 2 columns (1 line per sample) for DATA1$dLab, DATA1$ddot_env
2 integers (1 integer per BIN files here we have 1 BIN-file per sample) for DATA1$J, DATA1$K, DATA1$Nb_measurement.

Single-grain and multiple-grain OSL measurements can be merged in the same way. To plot the $L/T$ as a function of the regenerative dose the function LT_RegenDose() can be used again:

LT_RegenDose(
  DATA = DATA3,
  Path = path,
  FolderNames = Names,
  Nb_sample = nbsample,
  SG = rep(TRUE, nbsample)
)

Note: In the example DATA3 contains information from the samples ‘GDB3’ and ‘GDB5’, which are single-grain OSL measurements. For a correct treatment the argument SG has to be manually set be the user. Please see the function manual for further details.

Age analysis without stratigraphic constraints

If there no stratigraphic constraints were set, the following code to analyse simultaneously the age of the sample GDB5 and GDB3.

priorage = c(1, 10, 10, 100)
Age <- AgeS_Computation(
  DATA = DATA3,
  Nb_sample = 2,
  SampleNames = c("GDB5", "GDB3"),
  PriorAge = priorage,
  distribution = "cauchy",
  LIN_fit = TRUE,
  Origin_fit = FALSE,
  Iter = 1000, 
  jags_method = "rjags"
)

Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 1445
   Unobserved stochastic nodes: 1739
   Total graph size: 20624

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
A_GDB5   1.009       1.032 
D_GDB5   1.049       1.167 
sD_GDB5      1.004       1.021 
----------------------------------------------
 Sample name:  GDB3 
---------------------
         Point estimate Uppers confidence interval
A_GDB3   0.998       0.999 
D_GDB3   1.012       1.045 
sD_GDB3      1.001       1.01 


---------------------------------------------------------------------------------------------------
 *** 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 
 A_GDB5      7.141 
                         lower bound     upper bound
                 at level 95%    5.847       8.712 
                 at level 68%    6.296       7.677 

Parameter    Bayes estimate       Credible interval 
 D_GDB5      17.756 
                         lower bound     upper bound
                 at level 95%    16.585          19.061 
                 at level 68%    16.992          18.294 

Parameter    Bayes estimate       Credible interval 
sD_GDB5      4.448 
                         lower bound     upper bound
                 at level 95%    3.376       5.539 
                 at level 68%    3.892       4.945 
----------------------------------------------
 Sample name:  GDB3 
---------------------
Parameter    Bayes estimate       Credible interval 
 A_GDB3      47.371 
                         lower bound     upper bound
                 at level 95%    37.551          59.211 
                 at level 68%    40.065          50.824 

Parameter    Bayes estimate       Credible interval 
 D_GDB3      105.225 
                         lower bound     upper bound
                 at level 95%    97.205          114.359 
                 at level 68%    101.637         109.102 

Parameter    Bayes estimate       Credible interval 
sD_GDB3      16.043 
                         lower bound     upper bound
                 at level 95%    9.34        21.443 
                 at level 68%    13.391          19.448 

----------------------------------------------

Note: For an automated parallel processing you can set the argument jags_method = "rjags" to jags_method = "rjparallel".

Remarks

If the MCMC trajectories did not converge, we refer to the comments above.

As for the function Age_computation(), the age for each sample is set by default between 0.01 ka and 100 ka. If you have more informations on your samples it is possible to change PriorAge parameters. PriorAge is a vector of size = 2*$Nb_sample, the two first values of PriorAge concern the 1st sample, the next two values the 2nd sample and so on.

For example, if you know that sample named GDB5 is a young sample whose its age is between 0.01 ka and 10 ka, and GDB3 is an old sample whose age is between 10 ka and 100 ka, \[PriorAge=c(\underbrace{0.01,10}_{GDB5\ prior\ age},\underbrace{10,100}_{GDB3\ prior\ age})\]

Age analysis with stratigraphic constraints

With the function AgeS_Computation() it is possible to take the stratigraphic relations between samples into account and define constraints.

For example, we know that GDB5 is in a higher stratigraphical position, hence it likely has a younger age than sample GDB3.

Ordering samples

To take into account stratigraphic constraints, the information on the samples need to be ordered. Either you enter a sample name (corresponding to subfolder names) in Names parameter of the function Generate_DataFile(), ordered by order of increasing ages or you enter saved .RData informations of each sample in combine_DataFiles(), ordered by increasing ages.

# using Generate_DataFile function
Names <- c("samp1", "samp2")
nbsample <- 2
DATA3 <- Generate_DataFile(
  Path = path,
  FolderNames = Names,
  Nb_sample = nbsample,
  verbose = FALSE
)

# using the function combine_DataFiles()
data(DATA1, envir = environment()) # .RData on sample GDB3
data(DATA2, envir = environment()) # .RData on sample GDB5
DATA3 <- combine_DataFiles(L1 = DATA1, L2 = DATA2)

Define matrix to set stratigraphic constraints

Let SC be the matrix containing all information on stratigraphic relations for this two samples. This matrix is defined as follows:

matrix dimensions: the row number of StratiConstraints matrix is equal to Nb_sample+1, and column number is equal to $Nb\_sample$.
first matrix row: for all $i$ in $\{1,...,Nb\_Sample\}$, StratiConstraints[1,i] <- 1, means that the lower bound of the sample age given in PriorAge[2i-1] for the sample whose number ID is equal to $i$ is taken into account
sample relations: for all $j$ in ${2,…,Nb_Sample+1}$ and all $i$ in $\{j,...,Nb\_Sample\}$, StratiConstraints[j,i] <- 1 if the sample age whose ID is equal to $j-1$ is lower than the sample age whose ID is equal to $i$. Otherwise, StratiConstraints[j,i] <- 0.

To the define such matrix the function SCMatrix() can be used:

SC <- SCMatrix(Nb_sample = 2,
               SampleNames = c("samp1", "samp2"))

In our case: 2 samples, SC is a matrix with 3 rows and 2 columns. The first row contains c(1,1) (because we take into account the prior ages), the second line contains c(0,1) (sample 2, named samp2 is supposed to be older than sample 1, named samp1) and the third line contains c(0,0) (sample 2, named samp2 is not younger than the sample 1, here named samp1). We can also fill the matrix with the stratigraphic relations as follow:

SC <- matrix(
  data = c(1, 1, 0, 1, 0, 0),
  ncol = 2,
  nrow = (2 + 1),
  byrow = T
  )

Age computation

Age <-
  AgeS_Computation(
  DATA = DATA3,
  Nb_sample = 2,
  SampleNames = c("samp1", "samp2"),
  PriorAge = priorage,
  distribution = "cauchy",
  LIN_fit = TRUE,
  Origin_fit = FALSE,
  StratiConstraints = SC,
  Iter = 1000, 
  jags_method = 'rjags'
  )

Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 1445
   Unobserved stochastic nodes: 1739
   Total graph size: 20624

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:  samp1 
---------------------
         Point estimate Uppers confidence interval
A_samp1      1.005       1.023 
D_samp1      1.004       1.019 
sD_samp1     1.006       1.02 
----------------------------------------------
 Sample name:  samp2 
---------------------
         Point estimate Uppers confidence interval
A_samp2      1.037       1.057 
D_samp2      1.05        1.157 
sD_samp2     1.01        1.042 


---------------------------------------------------------------------------------------------------
 *** 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:  samp1 
---------------------
Parameter    Bayes estimate       Credible interval 
 A_samp1     9.74 
                         lower bound     upper bound
                 at level 95%    9.21        9.999 
                 at level 68%    9.694       9.998 

Parameter    Bayes estimate       Credible interval 
 D_samp1     29.363 
                         lower bound     upper bound
                 at level 95%    24.425          34.388 
                 at level 68%    27.523          32.728 

Parameter    Bayes estimate       Credible interval 
sD_samp1     68.235 
                         lower bound     upper bound
                 at level 95%    55.141          86.815 
                 at level 68%    59.049          74.864 
----------------------------------------------
 Sample name:  samp2 
---------------------
Parameter    Bayes estimate       Credible interval 
 A_samp2     10.408 
                         lower bound     upper bound
                 at level 95%    10          11.165 
                 at level 68%    10          10.458 

Parameter    Bayes estimate       Credible interval 
 D_samp2     18.308 
                         lower bound     upper bound
                 at level 95%    17.279          19.447 
                 at level 68%    17.873          19.012 

Parameter    Bayes estimate       Credible interval 
sD_samp2     4.621 
                         lower bound     upper bound
                 at level 95%    3.754       5.748 
                 at level 68%    4.164       5.253 

----------------------------------------------

BayLum: An Introduction

Claire Christophe, Anne Philippe, Sebastian Kreutzer, Guillaume Guérin

Updated for BayLum package version 0.2.1.9000.20 (2022-01-25)

Introduction

Installing `BayLum’ package

First steps: age analysis of one sample

Import information from a BIN/BINX-file.

Remarks

Data import/export

Data structure

Visualise Lx/Tx values and dose points

Generate data file from BIN/BINX-files of multi-grain OSL measurements

Age computation

Remark 1: MCMC trajectories

Remark 2: `LIN_fit` and `Origin_fit`, dose-response curves option

Remark 3: `distribution`, equivalent dose dispersion option

Remark 4: `SavePdf` and `SaveEstimates` option

Remark 4: `PriorAge` option

Multiple BIN/BINX-files for one sample

Age analysis of various samples

Generate data file from BIN/BINX-files

Combine files using the function `combine_DataFiles()`

Age analysis without stratigraphic constraints

Remarks

Age analysis with stratigraphic constraints

Ordering samples

Define matrix to set stratigraphic constraints

Age computation

References

Further reading

For more details on the diagnostic of Markov chains:

For details on the here used dataset

BayLum: An Introduction

Claire Christophe, Anne Philippe, Sebastian Kreutzer, Guillaume Guérin

Updated for BayLum package version 0.2.1.9000.20 (2022-01-25)

Introduction

Installing `BayLum’ package

First steps: age analysis of one sample

Import information from a BIN/BINX-file.

Remarks

Data import/export

Data structure

Visualise Lx/Tx values and dose points

Generate data file from BIN/BINX-files of multi-grain OSL measurements

Age computation

Remark 1: MCMC trajectories

Remark 2: LIN_fit and Origin_fit, dose-response curves option

Remark 3: distribution, equivalent dose dispersion option

Remark 4: SavePdf and SaveEstimates option

Remark 4: PriorAge option

Multiple BIN/BINX-files for one sample

Age analysis of various samples

Generate data file from BIN/BINX-files

Combine files using the function combine_DataFiles()

Age analysis without stratigraphic constraints

Remarks

Age analysis with stratigraphic constraints

Ordering samples

Define matrix to set stratigraphic constraints

Age computation

References

Further reading

For more details on the diagnostic of Markov chains:

For details on the here used dataset

Remark 2: `LIN_fit` and `Origin_fit`, dose-response curves option

Remark 3: `distribution`, equivalent dose dispersion option

Remark 4: `SavePdf` and `SaveEstimates` option

Remark 4: `PriorAge` option

Combine files using the function `combine_DataFiles()`