NiLeDAM

Aurélie Mercadié, Nathalie Vialaneix

19 juin, 2025

Preliminary steps

library(NiLeDAM)
#> 
#> ***********************************************************
#> 
#>       Welcome to the NiLeDAM package
#>       An online graphical interface is available at:
#>         http://shiny.nathalievilla.org/niledam
#>         or run: liveNILEDAM()
#>       Citation details with citation('NiLeDAM')
#> 
#>       For questions/remarks, please contact the maintainer:
#>         nathalie.vialaneix@inrae.fr
#> 
#> ***********************************************************

The package NiLeDAM will be tested on a data set called srilanka, provided by Anne-Magali Seydoux-Guillaume and published in Seydoux-Guillaume et al. (2012). For more information about this dataset, refer to the help page:

?srilanka
data("srilanka")
dim(srilanka)
#> [1] 32  6
summary(srilanka)
#>        U             errU             Th             ErrTh            Pb      
#>  Min.   : 495   Min.   :229.0   Min.   : 41275   Min.   : 856   Min.   :1000  
#>  1st Qu.:1816   1st Qu.:239.0   1st Qu.: 62489   1st Qu.:1006   1st Qu.:1548  
#>  Median :6275   Median :271.0   Median :187783   Median :1711   Median :4562  
#>  Mean   :4276   Mean   :256.8   Mean   :139063   Mean   :1433   Mean   :3457  
#>  3rd Qu.:6760   3rd Qu.:274.0   3rd Qu.:199075   3rd Qu.:1768   3rd Qu.:4871  
#>  Max.   :7359   Max.   :277.0   Max.   :215401   Max.   :1852   Max.   :5365  
#>      ErrPb      
#>  Min.   :314.0  
#>  1st Qu.:318.0  
#>  Median :338.0  
#>  Mean   :331.3  
#>  3rd Qu.:341.0  
#>  Max.   :345.0

Using all analyses

calculateAges()

As a first step, ages are calculated with the help of the function calculateAges().

?calculateAges

Remark: the data frame used in this function must have exactly 6 columns as the srilanka data set has (in that order). If not, then the function will generate an error.

calculated.ages <- calculateAges(srilanka, nloops = 10, seed = 12, verbose = TRUE)
#> Age estimation...
#> MC simulations...
#>  (it might take a while if 'nloops' is large...)
#> 
#> 32 ages and confidence intervals estimated from 10 bootstrap samples.
#>  Summary:
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>   447.0   491.2   505.5   515.3   546.5   598.0

What contains this ages object ?

calculated.ages@ages
#>   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20 
#> 569 447 598 515 543 567 557 567 458 497 481 497 520 519 477 559 515 525 493 581 
#>  21  22  23  24  25  26  27  28  29  30  31  32 
#> 561 501 479 500 482 505 486 468 506 501 521 494
calculated.ages@ci
#>             1       2       3       4      5       6       7      8       9
#> 2.5%  530.200 384.375 534.600 443.800 487.35 541.900 478.350 521.35 448.225
#> 97.5% 660.325 542.125 662.425 569.725 586.55 612.475 603.175 599.50 486.775
#>            10      11      12     13      14      15      16      17      18
#> 2.5%  447.825 457.925 475.575 496.35 496.025 438.675 434.725 336.075 390.625
#> 97.5% 508.775 508.500 512.975 543.65 539.325 549.275 744.650 572.775 596.475
#>            19      20      21      22      23      24      25     26      27
#> 2.5%  421.175 500.125 497.175 480.675 439.575 475.925 441.925 473.90 473.575
#> 97.5% 535.400 680.450 692.750 533.625 485.775 522.200 510.000 530.05 505.600
#>           28     29      30     31      32
#> 2.5%  453.90 485.95 489.125 488.60 468.725
#> 97.5% 488.75 531.30 518.975 540.65 507.875

tests()

Then, one can estimate the number of age populations using the previously generated object. This is done with the function tests, which help page is accessible with:

?tests

One can either test a unique number or a sequence of numbers to find the most likely number of different populations of ages (or common ages).

Here, 1 to 3 populations are tested:

res.tests <- tests(calculated.ages, nbmin = 1, nbmax = 3, verbose = TRUE)
#> Test if the 32 estimated ages are coming from
#>  1 2 3 
#> population(s).
#> 
#>     The ages are found to be likely coming from 2 population(s) at level 5 %.
#>     Chi2 test statistic: 26.52015  ~ df: 30 
#> 
#>     Estimated ages: 566 494 
#> 
#>     Population numbers:
#>  1 2 1 2 1 1 1 1 2 2 2 2 2 2 2 1 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2

It appears that the samples come from 2 different populations of ages.

Plotting methods

Finally, the results can be visualized thanks to the following methods:

plot(res.tests)

popline(res.tests)

Removing the first 8 analyses (control group)

srilanka2 <- srilanka[-(1:8), ]

calculateAges()

calculated.ages <- calculateAges(srilanka2, nloops = 10, seed = 12)
#> Age estimation...
#> MC simulations...
#>  (it might take a while if 'nloops' is large...)
#> 
#> 24 ages and confidence intervals estimated from 10 bootstrap samples.
#>  Summary:
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>   458.0   485.0   500.5   505.2   519.2   581.0

tests()

res.tests <- tests(calculated.ages, nbmax = 3)
#> Test if the 24 estimated ages are coming from
#>  1 2 3 
#> population(s).
#> 
#>     The ages are found to be likely coming from 1 population(s) at level 5 %.
#>     Chi2 test statistic: 28.15801  ~ df: 23 
#> 
#>     Estimated ages: 493 
#> 
#>     Population numbers:
#>  1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

This time, only one population is identified (as expected).

Plotting methods

plot(res.tests)

popline(res.tests)

Graphical user interface

NiLeDAM also contains a shiny graphical interface that can be used to perform the actions described above. This interface in launched using:

liveNiLeDAM()

References

Seydoux-Guillaume A.M., Montel J.M., Bingen B., Bosse V., de Parseval P., Paquette J.L., Janots E., Wirth R. (2012) Low-temperature alteration of monazite: fluid mediated coupled dissolution-precipitation, irradiation damage and disturbance of the U-Pb and Th-Pb chronometers. Chemical Geology, 330–331, 140–158.