The goal of ageR is to provide functions that facilitate the creation of age models using different data sources, including databases, comma and tab separated files.
You can install the development version from GitHub with:
# install.packages("remotes")
remotes::install_github("special-uor/ageR", "dev")ageR::Bacon creates symbolic links
(think of them as shortcuts), you should run RStudio in “Administrator
mode” to avoid error messages.
For an entity X, the input structure should look like
ageR::file_structure(entity = "X", am = "bacon")
#> levelName
#> 1 X
#> 2 ¦--Bacon_runs
#> 3 ¦ °--X
#> 4 ¦ ¦--X_depths.txt
#> 5 ¦ ¦--X_sample_ids.csv
#> 6 ¦ °--X.csv
#> 7 ¦--hiatus.csv
#> 8 °--not_used_dates.csvThe function ageR::create_input can be used to automatically create
the appropriate file structure (as shown previously).
-
Start by creating a data frame for the sampling depths, this should have two numeric columns called
idanddepth:sample_depths <- data.frame(id = 1:100, depth = seq(0, 500, length.out = 100)) knitr::kable(head(sample_depths))
id depth 1 0.000000 2 5.050505 3 10.101010 4 15.151515 5 20.202020 6 25.252525 -
Create a data frame with the core’s data, this should have at least four columns. The first column can be of character type and the remaining must be numeric. The column names should be
labID,age,error, anddepth. Optionally, a fifth column,cc, can be included to manually select the calibration curve for each observation.core <- data.frame(labID = paste0("X", sprintf("%03d", 1:5)), age = c(50, 200, 1150, 2060, 4050), error = c(10, 15, 5, 1, 70), depth = c(5, 100, 230, 300, 450), cc = 1) knitr::kable(core)
labID age error depth cc X001 50 10 5 1 X002 200 15 100 1 X003 1150 5 230 1 X004 2060 1 300 1 X005 4050 70 450 1 -
(Optional) Create a data frame with the hiatuses depths, this should have two numeric columns called
idanddepth:hiatus <- data.frame(id = c(1, 2), depth = c(50, 150)) knitr::kable(hiatus)
id depth 1 50 2 150 -
Call the
ageR::create_inputfunction. The first parameter should be a list containing the previous data frames, which must be calledsample_depths,core, andhiatus(if included). Next, a working directory (wdir), the entity’s name, and optionally the age model’s name (by default is Bacon).ageR::create_input(data = list(sample_depths = sample_depths, core = core, # Optional hiatus = hiatus), wdir = "./", entity = "X", am = "bacon")
ageR::Bacon(wdir = "./",
entity = "X",
# Optional parameters
cpus = 1,
postbomb = 0,
cc = 0,
seed = NA,
alt_depths = NULL,
quiet = FALSE,
acc_step = 5,
acc_lower = NULL,
acc_upper = NULL,
thick_step = 5,
thick_lower = NULL,
thick_upper = NULL,
dry_run = FALSE,
...)Dry-runs are useful to check how many scenarios will be generated with the current configuration:
ageR::Bacon(wdir = "./", entity = "X", dry_run = TRUE, quiet = TRUE)
#> The following scenarios will be executed:
#>
#>
#> | Accumulation rate| Thickness|
#> |-----------------:|---------:|
#> | 5| 10|
#> | 10| 10|
#> | 20| 10|
#> | 5| 15|
#> | 10| 15|
#> | 20| 15|
#> | 5| 20|
#> | 10| 20|
#> | 20| 20|
#> | 5| 30|
#> | 10| 30|
#> | 20| 30|
#> | 5| 40|
#> | 10| 40|
#> | 20| 40|
#> A total of 15 scenarios.Based on a dry-run, we might want to constraint our execution to a smaller set of scenarios. This can be done for both accumulation rate and core segment thickness.
ageR::Bacon(wdir = "./", entity = "X", acc_lower = 10, acc_upper = 20, dry_run = TRUE, quiet = TRUE)
#> The following scenarios will be executed:
#>
#>
#> | Accumulation rate| Thickness|
#> |-----------------:|---------:|
#> | 10| 10|
#> | 20| 10|
#> | 10| 15|
#> | 20| 15|
#> | 10| 20|
#> | 20| 20|
#> | 10| 30|
#> | 20| 30|
#> | 10| 40|
#> | 20| 40|
#> A total of 10 scenarios.ageR::Bacon(wdir = "./", entity = "X", thick_lower = 10, thick_upper = 30, dry_run = TRUE, quiet = TRUE)
#> The following scenarios will be executed:
#>
#>
#> | Accumulation rate| Thickness|
#> |-----------------:|---------:|
#> | 5| 10|
#> | 10| 10|
#> | 20| 10|
#> | 5| 15|
#> | 10| 15|
#> | 20| 15|
#> | 5| 20|
#> | 10| 20|
#> | 20| 20|
#> | 5| 30|
#> | 10| 30|
#> | 20| 30|
#> A total of 12 scenarios.This is particular useful for “neo-tropical” sites. It involves three steps:
-
Assign
cc = 4to thecoredata:core <- data.frame(labID = paste0("X", sprintf("%03d", 1:5)), age = c(50, 200, 1150, 2060, 4050), error = c(10, 15, 5, 1, 70), depth = c(5, 100, 230, 300, 450), cc = 4) knitr::kable(core)
labID age error depth cc X001 50 10 5 4 X002 200 15 100 4 X003 1150 5 230 4 X004 2060 1 300 4 X005 4050 70 450 4 -
Create a mixed calibration curve with
ageR::mix_curves. The following example uses a 50/50 mix betweenIntCal20(cc1 = 1) andSHCal20(cc2 = 3).ccdir <- "./ccurves" ageR::mix_curves(proportion = 0.5, cc1 = 1, cc = 3, name = "neotropics.14C", dir = ccdir) #> -------------------------------------------------------------------------------- #> | Mixed curved: 50/50 created. | #> --------------------------------------------------------------------------------
-
Add two new parameters to the
ageR::Baconcall:out <- ageR::Bacon(wdir = "./", entity = "X", cc4 = "neotropics.14C", ccdir = ccdir)
A progress bar can be displayed for long computations. Just “pipe” the
function call to ageR::pb().
`%>%` <- magrittr::`%>%`
out <- ageR::Bacon(wdir = "./", entity = "X", cpus = 8, verbose = FALSE) %>%
ageR::pb()Alternatively, if you are not familiar with the “pipe” operator, you can run the following code:
out <- ageR::pb(ageR::Bacon(wdir = "./", entity = "X", cpus = 2))`%>%` <- magrittr::`%>%`
out <- ageR::Bacon(wdir = "./", entity = "X", cpus = 8, verbose = FALSE) %>%
ageR::pb()
#> --------------------------------------------------------------------------------
#> | Checking input files |
#> --------------------------------------------------------------------------------
#> --------------------------------------------------------------------------------
#> | Loading input files |
#> --------------------------------------------------------------------------------
#> --------------------------------------------------------------------------------
#> | Setting up environment |
#> --------------------------------------------------------------------------------
#> --------------------------------------------------------------------------------
#> | Running Bacon |
#> --------------------------------------------------------------------------------
#> --------------------------------------------------------------------------------
#> | S001-AR005-T10 |
#> --------------------------------------------------------------------------------
#> Boundary set at depth(s) NA
#> Will run 7,425,000 iterations and store 2,000
#> Calculating age ranges...
#>
#> Preparing ghost graph...
#>
#> Mean 95% confidence ranges 426 yr, min. 136 yr at 303.030303030303 cm, max. 920 yr at 500 cm
#> 80% of the dates overlap with the age-depth model (95% ranges)
#>
#> --------------------------------------------------------------------------------
#> | Saving results |
#> --------------------------------------------------------------------------------
#> --------------------------------------------------------------------------------
#> | S002-AR010-T10 |
#> --------------------------------------------------------------------------------
#> Boundary set at depth(s) NA
#> Will run 7,425,000 iterations and store 2,000
#> Calculating age ranges...
#>
#> Preparing ghost graph...
#>
#> Mean 95% confidence ranges 524 yr, min. 140 yr at 0 cm, max. 1058 yr between 368.686868686869 and 500 cm
#> 100% of the dates overlap with the age-depth model (95% ranges)
#>
#> --------------------------------------------------------------------------------
#> | Saving results |
#> --------------------------------------------------------------------------------
#> --------------------------------------------------------------------------------
#> | S003-AR020-T10 |
#> --------------------------------------------------------------------------------
#> Boundary set at depth(s) NA
#> Will run 7,425,000 iterations and store 2,000
#> Calculating age ranges...
#>
#> Preparing ghost graph...
#>
#> Mean 95% confidence ranges 539 yr, min. 137 yr at 5.05050505050505 cm, max. 1569 yr at 500 cm
#> 100% of the dates overlap with the age-depth model (95% ranges)
#>
#> --------------------------------------------------------------------------------
#> | Saving results |
#> --------------------------------------------------------------------------------
#> --------------------------------------------------------------------------------
#> | S004-AR005-T15 |
#> --------------------------------------------------------------------------------
#> Boundary set at depth(s) NA
#> Will run 5,087,500 iterations and store 2,000
#> Calculating age ranges...
#>
#> Preparing ghost graph...
#>
#> Mean 95% confidence ranges 562 yr, min. 92 yr at 10.1010101010101 cm, max. 1482 yr at 500 cm
#> 80% of the dates overlap with the age-depth model (95% ranges)
#>
#> --------------------------------------------------------------------------------
#> | Saving results |
#> --------------------------------------------------------------------------------
#> --------------------------------------------------------------------------------
#> | S005-AR010-T15 |
#> --------------------------------------------------------------------------------
#> Boundary set at depth(s) NA
#> Will run 5,087,500 iterations and store 2,000
#> Calculating age ranges...
#>
#> Preparing ghost graph...
#>
#> Mean 95% confidence ranges 572 yr, min. 137 yr at 10.1010101010101 cm, max. 1291 yr at 388.888888888889 cm
#> 100% of the dates overlap with the age-depth model (95% ranges)
#>
#> --------------------------------------------------------------------------------
#> | Saving results |
#> --------------------------------------------------------------------------------
#> --------------------------------------------------------------------------------
#> | S006-AR020-T15 |
#> --------------------------------------------------------------------------------
#> Boundary set at depth(s) NA
#> Will run 5,087,500 iterations and store 2,000
#> Calculating age ranges...
#>
#> Preparing ghost graph...
#>
#> Mean 95% confidence ranges 603 yr, min. 96 yr at 0 cm, max. 1747 yr at 500 cm
#> 100% of the dates overlap with the age-depth model (95% ranges)
#>
#> --------------------------------------------------------------------------------
#> | Saving results |
#> --------------------------------------------------------------------------------
#> --------------------------------------------------------------------------------
#> | S007-AR005-T20 |
#> --------------------------------------------------------------------------------
#> Boundary set at depth(s) NA
#> Will run 3,987,500 iterations and store 2,000
#> Calculating age ranges...
#>
#> Preparing ghost graph...
#>
#> Mean 95% confidence ranges 764 yr, min. 119 yr at 5.05050505050505 cm, max. 2080 yr at 500 cm
#> 100% of the dates overlap with the age-depth model (95% ranges)
#>
#> --------------------------------------------------------------------------------
#> | Saving results |
#> --------------------------------------------------------------------------------
#> --------------------------------------------------------------------------------
#> | S008-AR010-T20 |
#> --------------------------------------------------------------------------------
#> Boundary set at depth(s) NA
#> Will run 3,987,500 iterations and store 2,000
#> Calculating age ranges...
#>
#> Preparing ghost graph...
#>
#> Mean 95% confidence ranges 599 yr, min. 126 yr at 5.05050505050505 cm, max. 1365 yr at 378.787878787879 cm
#> 100% of the dates overlap with the age-depth model (95% ranges)
#>
#> --------------------------------------------------------------------------------
#> | Saving results |
#> --------------------------------------------------------------------------------
#> --------------------------------------------------------------------------------
#> | S009-AR020-T20 |
#> --------------------------------------------------------------------------------
#> Boundary set at depth(s) NA
#> Will run 3,987,500 iterations and store 2,000
#> Calculating age ranges...
#>
#> Preparing ghost graph...
#>
#> Mean 95% confidence ranges 631 yr, min. 126 yr at 5.05050505050505 cm, max. 2051 yr at 500 cm
#> 100% of the dates overlap with the age-depth model (95% ranges)
#>
#> --------------------------------------------------------------------------------
#> | Saving results |
#> --------------------------------------------------------------------------------
#> --------------------------------------------------------------------------------
#> | S010-AR005-T30 |
#> --------------------------------------------------------------------------------
#> Boundary set at depth(s) NA
#> Will run 2,750,000 iterations and store 2,000
#> Calculating age ranges...
#>
#> Preparing ghost graph...
#>
#> Mean 95% confidence ranges 744 yr, min. 127 yr at 10.1010101010101 cm, max. 1697 yr at 500 cm
#> 100% of the dates overlap with the age-depth model (95% ranges)
#>
#> --------------------------------------------------------------------------------
#> | Saving results |
#> --------------------------------------------------------------------------------
#> --------------------------------------------------------------------------------
#> | S011-AR010-T30 |
#> --------------------------------------------------------------------------------
#> Boundary set at depth(s) NA
#> Will run 2,750,000 iterations and store 2,000
#> Calculating age ranges...
#>
#> Preparing ghost graph...
#>
#> Mean 95% confidence ranges 665 yr, min. 98 yr at 15.1515151515152 cm, max. 1699 yr at 388.888888888889 cm
#> 100% of the dates overlap with the age-depth model (95% ranges)
#>
#> --------------------------------------------------------------------------------
#> | Saving results |
#> --------------------------------------------------------------------------------
#> --------------------------------------------------------------------------------
#> | S012-AR020-T30 |
#> --------------------------------------------------------------------------------
#> Boundary set at depth(s) NA
#> Will run 2,750,000 iterations and store 2,000
#> Calculating age ranges...
#>
#> Preparing ghost graph...
#>
#> Mean 95% confidence ranges 687 yr, min. 101 yr at 5.05050505050505 cm, max. 2322 yr at 500 cm
#> 100% of the dates overlap with the age-depth model (95% ranges)
#>
#> --------------------------------------------------------------------------------
#> | Saving results |
#> --------------------------------------------------------------------------------
#> --------------------------------------------------------------------------------
#> | S013-AR005-T40 |
#> --------------------------------------------------------------------------------
#> Boundary set at depth(s) NA
#> Will run 2,200,000 iterations and store 2,000
#> Calculating age ranges...
#>
#> Preparing ghost graph...
#>
#> Mean 95% confidence ranges 645 yr, min. 102 yr at 5.05050505050505 cm, max. 1638 yr at 398.989898989899 cm
#> 100% of the dates overlap with the age-depth model (95% ranges)
#>
#> --------------------------------------------------------------------------------
#> | Saving results |
#> --------------------------------------------------------------------------------
#> --------------------------------------------------------------------------------
#> | S014-AR010-T40 |
#> --------------------------------------------------------------------------------
#> Boundary set at depth(s) NA
#> Will run 2,200,000 iterations and store 2,000
#> Calculating age ranges...
#>
#> Preparing ghost graph...
#>
#> Mean 95% confidence ranges 627 yr, min. 101 yr at 10.1010101010101 cm, max. 1689 yr at 358.585858585859 cm
#> 100% of the dates overlap with the age-depth model (95% ranges)
#>
#> --------------------------------------------------------------------------------
#> | Saving results |
#> --------------------------------------------------------------------------------
#> --------------------------------------------------------------------------------
#> | S015-AR020-T40 |
#> --------------------------------------------------------------------------------
#> Boundary set at depth(s) NA
#> Will run 2,200,000 iterations and store 2,000
#> Calculating age ranges...
#>
#> Preparing ghost graph...
#>
#> Mean 95% confidence ranges 679 yr, min. 120 yr at 5.05050505050505 cm, max. 2261 yr at 500 cm
#> 100% of the dates overlap with the age-depth model (95% ranges)
#>
#> --------------------------------------------------------------------------------
#> | Saving results |
#> --------------------------------------------------------------------------------
#> --------------------------------------------------------------------------------
#> | Bacon QC |
#> --------------------------------------------------------------------------------
#> --------------------------------------------------------------------------------
#> | Best scenario: Acc. Rate = 10yr/cm - Thickness: 10cm |
#> --------------------------------------------------------------------------------
#> --------------------------------------------------------------------------------
#> | Plot Accumulation Rate |
#> --------------------------------------------------------------------------------
#> --------------------------------------------------------------------------------
#> | Plot Accumulation Rate: Posterior vs Prior |
#> --------------------------------------------------------------------------------
#> --------------------------------------------------------------------------------
#> | Plot Log Posterior (MCMC) |
#> --------------------------------------------------------------------------------
#> --------------------------------------------------------------------------------
#> | Bye! |
#> --------------------------------------------------------------------------------
#> X: 55.833 sec elapsed
| Acc. Rate | Thickness | Area Between Curves | Relative Bias | |
|---|---|---|---|---|
| 2 | 10 | 10 | 1.086173 | 6.70e-17 |
| 5 | 10 | 15 | 1.251990 | 2.68e-17 |
| 1 | 5 | 10 | 1.353079 | -4.43e-17 |
| 8 | 10 | 20 | 1.359341 | -7.61e-17 |
| 11 | 10 | 30 | 1.615011 | -4.70e-17 |
| 14 | 10 | 40 | 1.645746 | 2.46e-17 |
| 3 | 20 | 10 | 1.654022 | 8.91e-18 |
| 4 | 5 | 15 | 1.754415 | -4.29e-17 |
| 9 | 20 | 20 | 1.860253 | -1.24e-16 |
| 12 | 20 | 30 | 1.863718 | 8.20e-17 |
| 15 | 20 | 40 | 2.068277 | 2.16e-17 |
| 6 | 20 | 15 | 2.173148 | 1.05e-16 |
| 7 | 5 | 20 | 2.278536 | -7.75e-17 |
| 10 | 5 | 30 | 4.275709 | 8.29e-18 |
| 13 | 5 | 40 | 5.736973 | -3.99e-17 |
- Log of posterior (MCMC)
- Accumulation Rate: Posterior vs Prior
