README.md
In special-uor/ageR: Supervised Age Modelling
ageR: Supervised Age Modelling
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.
Installation
You can install the development version from
GitHub with:
# install.packages("remotes")
remotes::install_github("special-uor/ageR", "dev")
Example
:warning: Users of Windows: ageR::Bacon creates symbolic links
(think of them as shortcuts), you should run RStudio in “Administrator
mode” to avoid error messages.
Bacon model
Input structure
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.csv
The function ageR::create_input can be used to automatically create
the appropriate file structure (as shown previously).
Create input structure
-
Start by creating a data frame for the sampling depths, this should
have two numeric columns called id and depth:
r
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, and depth. Optionally, a fifth column, cc, can
be included to manually select the calibration curve for each
observation.
r
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 id and depth:
r
hiatus <- data.frame(id = c(1, 2),
depth = c(50, 150))
knitr::kable(hiatus)
| id | depth |
|----:|------:|
| 1 | 50 |
| 2 | 150 |
-
Call the ageR::create_input function. The first parameter should
be a list containing the previous data frames, which must be called
sample_depths, core, and hiatus (if included). Next, a working
directory (wdir), the entity’s name, and optionally the age
model’s name (by default is Bacon).
r
ageR::create_input(data = list(sample_depths = sample_depths,
core = core,
# Optional
hiatus = hiatus),
wdir = "./",
entity = "X",
am = "bacon")
Run 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-run
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.
Change upper and lower bounds
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.
Use mixed calibration curves
This is particular useful for “neo-tropical” sites. It involves three
steps:
-
Assign cc = 4 to the core data:
r
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 between IntCal20 (cc1 = 1)
and SHCal20 (cc2 = 3).
``` r
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::Bacon call:
r
out <- ageR::Bacon(wdir = "./", entity = "X", cc4 = "neotropics.14C", ccdir = ccdir)
Display progress bar
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))
Run example
`%>%` <- 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 |
Best Scenario (Acc. Rate: 10 & Thicknes: 10)
- Log of posterior (MCMC)
- Accumulation Rate: Posterior vs Prior
special-uor/ageR documentation built on July 10, 2024, 10:08 p.m.
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ageR: Supervised Age Modelling
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.
Installation
You can install the development version from GitHub with:
# install.packages("remotes")
remotes::install_github("special-uor/ageR", "dev")
Example
:warning: Users of Windows: ageR::Bacon creates symbolic links
(think of them as shortcuts), you should run RStudio in “Administrator
mode” to avoid error messages.
Bacon model
Input structure
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.csv
The function ageR::create_input can be used to automatically create
the appropriate file structure (as shown previously).
Create input structure
-
Start by creating a data frame for the sampling depths, this should have two numeric columns called
idanddepth:r 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.r 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:r 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).r ageR::create_input(data = list(sample_depths = sample_depths, core = core, # Optional hiatus = hiatus), wdir = "./", entity = "X", am = "bacon")
Run 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-run
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.
Change upper and lower bounds
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.
Use mixed calibration curves
This is particular useful for “neo-tropical” sites. It involves three steps:
-
Assign
cc = 4to thecoredata:r 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).``` r 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:r out <- ageR::Bacon(wdir = "./", entity = "X", cc4 = "neotropics.14C", ccdir = ccdir)
Display progress bar
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))
Run example
`%>%` <- 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 |
Best Scenario (Acc. Rate: 10 & Thicknes: 10)
- Log of posterior (MCMC)
- Accumulation Rate: Posterior vs Prior
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