SyncER Workflow
Step 0: Load and set-up SyncER example data
This vignette’s example dataset is distributed separately in the
companion package SyncERdata (a Suggests
dependency, not required to use SyncER itself on your own
data). Install it with install.packages("SyncERdata") to
run the vignette yourself.
library(SyncER)
has_data <- requireNamespace("SyncERdata", quietly = TRUE)
knitr::opts_chunk$set(eval = has_data) # every chunk below needs SyncERdata; skip them all if it's missing
# Choose where this example should run (syncer_wd), and copy the bundled SyncERdata example into it. Default is a temporary directory.
syncer_wd <- file.path(tempdir(), "SyncER_example")
dir.create(file.path(syncer_wd, "record_data_input"), recursive = TRUE, showWarnings = FALSE)
invisible(file.copy(
list.files(system.file("extdata", "record_data_input", package = "SyncERdata"), full.names = TRUE),
file.path(syncer_wd, "record_data_input"), overwrite = TRUE
))
# Restore the completed Bacon/Plum output shipped as SyncERdata::bacon_out_files
# (a named list of file lines, keyed by relative output path) back to real files.
for (rel_path in names(SyncERdata::bacon_out_files)) {
full_path <- file.path(syncer_wd, rel_path)
dir.create(dirname(full_path), recursive = TRUE, showWarnings = FALSE)
readr::write_lines(SyncERdata::bacon_out_files[[rel_path]], full_path)
}
# Restore the posterior age-sample tables shipped as SyncERdata list-of-data-frame objects,
# using SyncER's own write_age_output_data() to write one CSV per record.
out_dir <- file.path(syncer_wd, "SyncER_outputs")
write_age_output_data(SyncERdata::out_data_ages, out_dir, verbose = FALSE)
write_age_output_data(SyncERdata::out_data_ages_synced, out_dir, synced = "_synced", verbose = FALSE)
# Run every following chunk from that folder.
knitr::opts_knit$set(root.dir = syncer_wd)
Step 1: Set up the SyncER workflow
The setup chunk above already copied the bundled Example dataset from
the installed package into syncer_wd, the working directory
you chose. Call syncer_setup() to point SyncER at
that folder for the rest of this document and to create the
SyncER_outputs folder there. When applying this to your own
data, simply place your own record_data_input/ folder in
syncer_wd instead (and skip the copy step in the setup
chunk). This is a folder with one CSV file per record you would like to
be considered (named <record>.csv). Each file
represents a single record, for which information on the depth, age
& error for each dated sample and the record top is given. Also
include the radiocarbon calibration curve that is required (0=no
calibration, calendar ages; 1=IntCal20; 2=Marine20; 3=SHCal20).
Additionally, the depths of the considered event deposits needs to be
included. You are free to give the input depths as event-free depth or
total depth.
output_dir <- syncer_setup(wd = syncer_wd)
## SyncER directory: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example
## SyncER output will be saved to: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example/SyncER_outputs
file_name <- "record_data_input"
List every type of event deposit that is included in your records in
the single horizon_groups variable below (in the
example: isochron, synchronous, synchro-test and
non-synchronous). Each entry is named after the deposit’s label in
your input file and tagged with a role:
"isochron" - a synchronous reference horizon used to
synchronize your age models (steps 3-5). Your isochrons should
have unique names so that SyncER knows which ones to match
between records. You can choose either generic names (e.g.,
isochron1, isochron2 as in the example) or the actual name of
the deposit (e.g., a tephra name)."test" - a horizon whose potential synchronicity you
want to evaluate (steps 6-7). You have the option to compare one deposit
from each record, in which case you can use a single unique name. If you
want to compare potential synchronicity of one layer in a record to
multiple layers in another record, give it a generic name
(e.g. synchro-test) and list the actual per-record labels
(e.g. synchro-test, synchro-test-wrong) under
members; you can define as many independent
"test" entries as you need (e.g. add a second one to test
another, unrelated set of horizons in the same run)."other" - present in your records but not itself tested
(e.g. the synchronous/non-synchronous markers
in the example).
members only needs to be set when several
differently-named labels in your records should be compared together as
one group; it defaults to the entry’s own name. It is important to
consider all possible horizon correlations for which you want to test
for synchronicity and give them appropriate (unique) names, as the names
defined here will be used throughout the SyncER workflow and
cannot be adjusted without running the whole workflow again.
Populate the event_depths and hiatuses
variables in case you want to consider instantaneous deposits and/or
hiatuses, respectively, in some of your records. Finally, provide the
software with the label used for your radiocarbon ages and Pb-based ages
(if included).
horizon_groups <- list(
"isochron1" = list(role = "isochron"),
"isochron2" = list(role = "isochron"),
"isochron3" = list(role = "isochron"),
"isochron4" = list(role = "isochron"),
"isochron5" = list(role = "isochron"),
"isochron6" = list(role = "isochron"),
"isochron7" = list(role = "isochron"),
"isochron8" = list(role = "isochron"),
"isochron9" = list(role = "isochron"),
"synchronous" = list(role = "other"),
"non-synchronous" = list(role = "other"),
"synchro-test" = list(role = "test", members = c("synchro-test", "synchro-test-wrong"))
# Add one entry per event type in your records here. To test a second, independent set of
# horizons in the same run, just add another "test" entry, e.g.:
# "synchro-test2" = list(role = "test", members = c("synchro-test2", "synchro-test2-wrong"))
)
event_depths <- list("core1"=c(), "core2"=c(), "core3"=c(), "core4"=c(), "core5"=c()) # List with all event depths that should be considered as instantaneous deposits should you not have worked with event-free depths in the input file
hiatuses <- list("core1"=c(), "core2"=c(), "core3"=c(), "core4"=c(), "core5"=c()) # List with all depths that should be considered as hiatuses per record
radiocarbon_sample_names <- c("sample") # indicate how your radiocarbon samples are labeled
lead_sample_names <- c("") #indicate how your Pb ages are labelled, leave blank if you only consider radiocarbon ages
invisible(list2env(load_horizon_names(horizon_groups), environment())) # derives event_types, isochrons, test_events, isochron_groups, and test_horizon_groups
Step 2: Age-depth modelling
If you want to make use of the built-in compatibility with rbacon
or rplum, it is recommended to first run the
age-depth models separately (in a different folder) to experiment with
which parameters to use for each record. Subsequently, you can construct
an age-depth model for each of your records in the SyncER framework. To
do so, set up the correct folder structure to allow SyncER to
run without issues using the bacon-setup chunk below. This creates a
separate folder for each record, and places a *.csv file in
each folder containing all age info for that record.
input_record_data <- read_record_data(file_name = file_name)
record_data <- input_record_data$record_data
max_depths <- input_record_data$max_depths # This extends your age-depth models to the depth of the top of the deepest event mentioned in the input file. If you want to have them extend deeper, set them separately per record as such: max_depths <- c("record1"=100, "record2"=200)
sedrates <- input_record_data$sedrates
age_model_input(record_data,
radiocarbon_sample_names = radiocarbon_sample_names,
lead_sample_names = lead_sample_names)
## C14 data for core1 saved to: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example/core1/core1.csv
## C14 data for core2 saved to: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example/core2/core2.csv
## C14 data for core3 saved to: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example/core3/core3.csv
## C14 data for core4 saved to: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example/core4/core4.csv
## C14 data for core5 saved to: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example/core5/core5.csv
The code below includes some standard settings and automatically
determines the ages of all depths until the deepest depth given in the
record_data_input/ file (be it a sample or an event
deposit). If you need specific settings for a certain record (e.g.,
instantaneous deposits in case your input file contains total depth and
not event-free depth), adjust the first part of this coding block to
match the rbacon or rplum command you need (details on
the possible input variations can be found in their respective manuals)
and make sure to uncomment this part. Otherwise, all records will be ran
with the same standard settings.
Actually calling Bacon()/Plum() requires
the rbacon/rplum packages to be installed and loaded
(library(rbacon) or library(rplum)) -
SyncER does not load them for you. The bundled
Example dataset already comes with completed Bacon runs in
the core1-core5 folders, so there is no need
to install rbacon/rplum or (re)run the models just to
follow this vignette: run_age below defaults to
FALSE, and every Bacon()/Plum()
call in the remainder of this document is wrapped in
if (run_age) { ... }, so it is skipped and the existing
model output is left untouched. load_event_ages()
afterwards reads directly from those *.out files. Set
run_age <- TRUE (and load
rbacon/rplum) if you want to reconstruct the age-depth
models yourself, e.g. for your own dataset.
run_age <- FALSE # set to TRUE to actually (re)run rbacon/rplum; requires library(rbacon) or library(rplum)
if (run_age) {
for (record in names(record_data)){
# Adjust this block if you need specific settings for a certain record, and copy it if you need different settings for multiple records. Don't forget to uncomment it to make sure your age-depth models are constructed correctly. Or change to Plum if you defined lead_sample_names.
# if (record=="record"){ ## Replace "record" by the name of the record for which you require specific settings
# Bacon(core = record,
# coredir = syncer_wd,
# sep = ',',
# rotate.axes = TRUE,
# thick = 2,
# hiatus = hiatuses[[record]],
# slump = event_depths[[record]],
# ssize = 10000,
# d.max = max_depths[record],
# acc.mean = round(sedrates[record]),
# suggest = FALSE,
# accept.suggestions = TRUE
# )
# } else if {
Bacon(core = record,
coredir = syncer_wd,
sep = ',',
rotate.axes = TRUE,
thick = 2,
ssize = 1000,
slump = event_depths[[record]],
hiatus = hiatuses[[record]],
d.max = max_depths[record],
acc.mean = round(sedrates[record]),
suggest = FALSE,
accept.suggestions = TRUE,
run = FALSE ## set to FALSE in case you already have the age models constructed
)
# }
}
}
Extract the ages of the event deposits in each record and export them
to the out_data_ages/ folder.
reload_existing <- TRUE ## FALSE processes the bundled .out files and creates out_data_ages/; set TRUE to reuse an existing out_data_ages/
event_ages <- load_event_ages(record_data = record_data, event_types = event_types, max_depths = max_depths, isochrons = isochrons, test_horizons = test_events, reload_existing = reload_existing, instantaneous_event_depths=event_depths, thick = 2 ## set to the thick value used in Bacon/Plum, single value or as list (for example: list("core1" = 2, "core3 = 1))
)
## Data successfully read from: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example/SyncER_outputs/out_data_ages
## Loaded 5 record(s): core1, core2, core3, core4, core5
if (!reload_existing) {
write_age_output_data(event_ages)}
Once you have established the age models and extracted all required
ages, you do not need to re-run all the age-depth models when you simply
want to check for additional synchronicity events. This can be achieved
by using the run = FALSE argument when running the
{r age-depth modelling} code block and setting the
reload_existing variable to TRUE in the
{r extract-ages} block.
Step 3: Evaluate synchronicity of isochrons
Once the age-depth models have been constructed, you can evaluate
whether they indeed allow for the isochrons to be synchronous. To test
for potential synchronicity, log-transformations will be used, meaning
that no negative or zero values can exist in the age dataset. This
requires an age_offset value to be added so that the age
reference point is, for example, the year in which the (most recent)
records were retrieved, rather than 1950 AD as used in radiocarbon
dating. In SyncER, age_offset is derived
automatically via bp_datum() (current year minus 1950), but
can be set manually if desired.
You also need to predefine what you want to consider as synchronous
deposits so that a synchronicity score can be calculated. This is an
assumption-free test that calculates the percentage of possible ages for
each deposit that fall within a predefined (small) timeframe, or in
other words, the probability that both events can be assumed to be
deposited in the same predefined timeframe. To do so, you need to
provide the maximum age_difference between both records you
consider reasonable to still assume synchronicity. For example, for an
age_difference of 0.05, the synchronicity score represents
the confidence level with which we can assume the maximum age difference
between both records is 5%. For an age_difference of 1 or
larger, these will be considered as absolute age differences to be
considered (e.g. 10 yrs). Different values can be set for different
horizons, and a default value for non-specified horizons should be given
as last argument. You also need to set a confidence_level
(ratio) with which you want to test for possible synchronicity. For
example, when the confidence_level parameter is set to
0.95, only correlations for which synchronicity scores higher than 0.95
will pass the test.
age_offset <- bp_datum() # value to ensure no negative values in the dataset
confidence_level = 0.95
age_difference = 0.07
isochron_stats <- process_event_ages(event_ages, isochrons, offset=age_offset)
isochron_synchro <- compute_synchronicity_values(isochron_stats, isochrons, horizon_groups = isochron_groups, confidence_level = confidence_level, age_difference = age_difference)
isochron_results <- verify_synchronicity(isochron_synchro, isochrons, isochron=TRUE, offset=age_offset)
##
## All horizon statistics saved to: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example/SyncER_outputs/isochron_stats.csv
## Warning: You have a high percentage (74.58%) of ages that do not pass the test.
## Please verify that your isochrons are correctly labeled!
##
## === Overall Synchronicity Test Summary ===
## Total comparisons: 59
## Pass: 15 (25.4%) | Fail: 44 (74.6%)
## Lowest score: 7.8% | Highest score: 100.0%
## ==========================================
## Printing comparison PDFs...
For each unique pair of records in which the considered horizons
(here: isochrons) are present, the synchronicity score is calculated and
saved in the *_stats.csv file (synchronicity_score column),
where the * is replaced by the name of the horizons you are testing.
Additionally, an overall synchronicity score per tested horizon is also
calculated. A test summary where the number of fails (the desired
confidence_level cannot be reached for the given
age_difference) and test passes (the desired
confidence_level can be reached for the given
age_difference) is printed in-line.
In theory, also the age difference between both records that is
needed to achieve a synchronicity score of confidence_level
can be calculated (synchronicity precision). However, direct calculation
of the synchronicity precision is no longer an assumption-free test and
the outcome strongly depends on the distribution of the data. It is thus
only calculated in case some quality criteria are met, in which case it
is also given in the *_stats.csv file
(synchronicity_precision column). An alternative way to approximate the
time interval within which the desired confidence_level for
synchronicity can be reached would be to calculate the synchronicity
score for systematically larger age_difference values until
the desired confidence_level is reached.
Step 4: Set age difference of isochrons to zero
If your isochrons show a significant age difference and/or wide age
ranges, you can set this age difference to zero by assigning a fixed age
to the isochron. There are five different options to achieve this:
age, ageofrecord, Bayesian,
mean or mean_fixederror.
age uses a specific age and error for one or more
specific isochron (recommended if independent calibrated age information
is available), and requires the variables age_value,
age_error and age_cc to be defined;
ageofrecord, allowing you to choose one of
the available age estimates (recommended if there are clear indications
that one age model is more accurate) by defining the variable
age_record;
Bayesian, which uses the Bayesian rules
for combinations of probabilities as detailed in Bayes 1763 and
Doran and Hodgson 1975 (recommended if the actual age is unknown but the
age-depth models are assumed to be accurate);
mean, using the mean and standard deviation
of the available age estimates (recommended if the actual age is
unknown, default);
mean_fixederror, using the mean of the available age
estimates but considers an arbitrarily small error that should be
defined using the age_error variable (recommended if the
actual age is unknown and you want to be very strict for synchronicity
testing);
For each of these methods, you can exclude a certain record from the
calculations (e.g., if it has inaccurate age information) by adding the
excluded_records argument to the
synchronize_ages() function.
You can choose a single method (e.g.,
matching_method = "ageofdepth") or combine them when
different isochrons require different methods as in the example below.
The horizons variable should include all horizons that you
want to synchronize. For those that are listed but no
matching_method is defined, meanis used.
If your isochrons are indeed already potentially synchronous within a
relatively narrow time interval and thus passed the test because you,
for example, already used their independent ages as input for your age
models, you can simply fill in those ages here below using the
age matching method and appropriate age_value,
age_error and age_cc values. After running
these code blocks, you can skip step 5 and proceed to step 6 to evaluate
synchronicity for other deposits.
matching_method <- c("isochron1" = "mean_fixederror", "isochron2" = "age", "isochron3" = "Bayesian", "isochron4" = "ageofrecord", "isochron5" = "age") # choose from mean, ageofrecord or age
horizons <- c("isochron1", "isochron2", "isochron3", "isochron4", "isochron5", "isochron6", "isochron7")
age_record <- "core2" # choose the age record you want to use as reference when matching_method "ageofrecord" is used
age_value <- c("isochron2" = 1100, "isochron5" = 4100) # give age that needs to be used in case matching_method "age" is used
age_error <- c("isochron1" = 10, "isochron2" = 20, "isochron5" = 25) # give error on age that needs to be used in case matching_method "age" is used
age_cc <- c("isochron2" = 0, "isochron5" = 0, "isochron1" = 0)
excluded_records <- c() # This can exclude a record entirely (excluded_records = c("core3")) or for certain horizons (excluded_records = list("isochron3" = "core3")).
adjusted_ages <- synchronize_ages(isochron_stats, method = matching_method, horizons = horizons, age_record = age_record, age_value = age_value, age_error = age_error, offset = age_offset, excluded_records = excluded_records, horizon_groups = isochron_groups)
## Using mean age with fixed error for isochron1
## Adjusted age: 103.5 +/- 10.0 cal yrs BP
## Using age of 1100.0 +/- 20.0 (cc = 0) for isochron2
## Using Bayesian MC combined age for isochron3
## Adjusted age: 2107.0 +/- 35.7 cal yrs BP
## Using age of core2 for isochron4
## Adjusted age: 3053.0 +/- 78.7 cal yrs BP
## Using age of 4100.0 +/- 25.0 (cc = 0) for isochron5
## Warning in compute_synchronized_ages(event_stats = event_stats, method = method, :
## WARNING: log-ratios are not normally distributed for horizon 'isochron6' (p=0.0001)!
## The error on this age might not accurately represent the age differences between the records.
## Using mean age for isochron6
## Adjusted age: 5064.9 +/- 135.6 cal yrs BP
## Warning in compute_synchronized_ages(event_stats = event_stats, method = method, :
## WARNING: log-ratios are not normally distributed for horizon 'isochron7' (p=0.0207)!
## The error on this age might not accurately represent the age differences between the records.
## Using mean age for isochron7
## Adjusted age: 6077.2 +/- 122.5 cal yrs BP
## Saved Bayesian plot: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example/SyncER_outputs/isochron3_Bayesian_combination.pdf
Step 5: Create new precise age-depth models
With the synchronized ages for your isochrons, you can create new age
models. For this purpose, new *.csv files are created as
input files for rbacon, placed in folders with the same name as
the original record but with a _synced suffix. As the ages of
the isochron are considered to be precise, the t.a and
t.b parameters in rbacon are adjusted to more closely
approach a normal distribution rather than a t distribution with long
tails for those ages. This ensures that the isochron ages are considered
more strictly (i.e. given a higher weight) compared to the age
information obtained from the samples. After running rbacon
again with the code below, the synchronized age-depth models can also
be found in those folders. Keep in mind that these new age-depth
models have an improved precision, but are not necessarily more
accurate! This means synchronicity will be evaluated relative
to other synchronous deposits. An improvement in accuracy can only be
guaranteed in case you have independent age control for your isochrons.
Additionally, it is important to always keep checking if your age models
still seem reasonable (no weird changes in sedimentation rate, age
reversals etc).
record_data_synced <- age_model_input(record_data, adjusted_ages,
radiocarbon_sample_names = radiocarbon_sample_names, lead_sample_names = lead_sample_names, original_ages = TRUE) #Set original_ages = FALSE if you don't want the age-depth models to also use the original (radiocarbon) ages; you can adjust this per record (e.g. if one record has bad accuracy) by passing c("core1" = FALSE). Records not listed fall back to TRUE.
## C14 data for core1_synced saved to: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example/core1_synced/core1_synced.csv
## C14 data for core2_synced saved to: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example/core2_synced/core2_synced.csv
## C14 data for core3_synced saved to: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example/core3_synced/core3_synced.csv
## C14 data for core4_synced saved to: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example/core4_synced/core4_synced.csv
## C14 data for core5_synced saved to: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example/core5_synced/core5_synced.csv
As in step 2, actually (re)running rbacon/rplum
here is gated by run_age (see step 2) as the
Example dataset already includes completed
_synced runs. If you want to use the age-depth models for
which the original 14C age information is excluded, make sure to change
the record names by running
names(record_data_synced) <- paste0(names(record_data_synced), "_without14").
if (run_age) {
for (record_synced in names(record_data_synced)){
# Adjust this block if you need specific settings for a certain record, and copy it if you need different settings for multiple records. Don't forget to uncomment it to make sure your age-depth models are constructed correctly. Or change to Plum() if you defined lead_sample_names
# if (record_synced=="record_synced"){ ## Replace "record" by the name of the record for which you require specific settings
# Bacon(core = record_adjusted,
# coredir = syncer_wd,
# sep = ',',
# rotate.axes = TRUE,
# thick = 1,
# hiatus = hiatuses[[record]],
# slump = event_depths[[record]],
# ssize = 10000,
# d.max = max_depths[record],
# acc.mean = round(sedrates[record]),
# suggest = FALSE,
# accept.suggestions = TRUE
# )
# } else {
Bacon(core = record_synced,
coredir = syncer_wd,
sep = ',',
rotate.axes = TRUE,
thick = 2,
ssize = 1000,
d.max = max_depths[record],
hiatus = hiatuses[[record]],
acc.mean = round(sedrates[record]),
suggest = FALSE,
accept.suggestions = TRUE,
run = FALSE #set to FALSE in case you already have the age models constructed
)
# }
}
}
After constructing the new age-depth models, extract the ages of the
different event deposits in each synchronized record and export them to
the out_data_ages_synced/ folder.
reload_existing <- TRUE ## FALSE processes the bundled _synced .out files and creates out_data_ages_synced/; TRUE to reuse an existing folder
event_ages_synced <- load_event_ages(record_data = record_data, event_types = event_types, max_depths = max_depths, isochrons = isochrons, test_horizons = test_events, synced = "_synced", reload_existing = reload_existing, thick = 2)
## Data successfully read from: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example/SyncER_outputs/out_data_ages_synced
## Loaded 5 record(s): core1_synced, core2_synced, core3_synced, core4_synced, core5_synced
if (!reload_existing) {
write_age_output_data(event_ages_synced, synced="_synced")}
If you’ve used the mean matching method, you could
iteratively keep updating the isochron age to narrow down the precision,
and make synchronicity evaluation more strict. This can be done by
rerunning steps 3, 4 and 5, using the age-depth models created in step 5
(stored in the event_ages_synced variable) from the last
iteration in the process_event_ages() function.
Step 6: Evaluate synchronicity
The error on the above-defined ages for each isochron has an
implication on what can later be considered a reasonable age difference
to infer synchronicity when evaluating other event deposits. For
example, if you allow an error (1-sigma) of 10 years on an event
deposited 100 years ago, that already covers a maximum 20% age
difference, or even 40% considering a 2-sigma uncertainty. Considering
these are the isochrons, for which (presumably) there was independent
age information that these events indeed occurred synchronously, it is
not reasonable to demand an age_difference of less than 20
or 40% when evaluating potential synchronous deposition of an unknown
deposit. To check which age_difference values would be
reasonable for each isochron (and thus for the unknown deposits in that
same time range), the code block below reports the considered relative
age differences for each isochron considering a user-defined confidence
level on the age interval (sigma_multiplier; 2 for 95.4%
confidence on the age).
thresholds <- compute_isochron_thresholds(adjusted_ages, age_offset=age_offset, sigma_multiplier=2)
print_validation_summary(thresholds)
##
## === Validation Thresholds Summary ===
## Age offset used: 76 years
## Horizon Sigma Conf. % CV % Threshold %
## ------------------------------------------------------------------
## isochron1 2.0 95.4 5.57 22.29
## isochron2 2.0 95.4 1.70 6.80
## isochron3 2.0 95.4 1.63 6.54
## isochron4 2.0 95.4 2.52 10.06
## isochron5 2.0 95.4 0.60 2.39
## isochron6 2.0 95.4 2.64 10.55
## isochron7 2.0 95.4 1.99 7.96
## ==================================================================
You can first double-check if your isochrons now all show an improved
synchronicity score by running the code block below. For a sensible
test, it is recommended to have the age_difference and
confidence_level variables to not be more constraining that
the relative size of the error on the the assigned ages for each
isochron, as calculated in the
{r calculate-thresholds-from-synchronized-data} code block
of step 6. These values were stored in the thresholds
variable, and can thus be passed on to the
verify_synchronicity() function as in the example below.
The results are summarized in the *_stats_synced.csv file,
and a summary of the lowest and highest probabilities for synchronicity
is presented in-line.
isochron_stats_synced <- process_event_ages(event_ages_synced #change to event_ages in case you did not create new synchronized age models
, isochrons, offset=age_offset)
isochron_synchro_synced <- compute_synchronicity_values(isochron_stats_synced, isochrons,confidence_level=thresholds$confidence_levels, age_difference=thresholds$validation_thresholds,
horizon_groups = isochron_groups)
isochron_results <- verify_synchronicity(isochron_synchro_synced, isochrons, isochron=TRUE, synced="_synced" # set this to the suffix of the run you are referring to
, offset=age_offset)
##
## All horizon statistics saved to: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example/SyncER_outputs/isochron_stats_synced.csv
## Warning: You have a high percentage (32.20%) of ages that do not pass the test.
## Please verify that your isochrons are correctly labeled!
##
## === Overall Synchronicity Test Summary ===
## Total comparisons: 59
## Pass: 40 (67.8%) | Fail: 19 (32.2%)
## Lowest score: 72.5% | Highest score: 100.0%
## ==========================================
## Printing comparison PDFs...
Although the number of test passes has significantly increased, still
not all isochrons across the different records show the desired age
precision. This is especially true when the original radiocarbon ages
were also taken into account in the construction of the model, as these
might force the new age-depth model “away” from these new input ages
because the age PDFs of radiocarbon ages are not normal distributions -
unlike the input isochron ages. However, as long as your radiocarbon
ages are accurate, the impact should be minimal, and you can still use
them to construct the synchronized age-depth models. If you are not
satisfied with the results, you can go back to step 4 and change the
parameters that synchronize your age-depth models. An alternative option
(especially recommendable if your age-depth models do not all have a
comparable accuracy) is to create an age-depth model without (some of)
the original input ages. To evaluate synchronicity of a certain horizon
in a meaningful way, you need to have at least one age control (ideally,
isochron or radiocarbon age with a reasonable accuracy) above and below
the considered horizon.
In the example dataset, one of the events is consistently named
“synchro-test” in each record. These are chosen in a way that
they indeed represent events that were input as synchronous in the
synthetic dataset. For records 2 and 5, we deliberately selected an
additional wrong event layer”synchro-test-wrong” in addition to
the correctly-correlated horizon “synchro-test” to allow us to
evaluate how well the methodology can resolve age differences and
present reliable inferences on event synchronicity. The tested horizons
are all positioned between isochron2 and isochron3,
for which synchronicity scores of 67 and 75% are obtained when
considering an age difference of 5%.
As the thresholds for testing should not be more constraining that
the input errors on the nearby isochrons, the least constraining one can
be defined in the block below by listing the isochrons between which the
test horizon is deposited.
positioning_test <- list("synchro-test" = c("isochron2","isochron3"))
thresholds_tests <- sapply(positioning_test, function(horizons) { max(thresholds$validation_thresholds[horizons], na.rm = TRUE) })
test_event_stats <- process_event_ages(event_ages_synced, #change to event_ages in case you did not create new synchronized age models
test_events, offset=age_offset)
## Warning in FUN(X[[i]], ...): No matching event columns found in this record
## Warning in FUN(X[[i]], ...): No matching event columns found in this record
## Warning in FUN(X[[i]], ...): No matching event columns found in this record
## Warning in FUN(X[[i]], ...): No matching event columns found in this record
## Warning in FUN(X[[i]], ...): No matching event columns found in this record
test_synchro <- compute_synchronicity_values(test_event_stats, test_events, confidence_level = confidence_level, age_difference = thresholds_tests, horizon_groups = test_horizon_groups)
synchronicity_test <- verify_synchronicity(test_synchro, test_events, synced="_synced", offset=age_offset)
Using these values in combination with the test results of the input
isochrons, you can report on the probability of synchronicity for
different horizons. In the example, the highest synchronicity scores are
achieved for combinations of the “synchro-test” horizons, which
indeed represents the correct combination. Moreover, a synchronicity
score above 70% is only ever achieved when only
the”synchro-test” horizons are involved, any combination with
“synchro-test-wrong” yields lower synchronicity scores. This
forms another solid argument that”synchro-test-wrong” indeed
represents a false correlation.
Step 7: Iteratively improve age model precision
With the above information on potential synchronicity of certain
deposits, you could consider further improving the age model precision
by using these horizons as additional (secondary) isochrons. While this
allows to evaluate the potential synchronicity of other deposits in more
detail, keep in mind that all ensuing inferences depend heavily
on the assumption that these secondary isochrons can indeed be
considered as synchronously deposited (relative synchronicity
evaluation). If this assumption is false, all of the interpretations
resulting from synchronizing those deposits will be flawed as well. The
below illustrates how the result of synchronicity testing can be
included to further synchronize the different records.
Firstly, the synchronized age of the secondary isochron should be
calculated. If multiple horizons were included in the test for a single
record, the horizons that should be considered non-synchronous should be
added to the non_synchro_horizons variable. Called with
record_data_synced and the new ages (no
update_records), age_model_input() creates a
new _synced_1 generation of folders off the
_synced ones, adding the secondary isochron to each
record’s CSV, and returns record_data_synced_1 (re-keyed to
_synced_1, with the new age baked in).
matching_method <- c("Bayesian")
non_synchro_horizons <- list("core2_synced" = "synchro-test-wrong", "core5_synced" = "synchro-test-wrong") # Horizons for which you tested synchronicity, but they are likely not and so should not be age-matched across records. Tested horizon sets that should be excluded entirely can be listed as "global" = "tested horizon".
adjusted_ages <- synchronize_ages(test_event_stats, method = matching_method, horizons = "synchro-test", nonsynchro_horizons = non_synchro_horizons, offset = age_offset, horizon_groups = test_horizon_groups)
record_data_synced_1 <- age_model_input(record_data_synced, adjusted_ages,
radiocarbon_sample_names = radiocarbon_sample_names, lead_sample_names = lead_sample_names)
## C14 data for core1_synced_1 saved to: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example/core1_synced_1/core1_synced_1.csv
## C14 data for core2_synced_1 saved to: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example/core2_synced_1/core2_synced_1.csv
## C14 data for core3_synced_1 saved to: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example/core3_synced_1/core3_synced_1.csv
## C14 data for core4_synced_1 saved to: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example/core4_synced_1/core4_synced_1.csv
## C14 data for core5_synced_1 saved to: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example/core5_synced_1/core5_synced_1.csv
Additionally, rbacon and rplum have the option to
indicate older than or younger than ages in the input csv files. You
could make use of this function when assigning the synchronized age to
the false correlations tested earlier to ensure the age-depth models
respect this non-synchronicity. This is illustrated below: passing the
record_data_synced_1 just returned together with
update_records = TRUE updates those same
_synced_1 folders in place, adding the non-synchronous
ages, and returns the fully updated
record_data_synced_1.
non_synchro_ages <- assign_nonsynchro_age(adjusted_ages, non_synchro_horizons, test_event_stats, horizon_groups = test_horizon_groups)
record_data_synced_1 <- age_model_input(record_data_synced_1, non_synchro_ages, update_records = TRUE, radiocarbon_sample_names = radiocarbon_sample_names, lead_sample_names = lead_sample_names)
## C14 data for core1_synced_1 saved to: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example/core1_synced_1/core1_synced_1.csv
## C14 data for core2_synced_1 saved to: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example/core2_synced_1/core2_synced_1.csv
## C14 data for core3_synced_1 saved to: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example/core3_synced_1/core3_synced_1.csv
## C14 data for core4_synced_1 saved to: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example/core4_synced_1/core4_synced_1.csv
## C14 data for core5_synced_1 saved to: C:/Users/wilska/AppData/Local/Temp/RtmpApMcew/SyncER_example/core5_synced_1/core5_synced_1.csv
If you’ve assigned the synchronized age to the deposits that were
considered non-synchronous using the above code block, use the positions
of those horizons in your csv files to make use of the
younger.than = c() or older.than = c()
functionalities in rbacon and rplum. As per the
rbacon manual, this might not always work, and an error message
can be thrown - especially if the depths of the two tested horizons are
close to one another. If this is the case, remove the non-synchronized
horizons from you csv files again and proceed with merely the actual
synchronous deposits as input.
if (run_age) {
for (record_synced_1 in names(record_data_synced_1)){
# Adjust this block if you need specific settings for a certain record, and copy it if you need different settings for multiple records. Don't forget to uncomment it to make sure your age-depth models are constructed correctly.
if (record=="core2_synced_1"){ ## Replace "record" by the name of the record for which you require specific settings
Bacon(core = record_synced_1,
coredir = syncer_wd,
sep = ',',
rotate.axes = TRUE,
thick = 2,
ssize = 1000,
d.max = max_depths[record],
slump = event_depths[[record]],
hiatus = hiatuses[[record]],
acc.mean = round(sedrates[record]),
suggest = FALSE,
accept.suggestions = TRUE,
#younger.than = c(5),
run = TRUE #set to FALSE in case you already have the age models constructed
)
} else if (record=="core5_synced_1"){ ## Replace "record" by the name of the record for which you require specific settings
Bacon(core = record_synced_1,
coredir = syncer_wd,
sep = ',',
rotate.axes = TRUE,
thick = 2,
ssize = 1000,
d.max = max_depths[record],
slump = event_depths[[record]],
hiatus = hiatuses[[record]],
acc.mean = round(sedrates[record]),
suggest = FALSE,
accept.suggestions = TRUE,
#older.than = c(9),
run = TRUE #set to FALSE in case you already have the age models constructed
)
} else {
Bacon(core = record_synced_1,
coredir = syncer_wd,
sep = ',',
rotate.axes = TRUE,
thick = 2,
ssize = 1000,
d.max = max_depths[record],
slump = event_depths[[record]],
hiatus = hiatuses[[record]],
acc.mean = round(sedrates[record]),
suggest = FALSE,
accept.suggestions = TRUE,
run = TRUE #set to FALSE in case you already have the age models constructed
)
}
}
}
After running the age models, you can proceed with extracting the age
information as before, and return to step 6 to evaluate synchronicity of
additional deposits by considering the event_ages_synced1
variable calculated below as input for the
process_event_ages() function.
Example:
event_ages_synced_1 <- load_event_ages(record_data =
record_data_synced_1, event_types = event_types, max_depths =
max_depths, isochrons = isochrons, test_horizons = test_events, synced =
“synced_1”,reload_existing = FALSE, thick = 2)
write_age_output_data(event_ages_synced_1, synced = “synced_1”)
You could iteratively keep synchronizing new horizons by re-running
step 6 and this step again, each time passing the latest
record_data_synced_N into age_model_input().
This creates the next numbered generation (_synced_2,
_synced_3, …) and returns the record_data to carry into the
following iteration.