Running rbacon
The package comes with two pre-loaded datasets, MSB2K and RLGH3.
Start by running the default core, MSB2K. Note R’s comments about where
the files are going to be placed, as we will need this information
later. Press ‘y’ and/or Enter to accept any suggestions.
Bacon will calibrate any C-14 dates, do the MCMC run, calculate age
ranges, produce graphs and provide information about the confidence
ranges. The output graph, below, contains a number of panels containing
important information (so please don’t cut them out for your
publications):
First, at the top left we can see the ‘fit’ or ‘energy’ of all
MCMC (Markov Chain Monte Carlo) iterations of the run. If the iterations
seem clustered with sections of lower and of higher energy, then the run
probably didn’t go well and you might want to try running your core with
more iterations (parameter ssize).
The second panel at the top shows the accumulation rate (in
yr/cm, so actually sedimentation time). The prior (green) is a gamma
distribution with two parameters: acc.mean and acc.shape. The values of
gamma distributions are always >0, ensuring positive accumulation
rates. Larger values of shape will result in more peaked prior
distributions. The grey shape shows the posterior distribution.
Then we have the prior (green) and posterior (grey) for the
memory, or how much accumulation rates can change from one depth to the
other. This is a beta distribution, which allows values between 0 and 1.
A very low memory prior would allow for a high variability of
accumulation rate, while a very high memory prior would result in a
nearly straight age-depth model. Also shown is the section thickness of
the piece-wise linear model, and the resulting amounts of
sections.
If you have set a hiatus, then the rightmost top-panel will show
the prior (green) and posterior (grey) distributions for this. The prior
for this is a uniform distribution (but always >0; the maximum is set
at 10,000 years by default but this can be changed).
Finally there is the main panel, which shows the distribution of
the dates in blue (calibrated C-14 dates) or greenish (cal BP dates).
More precise dates will peak higher (on the depth scale) than less
precise, wider dates. The age-depth model is shown in grey-scale, where
darker hues indicate more certain areas. The red dashed curve shows the
mean model, and the dashed grey curves the 95% confidence
intervals.
Bacon output plot
Besides the graph, Bacon also produces files containing age estimates
(95% ranges and the mean) for each depth (by default every cm from the
top to the bottom core depth). This file can be found in the core’s
folder (see ‘6 Folders and Files’).
To see all options and settings, ask for help:
and to see all options and settings for the age-model plot, type:
Choosing priors
Accumulation
rate
Another core that comes with rbacon is RLGH3. This core has a lower
dating density than MSB2K and has a section where there appear to be
outlying dates (or in other words, some of the dates don’t seem to agree
with other nearby dates). Since initial checks indicate a slower
accumulation rate (or rather, sedimentation time) than rbacon’s default
of 20 yr/cm, rbacon will ask if the prior should be changed to 50 yr/cm.
For now, respond with n or Enter, so that the
default, non-adapted settings will be used.
age-depth model of RLGH3 using the default
settings
Note that with the default settings, the age-model bypasses some of
the younger dates between 130 and 60 cm core depth, since the older
dates within that core section seem to fit better with the model and the
dates further downcore and higher up the core. On top of this, the model
bypasses the bottom date, because if the age-model were to go through
this date, it would require large-scale changes in sedimentation times
which seem to be prohibited by the default settings for the prior
information. So let’s run the model again, this time accepting the
suggested value of 50 yr/cm for the accumulation rate
acc.mean (type y and press
Enter):
age-depth model of RLGH3 using suggested
settings
With the adapted prior for the accumulation rate, now the lowermost
date is taken into account. Now, what if someone you trusted
told you that they had some very strong information from other nearby
lakes that sedimentation times are very likely to be 50 yr/cm? Then we
can set the prior to be much more restrictive/stronger than the default
- we let the green curve peak higher so that the accumulation rates are
‘forced’ to values around 50 yr/cm:
Bacon('RLGH3', acc.mean=50, acc.shape=100)
age-depth model of RLGH3 using a much stronger
accumulation rate prior
Bacon did what it was told to do. The posterior accumulation rates
fit very well with the prior’s mean of 50 yr/cm. The age-depth model
itself however is very straight and isn’t very realistic, and most of
the dates don’t fit with the model. Note also the top-left panel which
shows the MCMC run; the first thousand or so iterations are still
sub-optimal and should really be removed using the scissors
function. In all, not a good age-depth model, and it’s probably a good
idea to talk to your expert again, or find another one with more
realistic prior information.
When comparing the prior (green) and posterior (grey) distributions
of the accumulation rate and the memory, it can be helpful to consider
that the Bayesian approach aims to combine prior information with new
data in order to ‘learn’ about the process, in other words to update its
information about the parameters involved. The default priors were
chosen to be sufficiently broad to allow the data to inform us about the
accumulation rate and its variability.
Memory
Besides the prior for accumulation rates, the other prior is that of
the memory, and this defines how much accumulation rate can change from
one core depth to another. To take an example, in the previous run the
memory (rightmost top panel) is all the way at 1 or 100%. In other
words, the accumulation rate at one depth is highly correlated with
those at neighbouring depths, i.e. it’s a nearly straight line. If you
have a site where you have information about how constant the
accumulation rate must have been (e.g., a riverine site could have had
very episodical and varying accumulation), this can thus be included
using the memory prior.
Hiatuses and
slumps
If your site has hiatuses (gaps in accumulation), these can be set by
providing their depths. By default, the maximum length of hiatuses is
10,000 years, but this can be adapted, for example:
Bacon("RLGH3", acc.mean=50, hiatus.depths=125, hiatus.max=1000)
age-depth model of RLGH3 assuming a hiatus at
125 cm depth
If your core had episodes of instantaneous sedimentation (e.g.,
visible tephra layers), they can be modelled too:
Bacon("RLGH3", acc.mean=50, slump=c(180, 120, 40, 30))
age-depth model of RLGH3 assuming slumps at
180-120 and 40-30 cm depth
Section
thickness
The model underlying Bacon is made up of piece-wise linear sections,
and for each section the accumulation rate is modelled as constrained by
the priors for accumulation rate and memory. The sections, are 5 cm
thick by default (thick=5). However, for very short cores
this would result in a very elbowy model. For very long cores, the
default section thickness could result in too many parameters and the
model losing itself. Therefore, for very short or long cores, different
values for will be suggested for thick.
Sometimes for difficult cores it can be a good idea to first run it
with fewer, thicker sections in order to find useful combinations of
priors and settings, and then running it again with more, thinner
sections to obtain an age-depth model that looks ‘smooth enough’. Each
time, also check that the MCMC run looks fine and stable, and please
check and show all panels of the age-model plot.
Folders and files
It is important to understand where rbacon goes to look for files and
will place new files. The first time rbacon is running in a
new working directory, it will look for an ‘umbrella’ folder called
Bacon_runs (or Cores), and if it doesn’t find
this, it will ask if it may make one. Either accept the suggestion, or
provide an alternative folder by specifying coredir:
mydir <- tempdir()
Bacon(coredir=mydir)
Within this umbrella folder are folders for each individual core. For
example, if you’ve run the default core, there will be a folder
MSB2K, and similarly for RLGH3. Within each
core folder there will be the file with the dates, starting with the
core name and ending with the .csv extension, e.g.,
MSB2K.csv. So, if your core is named
MyLakeCore, then Bacon will look for a folder called
MyLakeCore, and then within that folder looks for a file
MyLakeCore.csv. Take care with capitalisation, and it’s
also best not to use spaces in the names.
Your file should contain headers as below, and the fields should be
separated by commas. In a spreadsheet program such as MS-Excel or
Libreoffice’s Calc, you can save your file as .csv. It’s often a good
idea to then open your .csv file in a plain-text editor such as Wordpad
or Notepad, to check that everything looks clean (e.g., no lines filled
with just commas, or lots of quotation marks).
What it looks like in a spreadsheet program:
| UBA-28881 |
2200 |
20 |
5 |
1 |
| UBA-28882 |
2400 |
20 |
10 |
1 |
| UBA-28883 |
3550 |
30 |
20 |
1 |
| UBA-28884 |
4200 |
35 |
25 |
1 |
And in a plain-text editor (with spaces added for enhanced
readability):
lab ID, Age, Error, Depth, cc
UBA-28881, 2200, 20, 5, 1
UBA-28882, 2400, 20, 10, 1
UBA-28883, 3550, 30, 20, 1
UBA-28884,
4200, 35, 25, 1
Some users have reported problems with writing access to some of
their folders, owing to permission limitations set by the computer’s
administrator. Perhaps you might have access to an external disk drive,
e.g., D:\. Then run Bacon as
Bacon("MyCore", coredir="D:\"). Then Bacon will look for a
folder MyCore in D:\.
If you have dates with age offsets, such as marine dates from a
region with a known delta.R and delta.STD, then this can be specified,
either as option (e.g., Bacon(delta.R=130, delta.STD=30)),
or, recommended, as extra columns in your core’s csv file. Add them as
columns 6 and 7 (in this example, the second date is terrestrial and has
no assumed age offset):
| UBA-28881 |
2200 |
20 |
5 |
2 |
130 |
30 |
| UBA-34567 |
1850 |
20 |
12 |
1 |
0 |
0 |
| UBA-28882 |
2400 |
20 |
10 |
2 |
130 |
30 |
| UBA-28883 |
3550 |
30 |
20 |
2 |
130 |
30 |
| UBA-28884 |
4200 |
35 |
25 |
2 |
130 |
30 |
By default, Bacon uses the t distribution to model the dates,
rather than the more usual normal distribution (default:
Bacon(normal=FALSE)). With the default parameters
t.a=3 and t.b=4, this distribution looks very
much like the normal distribution, but it has wider tails. This makes
for a very robust model, because it handles scatter and outliers very
well. In many cases, Bacon will be able to find its way through most
dates while bypassing outlying dates.
In some cases, some dates could be more reliable than others (e.g.,
some would consist of very reliable charcoal particles, while others
would be based on bulk sediment). Then such dates can be given t values
which resemble the normal distribution (e.g., t.a=33,
t.b=34), either as option in the Bacon command or within
the .csv file:
| UBA-28881 |
2200 |
20 |
5 |
2 |
130 |
30 |
3 |
4 |
| UBA-34567 |
1850 |
20 |
12 |
1 |
0 |
0 |
33 |
34 |
| UBA-28882 |
2400 |
20 |
10 |
2 |
130 |
30 |
3 |
4 |
| UBA-28883 |
3550 |
30 |
20 |
2 |
130 |
30 |
3 |
4 |
| UBA-28884 |
4200 |
35 |
25 |
2 |
130 |
30 |
3 |
4 |
If you want to provide t.a and t.b values in your .csv file, they
should always be in columns 8 and 9 (and similarly, cc should be in
column 5, delta.R in column 6 and delta.STD in column 7). Just provide
0s for delta.R and delta.STD if they don’t need to be adapted.
User interaction
By default, Bacon will tell where it will place files and suggest
values where appropriate. You can also tell Bacon to stop suggesting
alternative values, or instead accept all suggestions and run the cores
without user interaction:
Bacon(suggest=FALSE)
Bacon(accept.suggestions=TRUE)
This can be used to run all cores in a directory while you make
yourself some breakfast:
allcores <- list.files("Bacon_runs")
for(i in allcores)
try(Bacon(i, accept.suggestions=TRUE))
Postrun analysis
By default, Bacon will perform millions of MCMC iterations for each
age-model run, although only a fraction of these will be stored. In most
cases the remaining MCMC iterations will be well mixed (the upper left
panel of the fit of the iterations shows no strange features such as
sudden systematic drops or rises). However if the iterations seem not
well mixed, or if too few remain (say less than a few hundred), then you
could check the Gelman and Rubin Reduction Factor. Too high differences
(high Factors) between runs indicate poor MCMC mixing. Robust MCMC
mixing is indicated by a Gelman and Rubin Reduction factor below the
1.05 safety threshold.
For example, try the default core, running it five times with a very
small sample size of ssize=100:
Baconvergence("MSB2K", thick=5, runs=5, ssize=100, coredir=tempfile())
Did 5 Bacon runs.
Gelman and Rubin Reduction Factor 1.10078680880009 (smaller and closer to 1 is better).
Probably not a robust MCMC run! Too much difference between runs, above the 1.05 threshold. Increase sample size?
Greyscale plots
Once a robust, reliable and realistic age-depth model has been
produced, the fun starts. Greyscale plots for example can be used to
show not just one age-depth curve but the entire MCMC run output. If
pollen or other ‘proxies’ have been analysed across a range of depths of
your core, then these proxies can be plotted on the time-scale as
grey-scale ‘ghosts’ where less certain sections are plotted in lighter
grey than more certain sections. Bacon looks for a file in the core’s
folder, starting with the core’s name and ending in
_proxies.csv, e.g.,
Bacon_runs/MSB2K/MSB2K_proxies.csv. This file should have
columns separated by commas, with the first column being the depth,
followed by columns for the proxies. The first row should contain the
names of the columns. To produce a proxy ghost of the seventh proxy of
MSB2K:
proxy.ghost graph of the 7th proxy in core
MSB2K
Ghost graphs can also be produced for the accumulation rate
throughout the core (accrate.depth.ghost) or over time
(accrate.age.ghost):
accrate.depth.ghost()
accrate.age.ghost()
ghost-graph of the accumulation rate of core
MSB2K against depth
ghost-graph of the accumulation rate of core
MSB2K over time
Other analyses
R provides a very versatile environment to query the age-model
output. To get the age estimate of any single core depth:
## | | | 0%
## mean (red): 4848.6 cal yr BP, median (green): 4841.4 cal yr BP
## 95% range (blue): 4783.2 to 4944.1 cal yr BP
You can also store the iterations of the age estimates of that depth
in a new variable and then query it:
a.d20 <- Bacon.Age.d(20)
summary(a.d20)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4622 4825 4841 4849 4864 4999
Or calculate how much time has passed between 30 and 20 cm depth:
a.d30 <- Bacon.Age.d(30)
a.d20 <- Bacon.Age.d(20)
summary(a.d30-a.d20)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 187.0 400.4 440.5 441.2 486.2 658.6
Accumulation rates at specific depths or ages can also be
investigated:
acc.d20 <- accrate.depth(20)
summary(acc.d20)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.02228 8.54964 17.07930 20.76143 29.67122 72.90970
acc.a4500 <- accrate.age(4500)
summary(acc.a4500)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.2394 15.2002 23.8249 24.9931 33.3742 55.4283 2932
To automate the above, and write a file with accumulation rate
summaries to your core’s folder:
accrate.depth.summary(20)
## 2.5% 16% 84% 97.5% 50% mean
## 2.011100 5.478438 35.675704 61.192453 17.079300 20.761432
accrate.age.summary(4500)
## mean 2.5% 16% 84% 97.5% 50%
## 24.993136 6.720897 12.902912 38.195700 49.199648 23.824900
## writing the accumulation rate summary to /private/var/folders/w_/ktch6mh953z13lmhs4_9bj_80000gn/T/Rtmpc3yPAm/MSB2K/MSB2K_21_accrates.txt
Frequently Asked
Questions
What does Bacon stand
for?
Bayesian accumulation model. It also stands for a breakfast item
which can be cooked floppy/thick or crunchy/thin, much like the prior
settings for Bacon.
Moreover, Francis Bacon (1561–1626) once said that “if we begin with
certainties, we shall end in doubts; but if we begin with doubts, and
are patient in them, we shall end in certainties”. This quote is quite
appropriate for Bayesian age-models, which aim to express and robustly
quantify chronological uncertainties (and while it takes a bit of
patience waiting for them to finish a run, they do a better job at this
than classical approaches).
I don’t like
Bacon
Try tofu(), which does exactly the same but without the
meat.
How do I cite
Bacon?
Please cite Blaauw and Christen 2011, as well as the rbacon
version you are using (check this with sessionInfo()), any
calibration curve(s) used and also any non-default settings.
It is also a good idea to use the latest version of rbacon and to
periodically check if new versions have come out. Either run
update.packages(), install.packages('rbacon'),
or check the current version number at CRAN. Please regularly
check for and install updates to R itself.
Can you help me?
After reading the FAQ, you can contact Maarten Blaauw maarten.blaauw@qub.ac.uk for help with using
rbacon, or Andres Christen jac@cimat.mx for more detailed statistical/Bayesian
questions. Please provide any necessary .csv files and the
exact list of R commands and output.
I’ve found a bug or
have an idea for a new feature
Please contact Maarten Blaauw maarten.blaauw@qub.ac.uk for rbacon-specific
bugs/features, and/or Andres Christen jac@cimat.mx if your question contains much
statistical detail.
Where do I put my
files?
By default, Bacon looks, within the Bacon_runs folder
(sometimes called Cores), for a folder with the same name
as your core. So, if your core is named MyLakeCore, then
Bacon will look within the Bacon_runs folder for another
folder called MyLakeCore, and then within that folder looks
for a file MyLakeCore.csv. Take care with capitalisation,
and it’s also best not to use spaces in the names.
Your file should contain headers as below, and the fields should be
separated by commas. In a spreadsheet program such as MS-Excel or
Libreoffice’s Calc, you can save your file as .csv. It’s often a good
idea to then open your .csv file in a plain-text editor such as Wordpad
or Notepad, to check that everything looks clean (e.g., no lines filled
with just commas, or lots of quotation marks).
What it looks like in a spreadsheet program:
| UBA-28881 |
2200 |
20 |
5 |
1 |
| UBA-28882 |
2400 |
20 |
10 |
1 |
| UBA-28883 |
3550 |
30 |
20 |
1 |
| UBA-28884 |
4200 |
35 |
25 |
1 |
And in a plain-text editor (with spaces added for enhanced
readability):
lab ID, Age, Error, Depth, cc
UBA-28881, 2200, 20, 5, 1
UBA-28882, 2400, 20, 10, 1
UBA-28883, 3550, 30, 20, 1
UBA-28884,
4200, 35, 25, 1
How can I set a
different calibration curve?
By default, rbacon uses the IntCal20 Northern Hemisphere calibration
curve, or cc=1. This can be set to
the Marine20 calibration curve (cc=2),
the Southern Hemisphere SHCal20 curve (cc=3), or even a
custom-built one (cc=4). The cc option can be
provided within the Bacon command (e.g.,
Bacon("MyCore", cc=3)), but for reasons of transparency and
consistency we recommend instead to add cc as a fifth
column to your core’s .csv file:
| UBA-28881 |
2200 |
20 |
5 |
3 |
| UBA-28882 |
2400 |
20 |
10 |
3 |
| UBA-28883 |
3550 |
30 |
20 |
3 |
| UBA-28884 |
4200 |
35 |
25 |
3 |
Note that Bacon requires the raw, uncalibrated radiocarbon
dates as input, and that these dates are calibrated during the modelling
process. Thus, Bacon works with the calibrated dates.
How do I include
non-14C dates?
Here again cc is your friend. Dates that are already on
the cal BP scale, such as independently calendar-dated tephras, pollen
events or the known surface age of your core should get
cc=0. Please translate any AD dates into cal BP
(cal BP = 1950 - AD). Radiocarbon dates that should be
calibrated with IntCal20 Northern Hemisphere calibration curve receive a
cc=1, those with Marine20 cc=2, those with
SHCal20 curve cc=3, and those with a custom-built one
cc=4:
| surface |
-65 |
10 |
0 |
0 |
| UBA-28881 |
2200 |
20 |
5 |
3 |
| UBA-28882 |
2400 |
20 |
10 |
3 |
| UBA-28883 |
3550 |
30 |
20 |
3 |
| UBA-28884 |
4200 |
35 |
25 |
3 |
For Pb-210 data, please use rplum rather than
inputting CRS model output as cal BP dates into Bacon.
Note that Bacon expects >0 errors for all its dates.
Can Bacon calculate
age estimates for specific depths?
By default, Bacon calculates age estimates for each cm from the upper
to the lowest dated depth. This can be changed by specifying a different
value for d.by. The default depth units are
depth.unit="cm" and this can also be adapted. You can also
provide a list of depths, e.g.,
Bacon("MyCore", depths=1:50), or put these in a file
MyCore_depths.csv in the core’s folder and then run as
Bacon("MyCore", depths.file=TRUE).
You can also request the age estimate of any core depth after the
run, for example for 23.45cm:
Bacon.hist(23.45)
depth23.45 <- Bacon.Age.d(23.45)
hist(depth23.45)
mean(depth23.45)
Can Bacon take into
account a hiatus or a slump?
Yes. If your core’s stratigraphy indicates a hiatus at say 30 cm core
depth, put this as Bacon(hiatus.depths=30). With multiple
hiatuses, concatenate the values as, e.g.,
Bacon(hiatus.depths=c(30,52)). Going across a hiatus or
boundary, Bacon will lose its memory of the accumulation rate.
If you expect a boundary instead of a hiatus, so just an abrupt
change in sedimentation regime without an accompanying gap in time, then
use boundary, as in Bacon(boundary=30) or
Bacon(boundary=c(30,52)).
If you found events of abrupt sedimentation such as visible tephra
layers, these can be modelled as well by providing their top and bottom
depths, e.g. Bacon(slump=c(20,30, 50,55)) for slumps at
55-50 and 30-20 cm depth.
How can I reload a
previous run?
First load the old run (making sure you re-enter any non-default
prior settings as options within the Bacon command), telling Bacon not
to run it again, then produce a graph to load all the data into R’s
memory. After this, you should be able to proceed as before:
Bacon("MyCore", run=FALSE)
agedepth()
# or if you've set the accumulation rate prior to something different than the default:
Bacon("MyCore", run=FALSE, acc.mean=c(20,5))
agedepth()
What value should I
use for thick?
In most cases, Bacon should be able to find an appropriate value for
thick. The default is 5 cm, but if your core is very short or very long,
an alternative value for thick is suggested. Too thick sections will
look very ‘elbowy’, and too thin sections could result in too many
parameters and a ‘lost’ model. It is always recommended to try several
values for settings, in order to ensure your results are robust and not
overly sensitive to minor changes in the settings. Different values for
thick can be set as follows:
Bacon("MyCore", thick=1)
Bacon("MyCore", 1)
Why does my
age-model look like a trumpet?
Perhaps the model got lost as too many parameters had to be
estimated. Does the model follow some of the dated depths, but then it
diverges and runs away from the rest of the dates? Try running it with
fewer sections (larger value of thick).
Should I adapt the
priors to exactly match my core’s accumulation rate?
No. In most cases, the default or suggested values for acc.mean
should work fine. The default shape parameter acc.shape=1.5
is set such that a large range of accumulation rates is allowed and the
data will allow Bacon to update our information about the most likely
accumulation rate values. The same holds for memory’s strength parameter
mem.strength=10. It is thus quite allright for the prior
distributions to be much wider than the posterior distributions - then
you have learned more about the values of the parameters that make up
the age-depth model of your site.
If however you have additional information about a site’s
accumulation history, this can be included by setting ‘stronger’, more
informative priors. For example, a site might be known to have
accumulated without major swings in accumulation rates, and then the
memory prior can be set to be close to 1 (e.g.,
mem.strength=50, mem.mean=0.9). On the other hand, sites
which are assumed to have experienced wide fluctuations in
sedimentations could be modelled by setting the memory prior to very low
values (mem.strength=50, mem.mean=0.1).
The idea of prior information is just that; it is information you had
about the site before you started looking at the new data.
Bacon will then combine the prior information with the data to update
our information.
Can I set different
accumulation rates for different parts of my core?
Yes, by defining a boundary or hiatus at the depths where
accumulation rates change, and giving distinct accumulation rate priors
for each section. For example, if the stratigraphy of your core changes
from lake to a marsh at 80 cm depth, and the prior information for marsh
accumulation rates is acc.mean=10 and for the lake
acc.mean=50, then you’d model this as:
Bacon("MyCore", boundary=80, acc.mean=c(10,50))
Going across a boundary or hiatus, Bacon will lose its memory so the
accumulation rates above the boundary/hiatus will not be informed by
accumulation rates below it.
Should I use
Bayesian age-models only for cores with many dates?
Bayesian age-models can deal with an impressive range of dating
density and quality, and work very well also for cores dated
with only few dates. In fact, with few dates, the model choice becomes
even more important, so a Bayesian age-model would be preferable also
for low-resolution dated cores.
What are those blue
blobs and what are they doing on the depth scale?
Those are the age distributions of the dated depths. They lie on the
calendar axis, and their probabilities for each year are expressed on
the depth scale (imagine an invisible additional axis for each
individual date). Each date has the same plotted area by default, so
very precise dates will peak much higher (on the depth axis) than less
precise dates.
I want different
colours
Play around with the following options within the
agedepth function: C14.col,
C14.border, cal.col, cal.border.
These set the colours for the distributions and their borders for the
(calibrated) C14 dates and cal BP dates, respectively. If you want to
set an individual colour for each date, you can also use the option
dates.col, e.g.:
my_fancy_colours <- sample(colours(), nrow(info$dets))
agedepth(height=50, dates.col=my_fancy_colours)
What indicates a
good age-depth model?
First of all, check that the MCMC run (top-left panel) looks stable,
like white noise with no major structure where iterations seem ‘stuck’.
Ensure that there is no burn-in remaining. If the MCMC looks bad, try a
longer run by specifying a different value for ssize
(default 4000). You can always run the scissors or
thinner commands to trim the MCMC run to something nice and
manageable after the run.
Second, check that the posteriors (grey distributions) for the
accumulation rate and memory (and if inferred, the hiatus) either
overlap with the priors (green curves; then not much new has been
learned), or whether the posterior has learned from marrying the priors
with the data.
Finally, check that the age-depth model itself looks reasonable given
the information you have about the site. Is the fit with the dates OK,
do any bends make sense, do any outlying dates make sense, does the
greyscale uncertainty estimate look OK? Here some degree of user
expertise is required.