A seminal paper by John Rick some 30 years ago (1987) first introduced the idea of using the frequency of archaeological radiocarbon dates through time as a proxy for highs and lows in human population. The increased availability of large collections of archaeological (especially anthropogenic) radiocarbon dates has dramatically pushed this research agenda forward in recent years. New case studies from across the globe are regularly being published, stimulating the development of new techniques to tackle specific methodological and interpretative issues.
rcarbon (Crema and Bevan 2020) is an R package for the analysis of large collections of radiocarbon dates, with particular emphasis on this “date as data” approach. It offers basic calibration functions as well as a suite of statistical tests for examining aggregated calibrated dates, using the method commonly referred to as summed probability distributions of radiocarbon dates (SPDs).
Stable versions of the rcarbon package can be directly installed from CRAN (using the command install.packages("rcarbon")
), whilst the development version can be installed from the github repository using the following command (the function requires the devtools package):
Note that the development version can be unstable, so for general use we recommend the CRAN version.
Once the installation is completed the package can be loaded using the library()
command:
Single or multiple radiocarbon dates can be calibrated using the calibrate()
function, which uses the probability density approach (see Bronk Ramsey 2008) implemented in most calibration software (e.g. OxCal) as well as in other R packages (especially Bchron. Note that the latter R package also provides agedepth modelling for environmental cores and experimental options for aggregating dates via Gaussian mixtures.
The example below calibrates a sample with a \(^{14}\)C Age of 4200 BP and an error of 30 years using the IntCal20 calibration curve (Reimer et al 2020):
The resulting object of class CalDates
can then be plotted using the basic plot()
function (in this case highlighting the 95% higher posterior density interval):
Multiple dates can be calibrated by supplying a vector of numerical values (as well as other arguments, e.g. different calibration curves), and the summary()
function can be used to retrieve one and the two sigma ranges as well as the median calibrated date:
## DateID MedianBP OneSigma_BP_1 OneSigma_BP_2 OneSigma_BP_3 TwoSigma_BP_1
## 1 D001 6477 6532 to 6520 6500 to 6440 6422 to 6409 6600 to 6589
## 2 D002 5527 5593 to 5573 5528 to 5481 NA to NA 5645 to 5645
## 3 D003 7365 7422 to 7414 7399 to 7328 NA to NA 7426 to 7280
## 4 D004 7997 8019 to 7975 NA to NA NA to NA 8158 to 8147
## TwoSigma_BP_2 TwoSigma_BP_3
## 1 6562 to 6401 NA to NA
## 2 5602 to 5470 NA to NA
## 3 NA to NA NA to NA
## 4 8110 to 8099 8039 to 7939
CalDates
comprising multiple radiocarbon dates can be plotted using the plot()
function specifying the index number, or more conveniently using the multiplot()
function:
With most common machines, the calibrate()
function is capable of calibrating 10,000 dates in less than a minute; the function can also be executed in parallel for improved performance by specifying the number of cores using the argument ncores
.
Individual calibrated dates stored in the CalDates
class objects can be extracted using square brackets (e.g. xx[2]
to extract the second date). The package also provides a subset()
method and a which.CalDates()
function to subset or identify specific dates with a given probability mass within a given interval. The example below extracts all dates from the object xx
with a cumulative probability mass of 0.5 or over between 7000 and 5500 cal BP:
## [1] 1 2
## DateID MedianBP OneSigma_BP_1 OneSigma_BP_2 OneSigma_BP_3 TwoSigma_BP_1
## 1 D001 6477 6532 to 6520 6500 to 6440 6422 to 6409 6600 to 6589
## 2 D002 5527 5593 to 5573 5528 to 5481 NA to NA 5645 to 5645
## TwoSigma_BP_2
## 1 6562 to 6401
## 2 5602 to 5470
Calibration can be done using different calibration curves. The following example is for a marine sample with \(\Delta R = 340\pm20\):
x < calibrate(4000,30,calCurves='marine20',resOffsets=340,resErrors=20)
plot(x,HPD=TRUE,calendar="BCAD") #using BC/AD instead of BP
Users can also supply their own custom calibration curves. The example below uses a mixed marine/terrestrial curve generated using the mixCurves()
function:
#generate 70% terrestrial and 30% marine curve
myCurve < mixCurves('intcal20','marine20',p=0.7,resOffsets=340,resErrors=20)
plot(calibrate(4000,30,calCurves=myCurve))
By default, calibrated probabilities are normalised so the total probability is equal to one, in step with most other radiocarbon calibration software. However, Weninger et al (2015) argue that when dates are aggregated by summation, this normalisation process can generate artificial spikes in the resulting summed probability distributions (SPDs) coinciding with steeper portions of the calibration curve. By specifying normalised=FALSE
in calibrate()
it is possible to obtain unnormalised calibrations. Using normalised or unnormalised calibrations does not have an impact on the shape of each individual dates calibrated probability distribution, but does influence the shape of SPDs, so we suggest at minimum that any case study should explore whether its results differ much when normalised versus unnormalised dates are used.
The function spd()
aggregates (sums) calibrated radiocarbon dates within a defined chronological range. The resulting object can then be displayed using the plot()
function. The example below uses data from the EUREOVOL project database (Manning et al 2016) which can be directly accessed within the package.
data(euroevol)
DK=subset(euroevol,Country=="Denmark") #subset of Danish dates
DK.caldates=calibrate(x=DK$C14Age,errors=DK$C14SD,calCurves='intcal20')
DK.spd = spd(DK.caldates,timeRange=c(8000,4000))
plot(DK.spd)
plot(DK.spd,runm=200,add=TRUE,type="simple",col="darkorange",lwd=1.5,lty=2) #using a rolling average of 200 years for smoothing
It is also possible to limit the plot of the SPD to a particular window of time and/or use a ‘BC/AD’ timescale:
SPDs can be potentially biased if there is strong intersite variability in sample size, for example where one wellresourced research project has sampled one particular site for an unusual number of dates. This might generate misleading peaks in the SPD and to mitigate this effect it is possible to create artificial bins, a local SPD based on samples associated with a particular site and close in time that is divided by the number of dates in the bin or to the average SPD (in case of nonnormalised calibration). Dates are assigned to the same or different bins based on their proximity to one another in (either \(^{14}\)C in time or median calibrated date) using hierarchical clustering with a userdefined cutoff value (using the hclust()
function and the argument h
) and this binning is implemented by the binPrep()
function. The code below illustrates an example using a cutoff value of 100 years:
DK.bins = binPrep(sites=DK$SiteID,ages=DK$C14Age,h=100)
# DK.bins = binPrep(sites=DK$SiteID,ages=DK.caldates,h=100) #using median calibrated date
The resulting object can then be used as an argument for the spd()
function:
The selection of appropriate cutoff values for binning has not been discussed in the literature (Shennan et al 2013 uses a value of 200 years but their method is slightly different). From a practical point of view, a “bin” could represent a “phase” or episode of occupation (at an archaeological site, for example), but clearly this is a problematic definition in the case of a continuous occupation. The binning process should hence be used with caution, and its implications should be explored via a sensitivity analysis. The function binsense()
enables a visual assessment of how different cutoff values can modify the shape of the SPD. The example below explores six different values and show how the highest peak in the SPD changes as a function of h
but the overall dynamics remains essentially the same.
The location (in time) of individual bins can be shown by using the binMed()
and the barCodes()
functions. The former computes the median date from each bin while the latter display them as vertical lines on an existing SPD plot (inspired by a method first used in the CalPal radiocarbon software package).
An alternative way to tackle intersite variation in sampling intensity is to set a maximum sample size per site or bin. This can be achieved by using the thinDates()
function which returns index values based on a variety of criteria. The example below randomly selects one date from each bin and constructs an SPD without binning:
# subset CalDates object based on random thinning
DK.caldates2 = DK.caldates[thinDates(ages=DK$C14Age, errors=DK$C14SD, bins=DK.bins, size=1, method='random')]
# aggregation and visualisation of the SPD
DK.spd.thinned = spd(DK.caldates2,timeRange=c(8000,4000))
## [1] "Extracting and aggregating..."
## [1] "Done."
An alternative approach to visualising the changing frequencies of radiocarbon dates is to create a composite kernel density estimate (CKDE, Brown 2017). The methods consist of randomly sampling calendar dates from each calibrated date and generate a kernel density estimate (KDE) with a userdefined bandwidth. The process is iterated multiple times and the resulting set of KDEs is visualised as an envelope. To generate a CKDE using the rcarbon package we first use the sampleDates()
function. This generates multiple sets of random dates that can then be used to generate the CKDE. If the bin argument is supplied the function generates random dates from each bin rather than each date:
The resulting object becomes the key argument for the ckde()
function, whose output can be displayed via a generic plot()
function:
## Warning in ckde(DK.randates, timeRange = c(8000, 4000), bw = 200): Simulated
## dates were generated from a dates calibrated with the argument `normalised` set
## to TRUE. The composite KDE will be executed with 'normalised' set to TRUE. To
## obtain a nonnormalised composite KDE calibrate setting 'normalised` to FALSE
The rcarbon package also enables bootstrapping (by settingboot=TRUE
in sampleDates()
), and the CKDE can be weighted (by settingnormalised=FALSE
in ckde()
; although the calibrated dates supplied to sampleDates()
should also not be normalised) to emulate an SPD with nonnormalised dates. For further details, please read the help documentation of the two functions.
The shape of empirical SPDs can be affected by a host of possible biases including taphonomic loss, sampling error, and the shape of the calibration curve. One way to approach this problem is to assess SPDs in relation to theoretical expectations and adopt a hypothesistesting framework. rcarbon provides several functions for doing this.
Shennan et al 2013 (Timpson et al 2014 for more detail and methodological refinement) introduced a MonteCarlo simulation approach consisting of a threestage process: 1) fit a growth model to the observed SPD, for example via regression; 2) generate random samples from the fitted model; and 3) uncalibrate the samples. The resulting set of radiocarbon dates can then be calibrated and aggregated in order to generate an expected SPD of the fitted model that takes into account idiosyncrasies of the calibration process. This process can be repeated \(n\) times to generate a distribution of SPDs (which takes into account the effect of sampling error) that can be compared to the observed data. Higher or lower than expected density of observed SPDs for a particular year will indicate local divergence of the observed SPD from the fitted model, and the magnitude and frequency of these deviations can be used to assess the goodnessoffit via a global test. rcarbon implements this routine with the function modelTest()
, which enables testing against exponential, linear, uniform, and userdefined custom models. The script below shows an example with the Danish SPD fitted to an exponential growth model:
nsim = 100
expnull < modelTest(DK.caldates, errors=DK$C14SD, bins=DK.bins, nsim=nsim, timeRange=c(8000,4000), model="exponential",runm=100)
## Warning in modelTest(DK.caldates, errors = DK$C14SD, bins = DK.bins, nsim =
## nsim, : edgeSize reduced
We can extract the global pvalue from the resulting object which can also be plotted.
## [1] 0.02970297
The grey shaded region depicts the critical envelope that encompasses the middle 95% of the simulated SPDs, with red and blue regions highlighting portions of the SPD where positive and negative deviations are detected. Further details can be extracted using the summary()
function:
## 'modelTest()' function summary:
##
## Number of radiocarbon dates: 699
## Number of bins: 443
##
## Statistical Significance computed using 100 simulations.
## Global pvalue: 0.0297.
##
## Signficant positive local deviations at:
## 5631~5442 BP
## 5348~5037 BP
## 4605~4598 BP
##
## Significant negative local deviations at:
## 7901~7822 BP
## 7469~7420 BP
## 6476~6419 BP
## 6311~6247 BP
## 4257~4064 BP
Please note also that rcarbon employs two different methods for generating 14C samples ( uncalsample and calsample), see help documentation and Crema and Bevan 2020 for detailed discussion on the differences between the two.
The modelTest()
function can also be used to test userdefined growth models. The example below compares the observed SPD against a fitted logistic growth model (using the nls()
function). Other applications may include theoretical models that are independent to the observed data (i.e. not fitted to the observed SPD) or based on alternative proxy timeseries (see for example Crema and Kobayashi 2020 for a comparison between SPDs and residential count data).
# Generate a smoothed SPD
DK.spd.smoothed = spd(DK.caldates,timeRange=c(8000,4000),bins=DK.bins,runm=100)
# Start values should be adjusted depending on the observed SPD
logFit < nls(PrDens~SSlogis(calBP, Asym, xmid, scale),data=DK.spd.smoothed$grid,control=nls.control(maxiter=200),start=list(Asym=0.2,xmid=5500,scale=100))
# Generate a data frame containing the fitted values
logFitDens=data.frame(calBP=DK.spd.smoothed$grid$calBP,PrDens=SSlogis(input=DK.spd.smoothed$grid$calBP,Asym=coefficients(logFit)[1],xmid=coefficients(logFit)[2],scal=coefficients(logFit)[3]))
# Use the modelTest function (returning the raw simulation output  see below)
LogNull < modelTest(DK.caldates, errors=DK$C14SD, bins=DK.bins,nsim=nsim,
timeRange=c(8000,4000), model="custom",predgrid=logFitDens, runm=100, raw=TRUE)
## [1] 0.00990099
The modelTest()
function also enables the statistical comparison between the observed and expected growth rates of the fitted model. The rate is based on the comparison of the summed probability observed at each focal year \(t\) against the summed probability at \(t\Delta\), where \(\Delta\) is defined by the argument backsight
in modelTest()
a the range is calculated using the expression defined in the argument changexpr
. The default values for backsight
an changexpr
are 50 years and the expression (t1/t0)^(1/d)  1
where t1
is the summed probability for each year (i.e. \(t\)), t0
is the summed probability of the backsight year (i.e. \(t\Delta\)), and d
is the distance between the two time points (i.e. \(\Delta\)). The output of the growth rates analysis can be assessed by setting the argument type
in the summary()
and in the plot()
functions. For example:
## 'modelTest()' function summary:
##
## Number of radiocarbon dates: 699
## Number of bins: 443
## Backsight size: 50
##
## Statistical Significance computed using 100 simulations.
## Global pvalue (rate of change): 0.89109.
##
## Signficant positive local deviations at:
## 7780~7729 BP
##
## Significant negative local deviations at:
## 4994~4992 BP
The modelTest()
function does not itself indicate whether the observed difference between two particular points in time is significant, as both local and global tests are based on the overall shape of the observed and expected SPDs (or their corresponding rates of change). The p2pTest()
follows a procedure introduced by Edinborough et al (2017) which compares the expected and the observed difference in radiocarbon density between just two userdefined points in time. The example below is based on the Danish subset using a uniform theoretical model.
#Fit a Uniform model (the argumet raw should be set to TRUE for p2pTest())
uninull < modelTest(DK.caldates, errors=DK$C14SD, bins=DK.bins, nsim=nsim, timeRange=c(8000,4000), model="uniform",runm=100, raw=TRUE)
## Warning in modelTest(DK.caldates, errors = DK$C14SD, bins = DK.bins, nsim =
## nsim, : edgeSize reduced
## $p1
## [1] 5120
##
## $p2
## [1] 4920
##
## $pval
## [1] 0.03960396
Note that when the arguments p1
and p2
are not supplied p2pTest()
displays the SPD and enable users to select the two points on the plotted SPD interactively.
SPDs are often compared against each other to evaluate regional variations in population trends (e.g.Timpson et al 2015) or to determine whether the relative proportion of different dated materials changes across time. Collard et al (2010) for instance demonstrates that the relative frequencies of different kinds of archaeological site have varied over time in Britain, whilst Stevens and Fuller (2012) argue that the proportion of wild versus domesticated crops fluctuated during the Neolithic (see also Bevan et al. 2017). The permTest()
function provides a permutation test (Crema et al. 2016) for comparing two or more SPDs, returning both global and local pvalues using similar procedures to modelTest()
.
The example below reproduces the analyses of Eastern Mediterranean dates by Roberts et al (2018):
data(emedyd) # load data
cal.emedyd = calibrate(emedyd$CRA,emedyd$Error,normalised=FALSE)
bins.emedyd = binPrep(ages = emedyd$CRA,sites = emedyd$SiteName,h=50)
perm.emedyd=permTest(x=cal.emedyd,marks=emedyd$Region,timeRange=c(16000,9000),bins=bins.emedyd,nsim=nsim,runm=50)
summary(perm.emedyd)
par(mfrow=c(3,1))
plot(perm.emedyd,focalm = 1,main="Southern Levant")
plot(perm.emedyd,focalm = 2,main="Northern Levant/Upper Mesopotamia")
plot(perm.emedyd,focalm = 3,main="SouthCentral Anatolia")
As for modelTest()
, it is also possible to compare the growth rates rather than the SPD:
par(mfrow=c(3,1))
plot(perm.emedyd,focalm = 1,main="Southern Levant", type='roc')
plot(perm.emedyd,focalm = 2,main="Northern Levant/Upper Mesopotamia", type='roc')
plot(perm.emedyd,focalm = 3,main="SouthCentral Anatolia", type='roc')
It is also possible to visually compare SPDs by creating stackCalSPD
class objects using the stackspd()
function. These can be plotted in a vatiety of ways to the ease the visual comparison of SPDs across different groups:
## [1] "Computing SPD for group 1 out of 3"
## [1] "Extracting and aggregating..."
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The core principles of the “dates as data” approach can be extended over space by interpreting regions of high or low concentrations of dates across multiple temporal slices as evidence of higher or lower population densities. As in the case of ordinary SPDs, the visual inspection of such SPD maps can be problematic, as peaks and troughs in the density of radiocarbon dates can potentially be the result of calibration processes or sampling error. The problem is further exacerbated when the spatial window of analysis becomes larger, as differences in sampling design and intensity can hinder the observed pattern. The rcarbon package offers two techniques that take into account these issues when assessing spatiotemporal patterns in the density of radiocarbon dates.
The stkde()
function enables the computation of spatiotemporal kernel density estimates (KDE) of radiocarbon dates for a particular focal
year based on userdefined spatial (sbw
) and temporal (tbw
) bandwidths. In practice, this is achieved by placing a Gaussian kernel around a chosen year and then the degree of overlap between this kernel and the probability distribution of each date is evaluated. The weights are then used to compute weighted spatial kernel density estimates using the density()
function of the spatstat package (Baddeley et al 2015). The function returns the KDE for each focal year, the rate of change (based on the userdefined expression provided in the argument changepr
) between focal years and some earlier backsight
year, as well as relative risk surfaces (Kelsall and Diggle 1995). The latter consist of dividing the KDE of a particular year with the KDE of all periods to take into account overall differences in sampling intensity (see Chaput et al 2015 and Bevan et 2017 for examples).
The example below examines a subset of the EUROEVOL radiocarbon dates from England and Wales between 6500 to 5000 BP, using a temporal bandwidth of 50 years, a spatial bandwidth of 40km, and backsight
of 200 years (i.e. change is computed between focal year and focal year minus 200 years):
## Load Data
data(ewdates)
data(ewowin)
## Calibrate and bin
x < calibrate(x=ewdates$C14Age, errors=ewdates$C14SD, normalised=FALSE,verbose=FALSE)
bins1 < binPrep(sites=ewdates$SiteID, ages=ewdates$C14Age, h=50)
## Create centennial timeslices (see help doc for further argument details)
stkde1 < stkde(x=x, coords=ewdates[,c("Eastings", "Northings")], win=ewowin, sbw=40000, cellres=2000, focalyears=seq(6500, 5000, 100), tbw=50, bins=bins1, backsight=200, outdir=tempdir(), amount=1, verbose=FALSE)
The actual KDE timeslices are stored by the stkde()
function in a userdefined directory defined by the argument outdir
(the example above uses a temporary directory via the tempdir()
function). The plot()
function can then be used to display a variety of maps. The example below shows the focal intensity at year 5900 cal BP, the overall intensity across all years, the relative risk surface ( focal proportion ), and the rate of change between 5700 and 5900 cal BP ( focal change ).
When geographic study areas are very large, it becomes inappropriate to assume that there is complete spatial homogeneity in the demographic trajectories of different subregions in the study area. At the same time, evaluating such regional divergences is difficult because any increase in spatial scale of a study usually entails also an increase in the heterogeneity of research design and in the overall sampling intensity. rcarbon enables an exploration of spatial heterogeneity in the SPDs that is robust to differences in sampling intensity and provides a permutationbased statistical significance framework (for details of the method see Crema et al. 2017).
In order to carry out a spatial analysis of aggregate radiocarbon dates, we need calibrated dates, bins, and a SpatialPoints
class object containing the site locations:
euroevol=subset(euroevol,C14Age<=7200&C14Age>=4200)
eurodates < calibrate(euroevol$C14Age,euroevol$C14SD,normalised=FALSE,verbose=FALSE)
eurobins < binPrep(sites=euroevol$SiteID,ages=euroevol$C14Age,h=200)
# Create a data.frame of site locations extracting spatial coordinates
sites < unique(data.frame(id=euroevol$SiteID,lat=euroevol$Latitude,lon=euroevol$Longitude))
rownames(sites) < sites$id
sites < sites[,1]
# Convert to a SpatialPoints class object:
sp::coordinates(sites) < c("lon","lat")
sp::proj4string(sites) < sp::CRS("+proj=longlat +datum=WGS84")
We then need to generate a distance matrix which we will use to define a spatial weighting scheme using the spweights()
function. The example below uses a Gaussian decay function (the default) with a bandwidth size of 100 km:
#Compute distance matrix
d < sp::spDists(sites,sites,longlat=TRUE)
#Compute spatial weights
w < spweights(d,h=100)
The core function sptest()
compares the observed and the expected geometric growth rates rather than the raw SPD. Thus we need to define the breakpoints of our chronological blocks and the overall time range of our analysis. In this case, we examine a sequence of blocks, each 500 years long.
breaks < seq(8000,5000,500) #500 year blocks
timeRange < c(8000,5000) #set the timerange of analysis in calBP, older date first
The function spd2rc()
can be used to calculate and visualise the growth rates for specific sequence of blocks.
eurospd = spd(x = eurodates,bins=eurobins,timeRange = timeRange)
plot(spd2rc(eurospd,breaks = breaks))
In this case, the panregional trend shows a positive but declining growth rate through time, except for the transition 65006000 to 60005500 cal BP when the rate increases slightly.
In order examine whether these dynamics is observed consistently across the study region we conduct a permutation test with the sptest()
function:
The output of the function has its own plot()
method which provides various ways to display the outcome. The function plots only the point locations, so it is often convenient to load a separate base map. The example below uses the rworldmap package:
library(rworldmap)
base < getMap(resolution="low") #extract basemap
#extract bounding coordinates of the site distribution
xrange < bbox(sites)[1,]
yrange < bbox(sites)[2,]
The plot function requires the definition of an index
value (a numerical integer representing the \(i\)th transition (thus index=1
means first transition, in this case the transition from the time block 80007500 to the time block 75007000 cal BP), and an option
argument, which indicates what needs to be plotted (either the results of the statistical tests or the local estimates of geometric growth rates). The script below examines the transition when the declining growth rate exhibits a short reversion (i.e. 65006000 to 60005500 cal BP).
## Spatial Permutation Test for Transition 4
par(mar=c(1,1,4,1),mfrow=c(1,2))
plot(base,col="antiquewhite3", border="antiquewhite3",xlim=xrange, ylim=yrange,main="6.56 to 65.5 kBP \n (Test Results)")
## Warning in wkt(obj): CRS object has no comment
plot(eurospatial,index=4, option="test", add=TRUE, legend=TRUE, legSize=0.7, location="topleft")
## Geometric Growth Rate for Transition 4
plot(base,col="antiquewhite3", border="antiquewhite3", xlim=xrange, ylim=yrange, main="6.56 to 65.5 kBP \n (Growth Rate)")
## Warning in wkt(obj): CRS object has no comment
The two figures show significant spatial heterogeneity in growth rates. Southern Ireland, Britain, and the Baltic area all exhibit positive growth, while most of France is associated with negative deviations from the panregional model. Given the large number of site locations and consequent inflation of type I error, sptest()
also calculates a false discovery rate (qvalues) using the p.adjust()
function with method="fdr"
. A qvalue of 0.05 implies that 5% of the results that have a qvalue below 0.05 are false positives.
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