Archaeological Synchronism.

eratosthenes: Archaeological Synchronism

The R package eratosthenes aims to provide a coherent foundation for archaeological chronology-building by incorporating, computationally, all relevant sources of information on uncertain archaeological or historical dates. Archaeological dates are often subject to relational conditions (via seriation or stratigraphic relationships) and absolute constraints (such as radiocarbon dates, datable artifacts, or other known historical events, as termini post or ante quos), which prompt the use of a joint conditional probability density to convey those relationships. The date of any one event can then be marginalized from that full, joint conditional distribution, which is achieved using a two-stage Gibbs sampler to draw estimates uniformly between potential earliest and latest bounds. Ancillary functions include checking for discrepancies in sequences of events and constraining optimal seriations to known sequences.

While software exists for calibrating and conditioning radiocarbon dates upon relative constraints, such as BCal (Buck, Christen, and James 1999) and OxCal (Bronk Ramsey 2009), the aim of eratosthenes is to extend the application of probability theory more generally to dating all archaeological phenomena, especially the production dates of artifact types. The method of sampling employed in eratosthenes involves a two-step process, which entails first performing iterative routines of Gibbs sampling to determine the initial value for the main sampler, which uses consistent batch means (CBM) and Monte Carlo standard errors (MCSE) to determine convergence (Flegal, Haran, and Jones 2008). Furthermore, eratosthenes provides tools for analyzing the impact of events on each other with the conditional structure stipulated by the investigator, by implementing a jackknife-style estimator of squared displacement (how much the date of one event shifts when another is omitted, squared). Rcpp is required for faster Gibbs sampling.

The package is named after Eratosthenes of Cyrene, author of the Chronographiai.

Installation

To obtain the current development version of eratosthenes from GitHub, install the package in the R command line with devtools:

library(devtools)
install_github("scollinselliott/eratosthenes", dependencies = TRUE, build_vignettes = TRUE) 

Usage

The following comments are intended as a general introduction. See vignettes for more information on package functionality.

The basic objects of interest in eratosthenes are:

• sequences of relative events, typically stratigraphic deposits, but also isolated contexts such as may be part of a frequency or contextual seriation
• finds, elements which belong to those events, typically artifacts
• absolute constraints, as either termini post or ante quos, expressed as samples from a probability density

Information related to these three items must be formatted in objects of a list class, as follows.

Sequences

Relative sequences should run in order from left (earliest) to right (latest). All sequences should consist of vectors, contained in a list. In the following example, the object contexts contains three sequences of events.

x <- c("A", "B", "C", "D", "E", "F", "G", "H", "I", "J")
y <- c("B", "D", "G", "H", "K")
z <- c("F", "K", "L", "M")
contexts <- list(x, y, z)

See also the section Evaluating Sequences below.

Finds

Finds should be formatted as a list of lists, each of which contains the entries of the following:

• id : a unique identification number or code
• assoc : an element in the sequences list to which that find or element pertains
• type : optional – one or more types, attributes, features, or aspects that pertain to that find (NULL if none)

In the following example, the artifacts object contains six artifacts which pertain to elements of the sequences contained in contexts above”

f1 <- list(id = "find01", assoc = "D", type = c("type1", "form1"))
f2 <- list(id = "find02", assoc = "E", type = c("type1", "form2"))
f3 <- list(id = "find03", assoc = "G", type = c("type1", "form1"))
f4 <- list(id = "find04", assoc = "H", type = c("type2", "form1"))
f5 <- list(id = "find05", assoc = "I", type = "type2")
f6 <- list(id = "find06", assoc = "H", type = NULL)
artifacts <- list(f1, f2, f3, f4, f5, f6)

Absolute Constraints

Constraints should be given as two separate lists, one for termini post quos and the other for termini ante quos. These take the same form as the finds object, as a list of lists, with the same headings, but include one additional heading of samples which contains the absolute dates pertinent to that t.p.q. or t.a.q.

coin1 <- list(id = "coin1", assoc = "B", type = NULL, samples = runif(100, -320, -300))
coin2 <- list(id = "coin2", assoc = "G", type = NULL, samples = runif(100, 37, 41))
destr <- list(id = "destr", assoc = "J", type = NULL, samples = 79)

tpq_info <- list(coin1, coin2)
taq_info <- list(destr)

Absolute dates can take any form:

• Single dates, e.g., 79 for 79 CE.
• Samples between two potential dates for a date range, e.g., -91:-88, seq(-91, -88, length = 10^5), or runif(10^5, -91, -88) for 91-88 BCE.
• Samples from a bespoke density, e.g., from a calibrated radiocarbon date. eratosthenes does not provide functionality for calibrating dates, which can be accomplished using preexisting software or directly from a calibration curve. The R package Bchron (Haslett and Parnell 2008) provides functions for calibrating dates. As a brief example, given an uncalibrated date and its standard deviation, a crude sample of calibrated dates can be drawn from the IntCal20 curve data, available from IntCal here (Reimer et al. 2020), using the following script:

intcal20 <- read.csv("../path/to/intcal20.14c")

# 14c date mean and st.dev.
mu <- 2040  
sigma <- 30

# samples of 14c dates
uncalib <- round(rnorm(10^5, mu, sigma))

calib <- c()

for (i in 1:length(uncalib)) {
  x <- intcal20$CAL.BP[ intcal20$X14C.age == uncalib[i] ] 
  #g <- intcal20$Sigma[ intcal20$X14C.age == uncalib[i] ]

  if (length(x) > 0) {
    for (j in 1:length(x)) {
      calib <- c(calib, x[j])  
    }
  }
}

# samples of cal BC date
calBC <- 1950 - calib
hist(calBC, breaks = 100)

Estimating Dates

The core approach of eratosthenes is a Gibbs sampler, a common Markov Chain Monte Carlo (MCMC) technique used for dating archaeological events, above all radiocarbon dates (Geman and Geman 1984; Buck, Cavanagh, and Litton 1996; Bronk Ramsey 2009). Gibbs sampling however can take a number of different forms, and so it is worthwhile to describe explicitly how it is conducted in eratosthenes. The precise method is as follows:

• To initialize, the earliest possible t.p.q. and latest possible t.a.q dates are selected.
• Relative events are put into a single sequence for the purpose of sampling.
• To select the initial date for each relative event, a sample is drawn uniformly at random between its upper and lower constraints (absolute and relative).
  • For each initial date, a subroutine of Gibbs sampling is performed in order to avoid catastrophic collapse of dates due to floating point errors (e.g., if one has a high number of events compressed into a brief span of time).
• After all dates are initialized, the main Gibbs sampler is performed for a specified maximum number of samples, which will stop automatically if convergence in distribution has been achieved.
  • Given that dates have already been initialized via Gibbs subroutines, the need to discard initial samples due to burn-in is obviated (or reduced).
  • Convergence is determined using consistent batch means (CBM), which divides the samples into batches. For all events (i.e., variates), the Monte Carlo standard errors (MCSE) of their batch means are computed. If the mean MCSE falls below a specified criterion (by default 0.5, to determine the date of an event +/- 1 year), the main Gibbs sampler will stop. See Jones et al. (2006) and Flegal, Haran, and Jones (2008) for details.
  • Given that this is the mean MCSE of all events, certain events will have higher or lower MCSE, and so each event’s MCSE should be reported.

There are two functions in eratosthenes for estimating dates:

• gibbs_ad() is the primary function, which will yield samples for dates of deposition, production, and any absolute constraints themselves (that is, the density of that extrinsic date as impacted by all other events in the joint distribution).
• gibbs_ad_use() is the secondary function, for estimating the date of use of a find given its production and depositional date.

See the section Evaluating Displacement below for tools on assessing the effective influence of events upon each other within the joint conditional density.

Dates of Production and Deposition

The function gibbs_ad() takes as inputs the following objects:

• sequences : A list of relative sequences of contexts or events.
• finds : A list of any elements which belong to a context or event, which may be assigned a given type.
• max_samples : The maximum number of samples to run, which will stop the main sampling routine even if convergence has not been achieved (default is 10^5).
• size : How many samples to take between each check for convergence (default is 10^3).
• mcse_crit : The criterion of the mean MCSE at which to stop the sampler (default is 0.5)
• tpq and taq: Separate lists that indicate any elements that provide extrinsic (i.e., absolute) chronological information, as termini post and ante quos.
• alpha_ and omega_: lowest and highest bounds within which to sample.
• trim: whether to remove contexts from the output that are before or after user-provided t.p.q. and t.a.q. (i.e., those which depend on alpha_ and omega_).
• rule: the rule for determining the earliest date of production of an artifact type. Initial threshold boundaries are first established between the earliest depositional context containing an artifact of that type and the next earliest context which lacks it. Then, the following rules will sample a date accordingly:
  • naive: samples are drawn between the initial threshold sample and the depositional date of that artifact
  • earliest: samples are drawn within the initial threshold boundaries

For example, to sample from the sequences, finds, and constraints given above, the following inputs are entered into the gibbs_ad() function:

result <- gibbs_ad(contexts, finds = artifacts, tpq = tpq_info, taq = taq_info)

The output is a list object of class marginals containing the following objects:

• deposition : a list of the marginal densities of the date of the final deposition of contexts.
• externals : a list of the marginal densities of date of any terminus post quem or terminus ante quem, as affected by depositional variates in the joint conditional distribution.
• production : a list of the marginal densities of the production date of a given type or class of artifact, given the rule stipulated in the input.
• mcse : a vector of the MCSE of all events.

Information on the marginals object can be accessed with print() and summary(). Density plots and density histograms of one more events can be produced using plot() and hist() respectively (see documentation for details).

Dates of Use

Determining the use date proceeds along the same method of Gibbs sampling discussed above, using consistent batch means to determine convergence. To compute a density of the use date of an artifact or artifact type, an object of class marginals is necessary (i.e., the estimation of the production and depositional dates must first be performed). Then, the use_dates() function will return a density conditional on the production and depositional dates.

Only one type at a time can be estimated with use_dates(). A type can be defined on the basis of:

• One or more id in the finds list.
• One or more type in the finds list.

That is, one can pool together multiple finds as a type on the basis of their id, even if they were not so explicitly given a type in the finds object. Similarly, one can pool together more than one type of artifact, e.g., if one is dealing with multiple subtypes and one wants to evaluate them as a single type (e.g., pooling the labels of “Late Greco-Italic amphora”, “MGS V amphora”, “MGS VI amphora” into a single type).

The gibbs_ad_use() function, takes the following inputs, similar to gibbs_ad(), but with a field for either id or type (only one or the other field must be used):

• gibbs : A list object of class marginals, as computed via gibbs_ad().
• finds : Either the list object of finds originally used as input to produce gibbs, or a data.frame of two columns, the first column listing the context and the second the incidence of the id or type in that context.
• id : A vector of the id of one or more specific finds whose use date is to be estimated. The values of id must match those in the list of finds. If type is used, id is ignored.
• type : A vector of one or more types to estimate a use density for. Must contain a value if id is left as NULL.
• max_samples, size, mcse_crit : The same information used for determining the maximum length of the Gibbs sampler and when convergence has been achieved.

Using the result object above, the densities of the use dates of the following types is computed using the use_dates() function as follows:

# use dates by specifying ids
gibbs_ad_use(result, artifacts, id = c("find04", "find05"))

# use dates by speciifying types
gibbs_ad_use(result, artifacts, type = "type1")

Adjusting the values of max_samples and mcse_crit is recommended to reduce computational time.

The result is an object of the class use_marginals, which contains information on the density of the date of use as well as the MCSE of the type specified, in the same fashion as the result of the gibbs_ad() function.

Graphics

Base R graphics are provided by eratosthenes to examine traceplots of the results of gibbs_ad(), as well as produce density histograms of the results of gibbs_ad() and gibbs_ad_use(). For gibbs_ad(), histograms may contain up to 12 distinct events. For gibbs_ad_use(), the production, use, and deposition of the stipulated artifact type are shown.

Evaluating Sequences

Managing and evaluating the validity of relative sequences consists of checking multiple partial sequences against one another. Not all relative sequences are of the same informational validity, and not all sequences will contain the same elements. An investigator may seek to constrain one sequence against another, i.e., keeping elements of sequence as close as possible to one another while reordering only some of the elements.

Some functions related to relative sequences:

• seq_check() sees whether partial sequences agree in their relative ordering of elements.
• seq_adj() provides the means to coerce an “input” sequence to a discrepant “target” sequence which contains fewer elements. E.g., if one has obtained an optimal seriation of contexts (of both single, unrelated deposits and stratigraphic deposits) as determined by the presence/absence of find-types, which conflicts with a sequence obtained from a stratigraphic sequence whose physical relationships are certain, this function will reorder the optimal seriation, fitting any single deposits missing from the stratigraphic sequence accordingly.

The package eratosthenes does not have functionality to produce seriations or ordinations, as packages seriation, vegan, and lakhesis can perform this task already.

Evaluating Displacement

As real-world joint conditional densities will comprise hundreds of events or more, it is easy for an investigator to loose track of which relative/absolute events are determinative or influential upon others, in terms of the estimation of their date. eratosthenes assesses such influence within the conditional structure via the estimation of “displacement.” That is, given the omission of an event $j$ (either a depositional event or an absolute constraint) from the set of all events, how much does the estimation of the date of another event change?

The squared displacement $\delta^2(i,j)$ of a target event $i$ caused by the omission of $j$ is computed as follows. Let $x_i$ be the estimated marginalized Monte Carlo mean date using all events within the full joint conditional, and then let $\tilde{x}_i^{(-j)}$ be the “jackknife” estimated date, when event $j$ has been omitted from all sequences and absolute constraints. Squared displacement of $j$ upon $i$ is then:

$$ \delta^2(i,j) = (\tilde{x}_i^{(-j)} - x_i)^2 $$

If squared displacement is high, then the omission of $j$ has greatly shifted the date of $i$. If squared displacement is low, then the omission of $j$ has not altered the date of $i$ much. Squared displacement is measured in continuous time, whichever scale the investigator is using (typically years).

Conversely, one can estimate the effective influence of an event $j$ upon all others by taking the mean squared displacement (MSD). This involves taking the mean of the squared displacements of all other events when $j$ is omitted. Where $\Theta$ represents the set of all relative and absolute events, the MSD is defined as

$$ \text{MSD}(j) = \frac{1}{n-1} \sum_{i \in \Theta, i \neq j} \delta^2 (i,j) $$

The squared displacement and MSD are computed in eratosthenes after running the gibbs_ad() function, as follows. Note that squared displacement may be computed for any event $i$ that represents a relative or absolute constraint, as well as an artifact production date, while $j$ can only be a relative event or absolute constraint (it would make no sense to omit an artifact production date, since these are conditional upon relative/absolute dates to begin with). Similarly, MSD can only be computed for relative/absolute events.

Objects in the example below are provided from the section Usage above. As these routines are fairly intensive, computational time can be reduced by lowering the values of max_samples and/or raising mcse_crit.

# run gibbs_ad() first
result <- gibbs_ad(contexts, finds = artifacts, tpq = tpq_info, taq = taq_info)

# squared displacement is estimated for a target event ("j" above) and all other events

# squared displacement for depositional context "E"
sq_disp(result, target = "E", sequences = contexts, 
        max_samples = 20000, mcse_crit = 2, tpq = tpq_info, taq = taq_info)

# squared displacement for production of artifact type "type1"
sq_disp(result, target = "type1", sequences = contexts, finds = artifacts,
        max_samples = 20000, mcse_crit = 2, tpq = tpq_info, taq = taq_info)
        
# mean squared displacement (MSD) is estimated for all relative and absolute dates
result_msd <- msd(result, contexts, finds = artifacts,
                  mcse_crit = 1, tpq = tpq_info, taq = taq_info)

Bibliography