date_event fit a date event model.

predict_event and predict_accumulation estimates the event and accumulation dates of an assemblage.

date_event(object, dates, ...)

predict_event(object, data, ...)

predict_accumulation(object, data, ...)

bootstrap_event(object, ...)

jackknife_event(object, ...)

# S4 method for CountMatrix,numeric
date_event(object, dates, cutoff = 90, level = 0.95, ...)

# S4 method for DateEvent,missing
predict_event(object, margin = 1, level = 0.95)

# S4 method for DateEvent,CountMatrix
predict_event(object, data, margin = 1, level = 0.95)

# S4 method for DateEvent,missing
predict_accumulation(object, level = 0.95)

# S4 method for DateEvent,CountMatrix
predict_accumulation(object, data, level = 0.95)

# S4 method for DateEvent
jackknife_event(
object,
level = 0.95,
progress = getOption("tabula.progress"),
...
)

# S4 method for DateEvent
bootstrap_event(
object,
level = 0.95,
probs = c(0.05, 0.95),
n = 1000,
progress = getOption("tabula.progress"),
...
)

## Arguments

object A CountMatrix or a DateEvent object. A numeric vector of dates. If named, the names must match the row names of object. Further arguments to be passed to internal methods. A CountMatrix object for which to predict event and accumulation dates. An integer giving the cumulative percentage of variance used to select CA factorial components for linear model fitting (see details). All compounds with a cumulative percentage of variance of less than the cutoff value will be retained. A length-one numeric vector giving the confidence level. A numeric vector giving the subscripts which the prediction will be applied over: 1 indicates rows, 2 indicates columns. A logical scalar: should a progress bar be displayed? A numeric vector of probabilities with values in $$[0,1]$$ (see quantile). A non-negative integer giving the number of bootstrap replications.

## Value

date_event returns a DateEvent object.

predict_event, predict_accumulation, bootstrap_event and jackknife_event return a data.frame.

## Details

This is an implementation of the chronological modeling method proposed by Bellanger and Husi (2012, 2013).

Event and accumulation dates are density estimates of the occupation and duration of an archaeological site (Bellanger and Husi 2012, 2013). The event date is an estimation of the terminus post-quem of an archaeological assemblage. The accumulation date represents the "chronological profile" of the assemblage. According to Bellanger and Husi (2012), accumulation date can be interpreted "at best [...] as a formation process reflecting the duration or succession of events on the scale of archaeological time, and at worst, as imprecise dating due to contamination of the context by residual or intrusive material." In other words, accumulation dates estimate occurrence of archaeological events and rhythms of the long term.

This method relies on strong archaeological and statistical assumptions. Use it only if you know what you are doing (see references below and the vignette: utils::vignette("dating", package = "tabula")).

## Note

Bellanger et al. did not publish the data supporting their demonstration: no replication of their results is possible and this implementation must be considered experimental. date_event may be subject to major changes in a future release.

## Date Model

If jackknife_event is used, one type/fabric is removed at a time and all statistics are recalculated. In this way, one can assess whether certain type/fabric has a substantial influence on the date estimate. A three columns data.frame is returned, giving the results of the resampling procedure (jackknifing fabrics) for each assemblage (in rows) with the following columns:

mean

The jackknife mean (event date).

bias

The jackknife estimate of bias.

error

The standard error of predicted means.

If bootstrap_event is used, a large number of new bootstrap assemblages is created, with the same sample size, by resampling each of the original assemblage with replacement. Then, examination of the bootstrap statistics makes it possible to pinpoint assemblages that require further investigation.

A five columns data.frame is returned, giving the bootstrap distribution statistics for each replicated assemblage (in rows) with the following columns:

min

Minimum value.

mean

Mean value (event date).

max

Maximum value.

Q5

Sample quantile to 0.05 probability.

Q95

Sample quantile to 0.95 probability.

## References

Bellanger, L. & Husi, P. (2013). Mesurer et modéliser le temps inscrit dans la matière à partir d'une source matérielle : la céramique médiévale. In Mesure et Histoire Médiévale. Histoire ancienne et médiévale. Paris: Publication de la Sorbonne, p. 119-134.

Bellanger, L. & Husi, P. (2012). Statistical Tool for Dating and Interpreting Archaeological Contexts Using Pottery. Journal of Archaeological Science, 39(4), 777-790. doi: 10.1016/j.jas.2011.06.031 .

Bellanger, L., Tomassone, R. & Husi, P. (2008). A Statistical Approach for Dating Archaeological Contexts. Journal of Data Science, 6, 135-154.

Bellanger, L., Husi, P. & Tomassone, R. (2006). Une approche statistique pour la datation de contextes archéologiques. Revue de Statistique Appliquée, 54(2), 65-81.

Bellanger, L., Husi, P. & Tomassone, R. (2006). Statistical Aspects of Pottery Quantification for the Dating of Some Archaeological Contexts. Archaeometry, 48(1), 169-183. doi: 10.1111/j.1475-4754.2006.00249.x .

Poblome, J. & Groenen, P. J. F. (2003). Constrained Correspondence Analysis for Seriation of Sagalassos Tablewares. In Doerr, M. & Apostolis, S. (eds.), The Digital Heritage of Archaeology. Athens: Hellenic Ministry of Culture.

Other dating: date_mcd()

N. Frerebeau

## Examples

## Event and accumulation dates (Bellanger et al.)
## See the vignette:
if (FALSE) {
utils::vignette("dating")
}