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"),
...
)
A numeric vector of dates. If named,
the names must match the row names of object.
Further arguments to be passed to internal methods.
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 non-negative integer giving the number of bootstrap
replications.
date_event returns a '>DateEvent object.
predict_event, predict_accumulation, bootstrap_event
and jackknife_event return a data.frame.
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:
The jackknife mean (event date).
The jackknife estimate of bias.
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:
Minimum value.
Mean value (event date).
Maximum value.
Sample quantile to 0.05 probability.
Sample quantile to 0.95 probability.
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")).
Bellanger, L. & Husi, P. (2013). Mesurer et mod<U+00E9>liser le temps inscrit dans la mati<U+00E8>re <U+00E0> partir d'une source mat<U+00E9>rielle : la c<U+00E9>ramique m<U+00E9>di<U+00E9>vale. In Mesure et Histoire M<U+00E9>di<U+00E9>vale. Histoire ancienne et m<U+00E9>di<U+00E9>vale. 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. 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 arch<U+00E9>ologiques. Revue de Statistique Appliqu<U+00E9>e, 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. 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()
# NOT RUN {
## Event and accumulation dates (Bellanger et al.)
## See the vignette:
# }
# NOT RUN {
utils::vignette("dating")
# }
Run the code above in your browser using DataLab