event()
fit a date event model.predict_event()
andpredict_accumulation()
estimates the event and accumulation dates of an assemblage.
Usage
event(object, dates, ...)
predict_event(object, data, ...)
predict_accumulation(object, data, ...)
# S4 method for data.frame,numeric
event(object, dates, cutoff = 90, level = 0.95, ...)
# S4 method for matrix,numeric
event(object, dates, cutoff = 90, level = 0.95, ...)
# S4 method for EventDate,missing
predict_event(object, margin = 1, level = 0.95)
# S4 method for EventDate,matrix
predict_event(object, data, margin = 1, level = 0.95)
# S4 method for EventDate,missing
predict_accumulation(object)
# S4 method for EventDate,matrix
predict_accumulation(object, data)
# S4 method for EventDate
jackknife(object, level = 0.95, progress = getOption("kairos.progress"), ...)
# S4 method for EventDate
bootstrap(
object,
level = 0.95,
probs = c(0.05, 0.95),
n = 1000,
progress = getOption("kairos.progress"),
...
)
Arguments
- object
An \(m \times p\)
numeric
matrix
or adata.frame
of count data (absolute frequencies).- dates
A
numeric
vector of dates expressed in CE years (BCE years must be given as negative numbers). If named, the names must match the row names ofobject
.- ...
Further arguments to be passed to internal methods.
- data
A
numeric
matrix
or adata.frame
of count data (absolute frequencies)for which to predict event and accumulation dates.- cutoff
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 thecutoff
value will be retained.- level
A length-one
numeric
vector giving the confidence level.- margin
A
numeric
vector giving the subscripts which the prediction will be applied over:1
indicates rows,2
indicates columns.- progress
A
logical
scalar: should a progress bar be displayed?- probs
A
numeric
vector of probabilities with values in \([0,1]\) (seestats::quantile()
). IfNULL
, quantiles are not computed.- n
A non-negative
integer
giving the number of bootstrap replications.
Value
event()
returns an EventDate object.predict_event()
returns adata.frame
.predict_accumulation()
returns a MeanDate object.bootstrap()
andjackknife()
return adata.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
(see vignette("kairos")
).
Note
All results are rounded to zero decimal places (sub-annual precision does
not make sense in most situations). You can change this behavior with
options(kairos.precision = x)
(for x
decimal places).
Bellanger et al. did not publish the data supporting their demonstration: no replication of their results is possible. This implementation must be considered experimental and subject to major changes in a future release.
Resampling
If jackknife()
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()
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.
See also
Other dating methods:
mcd()
Examples
if (FALSE) {
utils::vignette("kairos")
}