hasData <- requireNamespace("ArchaeoPhases.dataset", quietly = TRUE) if (!hasData) { knitr::opts_chunk$set(eval = FALSE) msg <- paste("Note: Examples in this vignette require that the", "`ArchaeoPhases.dataset` package be installed. The system", "currently running this vignette does not have that package", "installed, so code examples will not be evaluated.") msg <- paste(strwrap(msg), collapse="\n") message(msg) } knitr::opts_chunk$set(comment = "") options(width = 120, max.print = 5) library(ArchaeoPhases) library(ArchaeoPhases.dataset)

The tempo plot has been introduced by Thomas S. Dye (Dye, T.S. (2016) Long-term rhythms in the development of Hawaiian social stratification. Journal of Archaeological Science, 71, 1--9). See Philippe and Vibet 2017 for more statistical details.

The tempo plot is one way to measure change over time: it estimates the cumulative occurrence of archaeological events in a Bayesian calibration. The tempo plot yields a graphic where the slope of the plot directly reflects the pace of change: a period of rapid change yields a steep slope and a period of slow change yields a gentle slope. When there is no change, the plot is horizontal. When change is instantaneous, the plot is vertical.

The code is the following (Warning : be patient. The execution time depends on the number of dates included.)

data("KADatesChronoModel") tempo_plot(KADatesChronoModel, c(2:10), level = 0.95)

From these graphs, we can see that the highest part of the sampled activity is dated between -45 000 to -35 000 but two dates are younger, at about -32 000 and -28 000.

The activity plot displays the derivative of the Bayes estimate of the Tempo plot. It is an other way to see changes over time.

The code is the following (Warning : be patient. The execution time depends on the number of dates included.)

tempo_activity_plot(KADatesChronoModel, c(2:10))

The Occurrence plot calculates the calendar date t corresponding to the smallest date such that the number of events observed before t is equal to k, for k =[(1, 16)]. The Occurrence plot draws the credible intervals or the highest posterior density (HPD) region of those dates associated to a desired level of confidence.

data("KADatesChronoModel") OccurrencePlot(KADatesChronoModel, c(2:17), level = 0.95, newWindow= FALSE)

For a description of the statiscal aspects of the functions implemented in ArchaeoPhases version 1.0 : Anne Philippe, Marie-Anne Vibet. (2017). Analysis of Archaeological Phases using the CRAN Package 'ArchaeoPhases'. HAL, hal-01347895, version 3.

For a use of the tempo plot defined by Dye : Dye, T.S. (2016). Long-term rhythms in the development of Hawaiian social stratification. Journal of Archaeological Science, 71, 1--9

For more details on the diagnostic of Markov chain : Robert and Casella (2009). Introducing Monte Carlo Methods with R. Springer Science & Business Media.

For more details on the Ksar Akil site : Bosch, M. et al. (2015) New chronology for Ksar Akil (Lebanon) supports Levantine route of modern human dispersal into Europe. Proceedings of the National Academy of Sciences, 112, 7683--6.

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