Package 'kairos'

Aoristic Analysis

Description

Computes the aoristic sum.

Usage

aoristic(x, y, ...)

## S4 method for signature 'numeric,numeric'
aoristic(
  x,
  y,
  step = 1,
  start = min(x),
  end = max(y),
  calendar = CE(),
  weight = TRUE,
  groups = NULL
)

## S4 method for signature 'ANY,missing'
aoristic(
  x,
  step = 1,
  start = NULL,
  end = NULL,
  calendar = CE(),
  weight = TRUE,
  groups = NULL
)

Arguments

x, y	A numeric vector giving the lower and upper boundaries of the time intervals, respectively. If y is missing, an attempt is made to interpret x in a suitable way (see grDevices::xy.coords()).
...	Currently not used.
step	A length-one integer vector giving the step size, i.e. the width of each time step in the time series (defaults to $1$, i.e. annual level).
start	A length-one numeric vector giving the beginning of the time window.
end	A length-one numeric vector giving the end of the time window.
calendar	An aion::TimeScale object specifying the calendar of x and y (see calendar()). Defaults to Gregorian Common Era.
weight	A logical scalar: should the aoristic sum be weighted by the length of periods (default). If FALSE the aoristic sum is the number of elements within a time block.
groups	A factor vector in the sense that as.factor(groups) defines the grouping. If x is a list (or a data.frame), groups can be a length-one vector giving the index of the grouping component (column) of x.

Details

Aoristic analysis is used to determine the probability of contemporaneity of archaeological sites or assemblages. The aoristic analysis distributes the probability of an event uniformly over each temporal fraction of the period considered. The aoristic sum is then the distribution of the total number of events to be assumed within this period.

Muller and Hinz (2018) pointed out that the overlapping of temporal intervals related to period categorization and dating accuracy is likely to bias the analysis. They proposed a weighting method to overcome this problem. This method is not implemented here (for the moment), see the aoristAAR package.

Value

An AoristicSum object.

Author(s)

N. Frerebeau

References

Crema, E. R. (2012). Modelling Temporal Uncertainty in Archaeological Analysis. Journal of Archaeological Method and Theory, 19(3): 440-61. doi:10.1007/s10816-011-9122-3.

Johnson, I. (2004). Aoristic Analysis: Seeds of a New Approach to Mapping Archaeological Distributions through Time. In Ausserer, K. F., Börner, W., Goriany, M. & Karlhuber-Vöckl, L. (ed.), Enter the Past - The E-Way into the Four Dimensions of Cultural Heritage, Oxford: Archaeopress, p. 448-52. BAR International Series 1227. doi:10.15496/publikation-2085

Müller-Scheeßel, N. & Hinz, M. (2018). Aoristic Research in R: Correcting Temporal Categorizations in Archaeology. Presented at the Human History and Digital Future (CAA 2018), Tubingen, March 21. https://www.youtube.com/watch?v=bUBukex30QI.

Palmisano, A., Bevan, A. & Shennan, S. (2017). Comparing Archaeological Proxies for Long-Term Population Patterns: An Example from Central Italy. Journal of Archaeological Science, 87: 59-72. doi:10.1016/j.jas.2017.10.001.

Ratcliffe, J. H. (2000). Aoristic Analysis: The Spatial Interpretation of Unspecific Temporal Events. International Journal of Geographical Information Science, 14(7): 669-79. doi:10.1080/136588100424963.

Ratcliffe, J. H. (2002). Aoristic Signatures and the Spatio-Temporal Analysis of High Volume Crime Patterns. Journal of Quantitative Criminology, 18(1): 23-43. doi:10.1023/A:1013240828824.

See Also

Other aoristic analysis: plot_aoristic, roc()

Examples

## Data from Husi 2022
data("loire", package = "folio")

## Get time range
loire_range <- loire[, c("lower", "upper")]

## Calculate aoristic sum (normal)
aorist_raw <- aoristic(loire_range, step = 50, weight = FALSE)
plot(aorist_raw, col = "grey")

## Calculate aoristic sum (weights)
aorist_weighted <- aoristic(loire_range, step = 50, weight = TRUE)
plot(aorist_weighted, col = "grey")

## Calculate aoristic sum (weights) by group
aorist_groups <- aoristic(loire_range, step = 50, weight = TRUE,
                          groups = loire$area)
plot(aorist_groups, flip = TRUE, col = "grey")
image(aorist_groups)

## Rate of change
roc_weighted <- roc(aorist_weighted, n = 30)
plot(roc_weighted)

## Rate of change by group
roc_groups <- roc(aorist_groups, n = 30)
plot(roc_groups, flip = TRUE)

---

Chronological Apportioning

Description

Chronological Apportioning

Usage

apportion(object, ...)

## S4 method for signature 'data.frame'
apportion(
  object,
  s0,
  s1,
  t0,
  t1,
  from = min(s0),
  to = max(s1),
  step = 25,
  method = c("uniform", "truncated"),
  z = 2,
  progress = getOption("kairos.progress")
)

## S4 method for signature 'matrix'
apportion(
  object,
  s0,
  s1,
  t0,
  t1,
  from = min(s0),
  to = max(s1),
  step = 25,
  method = c("uniform", "truncated"),
  z = 2,
  progress = getOption("kairos.progress")
)

Arguments

object	A $m \times p$ numeric matrix or data.frame of count data (absolute frequencies giving the number of individuals for each category, i.e. a contingency table). A data.frame will be coerced to a numeric matrix via data.matrix().
...	Currently not used.
s0	A length-$m$ numeric vector giving the site beginning dates expressed in CE years (BCE years must be given as negative numbers).
s1	A length-$m$ numeric vector giving the site end dates expressed in CE years (BCE years must be given as negative numbers).
t0	A length-$p$ numeric vector giving the type beginning dates expressed in CE years (BCE years must be given as negative numbers).
t1	A length-$p$ numeric vector giving the type end dates expressed in CE years (BCE years must be given as negative numbers).
from	A length-one numeric vector giving the beginning of the period of interest (in years CE).
to	A length-one numeric vector giving the end of the period of interest (in years CE).
step	A length-one integer vector giving the step size, i.e. the width of each time step for apportioning (in years CE; defaults to $25$).
method	A character string specifying the distribution to be used (type popularity curve). It must be one of "uniform" (uniform distribution) or "truncated" (truncated standard normal distribution). Any unambiguous substring can be given.
z	An integer value giving the lower and upper truncation points (defaults to $2$). Only used if method is "truncated".
progress	A logical scalar: should a progress bar be displayed?

Value

A CountApportion object.

Author(s)

N. Frerebeau

References

Roberts, J. M., Mills, B. J., Clark, J. J., Haas, W. R., Huntley, D. L. & Trowbridge, M. A. (2012). A Method for Chronological Apportioning of Ceramic Assemblages. Journal of Archaeological Science, 39(5): 1513-20. doi:10.1016/j.jas.2011.12.022.

See Also

Other chronological analysis: fit(), plot_fit

Examples

## Replication of Roberts et al. 2012
bayless <- matrix(
  data = c(4, 333, 11, 11, 13, 1605, 252, 9, 48), nrow = 1,
  dimnames = list(c("Bayless"), c("CWW", "CBW", "LMGRW", "LTB", "MMS",
                                  "PBW", "RRW", "SCBW", "TBBW"))
)

## Set ware start and end dates
start <- c(550, 800, 1200, 1150, 1275, 200, 1275, 1200, 750)
end <- c(1325, 1400, 1450, 1300, 1400, 1450, 1450, 1450, 1300)

## Apportion ceramic assemblage under flat/uniform distribution
app <- apportion(bayless, s0 = 1200, s1 = 1350, t0 = start, t1 = end,
                 step = 50, method = "uniform")

## Apportion ceramic assemblage under truncated standard normal distribution
app <- apportion(bayless, s0 = 1200, s1 = 1350, t0 = start, t1 = end,
                 step = 50, method = "truncated", z = 2)

## Array of results
head(app)

---

Coerce an R Object to a Seriation Order

Description

Coerce an R Object to a Seriation Order

Usage

as_seriation(object, ...)

## S4 method for signature 'CA'
as_seriation(object, margin = c(1, 2), axes = 1)

Arguments

object	An R object.
...	Currently not used.
margin	A numeric vector giving the subscripts which the rearrangement will be applied over: 1 indicates rows, 2 indicates columns, c(1, 2) indicates rows then columns, c(2, 1) indicates columns then rows.
axes	An integer vector giving the subscripts of the CA axes to be used.

Value

A PermutationOrder object.

Author(s)

N. Frerebeau

See Also

Other seriation methods: assess(), order(), permute(), refine(), seriate_average(), seriate_rank()

---

Statistical Significance of Seriation Solutions

Description

Tests the significance of seriation solutions.

Usage

assess(object, ...)

## S4 method for signature 'AveragePermutationOrder'
assess(object, axes = 1, n = 1000, progress = getOption("kairos.progress"))

Arguments

object	A PermutationOrder object giving the permutation order for rows and columns (typically returned by seriate_average()).
...	Currently not used.
axes	An integer vector giving the subscripts of the CA axes to be used.
n	A non-negative integer giving the number of bootstrap replications.
progress	A logical scalar: should a progress bar be displayed?

Value

A list with the following elements:

random

A numeric vector giving the randomized total number of modes values.

observed

A numeric value giving the observed total number of modes.

expected

A numeric value giving the expected total number of modes if all types had unimodal distributions.

maximum

A numeric value giving the maximum total number of modes.

coef

A numeric value giving the seriation coefficient (a value close to 1 indicates a strong fit to the seriation model, while a value close to 0 indicates a poor fit).

Author(s)

N. Frerebeau

References

Porčić, M. (2013). The Goodness of Fit and Statistical Significance of Seriation Solutions. Journal of Archaeological Science, 40(12): 4552-4559. doi:10.1016/j.jas.2013.07.013.

See Also

Other seriation methods: as_seriation(), order(), permute(), refine(), seriate_average(), seriate_rank()

Examples

## Not run: 
## Data from Desachy 2004
data("compiegne", package = "folio")

## Correspondance analysis based seriation
(indices <- seriate_average(compiegne, margin = c(1, 2), axes = 1))

## Test significance of seriation results
## Warning: this may take a few seconds!
signif <- assess(indices, axes = 1, n = 1000)

## Histogram of randomized total number of modes
hist(signif$random)

## Observed value is smaller than the 5th percentile of the
## distribution of randomized samples
quantile(signif$random, probs = 0.05)
signif$observed

## Seriation coefficient
## (close to 1: relatively strong and significant signal of unimodality)
signif$coef

## End(Not run)

---

Coerce to a Data Frame

Description

Coerce to a Data Frame

Usage

## S4 method for signature 'MeanDate'
as.data.frame(x, ..., calendar = getOption("kairos.calendar"))

## S4 method for signature 'AoristicSum'
as.data.frame(x, ..., calendar = getOption("kairos.calendar"))

## S4 method for signature 'IncrementTest'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Arguments

x	An object.
...	Further parameters to be passed to data.frame().
calendar	An aion::TimeScale object specifying the target calendar (see calendar()). If NULL, rata die are returned.
row.names, optional	Currently not used.

Value

A data.frame with an extra time column giving the (decimal) years at which the time series was sampled.

Author(s)

N. Frerebeau

See Also

Other mutators: mutators, series(), subset()

---

Density of Event and Accumulation Dates

Description

Estimates the event and accumulation density.

Usage

density_event(object, ...)

density_accumulation(object, ...)

## S4 method for signature 'EventDate'
density_event(object, dates = NULL, calendar = NULL, n = 500, ...)

## S4 method for signature 'EventDate'
density_accumulation(
  object,
  dates = NULL,
  calendar = NULL,
  type = c("activity", "tempo"),
  n = 500,
  ...
)

Arguments

object	An EventDate object.
...	Currently not used.
dates	A numeric vector of dates expressed as calendar years or rata die (if calendar is NULL).
calendar	An aion::TimeScale object specifying the calendar of dates (see aion::calendar()). If NULL (the default), rata die are expected.
n	A length-one non-negative numeric vector giving the desired length of the vector of quantiles for density computation.
type	A character string indicating the type of plot. It must be one of "activity" (default) or "tempo" (see details). Any unambiguous substring can be given.

Value

An aion::TimeSeries object.

Author(s)

N. Frerebeau

See Also

Other event date tools: event(), model_event, plot_event, predict_event(), resample_event

Examples

## Data from Peeples and Schachner 2012
data("zuni", package = "folio")

## Assume that some assemblages are reliably dated (this is NOT a real example)
zuni_dates <- c(
  LZ0569 = 1097, LZ0279 = 1119, CS16 = 1328, LZ0066 = 1111,
  LZ0852 = 1216, LZ1209 = 1251, CS144 = 1262, LZ0563 = 1206,
  LZ0329 = 1076, LZ0005Q = 859, LZ0322 = 1109, LZ0067 = 863,
  LZ0578 = 1180, LZ0227 = 1104, LZ0610 = 1074
)

## Model the event and accumulation date for each assemblage
model <- event(zuni, zuni_dates, rank = 10)
plot(model, select = 1:10, event = TRUE, flip = TRUE)

---

Event and Accumulation Dates

Description

Fits a date event model.

Usage

event(object, dates, ...)

## S4 method for signature 'data.frame,numeric'
event(object, dates, rank = NULL, sup_row = NULL, calendar = CE(), ...)

## S4 method for signature 'matrix,numeric'
event(
  object,
  dates,
  calendar = CE(),
  rank = NULL,
  sup_row = NULL,
  total = 5,
  verbose = getOption("kairos.verbose"),
  ...
)

Arguments

object	A $m \times p$ numeric matrix or data.frame of count data (absolute frequencies giving the number of individuals for each category, i.e. a contingency table). A data.frame will be coerced to a numeric matrix via data.matrix().
dates	A numeric vector of dates. If named, the names must match the row names of object.
...	Further arguments to be passed to internal methods.
rank	An integer specifying the number of CA factorial components to be use for linear model fitting (see details). If NULL (the default), axes corresponding to at least 60% of the inertia will be used.
sup_row	A numeric or logical vector specifying the indices of the supplementary rows.
calendar	An aion::TimeScale object specifying the calendar of dates (see calendar()). Defaults to Gregorian Common Era.
total	A length-one numeric vector specifying the minimum total of a row/column. Rows/columns whose total is less than this value will be omitted from the analysis.
verbose	A logical scalar: should R report extra information on progress?

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.

Dates are converted to rata die before any computation.

This method relies on strong archaeological and statistical assumptions (see vignette("event")).

Value

An EventDate object.

Author(s)

N. Frerebeau

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

plot(), predict_event(), predict_accumulation(), jackknife(), bootstrap()

Other event date tools: density_event(), model_event, plot_event, predict_event(), resample_event

Other dating methods: mcd()

Examples

## Data from Peeples and Schachner 2012
data("zuni", package = "folio")

## Assume that some assemblages are reliably dated (this is NOT a real example)
zuni_dates <- c(
  LZ0569 = 1097, LZ0279 = 1119, CS16 = 1328, LZ0066 = 1111,
  LZ0852 = 1216, LZ1209 = 1251, CS144 = 1262, LZ0563 = 1206,
  LZ0329 = 1076, LZ0005Q = 859, LZ0322 = 1109, LZ0067 = 863,
  LZ0578 = 1180, LZ0227 = 1104, LZ0610 = 1074
)

## Model the event and accumulation date for each assemblage
model <- event(zuni, zuni_dates, rank = 10)
plot(model, select = 1:10, event = TRUE, flip = TRUE)

---

Frequency Increment Test

Description

Frequency Increment Test

Usage

fit(object, dates, ...)

## S4 method for signature 'data.frame,numeric'
fit(object, dates, calendar = CE(), level = 0.95, roll = FALSE, window = 3)

## S4 method for signature 'matrix,numeric'
fit(object, dates, calendar = CE(), level = 0.95, roll = FALSE, window = 3)

Arguments

object	A $m \times p$ numeric matrix or data.frame of count data (absolute frequencies giving the number of individuals for each category, i.e. a contingency table). A data.frame will be coerced to a numeric matrix via data.matrix().
dates	A length-$m$ numeric vector of dates.
...	Currently not used.
calendar	An aion::TimeScale object specifying the calendar of dates (see calendar()). Defaults to Gregorian Common Era.
level	A length-one numeric vector giving the confidence level.
roll	A logical scalar: should each time series be subsetted to look for episodes of selection?
window	An odd integer giving the size of the rolling window. Only used if roll is TRUE.

Details

The Frequency Increment Test (FIT) rejects neutrality if the distribution of normalized variant frequency increments exhibits a mean that deviates significantly from zero.

If roll is TRUE, each time series is subsetted according to window to see if episodes of selection can be identified among variables that might not show overall selection.

Value

An IncrementTest object.

Author(s)

N. Frerebeau

References

Feder, A. F., Kryazhimskiy, S. & Plotkin, J. B. (2014). Identifying Signatures of Selection in Genetic Time Series. Genetics, 196(2): 509-522. doi:10.1534/genetics.113.158220.

See Also

Other chronological analysis: apportion(), plot_fit

Examples

## Data from Crema et al. 2016
data("merzbach", package = "folio")

## Keep only decoration types that have a maximum frequency of at least 50
keep <- apply(X = merzbach, MARGIN = 2, FUN = function(x) max(x) >= 50)
counts <- merzbach[, keep]

## Group by phase
## We use the row names as time coordinates (roman numerals)
dates <- as.numeric(utils::as.roman(rownames(counts)))

## Frequency Increment Test
freq <- fit(counts, dates, calendar = NULL)

## Plot time vs abundance
plot(freq, calendar = NULL, ncol = 3, xlab = "Phases")

## Plot time vs abundance and highlight selection
freq <- fit(counts, dates, calendar = NULL, roll = TRUE, window = 5)
plot(freq, calendar = NULL, ncol = 3, xlab = "Phases")

---

Mean Ceramic Date

Description

Estimates the Mean Ceramic Date of an assemblage.

Usage

mcd(object, dates, ...)

## S4 method for signature 'numeric,numeric'
mcd(object, dates, calendar = CE())

## S4 method for signature 'data.frame,numeric'
mcd(object, dates, calendar = CE())

## S4 method for signature 'matrix,numeric'
mcd(object, dates, calendar = CE())

Arguments

object	A $m \times p$ numeric matrix or data.frame of count data (absolute frequencies giving the number of individuals for each category, i.e. a contingency table). A data.frame will be coerced to a numeric matrix via data.matrix().
dates	A length-$p$ numeric vector of dates expressed in years.
...	Currently not used.
calendar	An aion::TimeScale object specifying the calendar of dates (see calendar()). Defaults to Gregorian Common Era.

Details

The Mean Ceramic Date (MCD) is a point estimate of the occupation of an archaeological site (South 1977). The MCD is estimated as the weighted mean of the date midpoints of the ceramic types (based on absolute dates or the known production interval) found in a given assemblage. The weights are the relative frequencies of the respective types in the assemblage.

A bootstrapping procedure is used to estimate the confidence interval of a given MCD. For each assemblage, a large number of new bootstrap replicates is created, with the same sample size, by resampling the original assemblage with replacement. MCDs are calculated for each replicates and upper and lower boundaries of the confidence interval associated with each MCD are then returned.

Value

A MeanDate object.

Author(s)

N. Frerebeau

References

South, S. A. (1977). Method and Theory in Historical Archaeology. New York: Academic Press.

See Also

Other mean ceramic date tools: plot_mcd, resample_mcd

Other dating methods: event()

Examples

## Data from Peeples and Schachner 2012
data("zuni", package = "folio")

## Set the start and end dates for each ceramic type
dates <- list(
  LINO = c(600, 875), KIAT = c(850, 950), RED = c(900, 1050),
  GALL = c(1025, 1125), ESC = c(1050, 1150), PUBW = c(1050, 1150),
  RES = c(1000, 1200), TULA = c(1175, 1300), PINE = c(1275, 1350),
  PUBR = c(1000, 1200), WING = c(1100, 1200), WIPO = c(1125, 1225),
  SJ = c(1200, 1300), LSJ = c(1250, 1300), SPR = c(1250, 1300),
  PINER = c(1275, 1325), HESH = c(1275, 1450), KWAK = c(1275, 1450)
)

## Calculate date midpoints
mid <- vapply(X = dates, FUN = mean, FUN.VALUE = numeric(1))

## Calculate MCD
(mc_dates <- mcd(zuni[100:125, ], dates = mid))

## Get MCD in years CE
time(mc_dates, calendar = CE())

## Plot
plot(mc_dates)

## Bootstrap resampling
boot <- bootstrap(mc_dates, n = 30)
head(boot)

## Jackknife resampling
jack <- jackknife(mc_dates)
head(jack)

## Simulation
sim <- simulate(mc_dates, nsim = 30)
plot(sim, interval = "range", pch = 16)

---

Extract Event Date Model Results

Description

• summary() summarizes linear model fit.

• coef() extracts model coefficients (see stats::coef()).

• fitted() extracts model fitted values (see stats::fitted()).

• residuals() extracts model residuals (see stats::residuals()).

• sigma() extracts the residual standard deviation (see stats::sigma()).

• terms() extracts model terms (see stats::terms()).

Usage

## S4 method for signature 'EventDate'
summary(object, ...)

## S4 method for signature 'EventDate'
coef(object, calendar = NULL, ...)

## S4 method for signature 'EventDate'
fitted(object, calendar = NULL, ...)

## S4 method for signature 'EventDate'
residuals(object, calendar = NULL, ...)

## S4 method for signature 'EventDate'
sigma(object, calendar = NULL, ...)

## S4 method for signature 'EventDate'
terms(x, ...)

Arguments

...	Currently not used.
calendar	An aion::TimeScale object specifying the target calendar (see aion::calendar()). If NULL (the default), rata die are returned.
x, object	An EventDate object.

Author(s)

N. Frerebeau

See Also

Other event date tools: density_event(), event(), plot_event, predict_event(), resample_event

Examples

## Data from Peeples and Schachner 2012
data("zuni", package = "folio")

## Assume that some assemblages are reliably dated (this is NOT a real example)
zuni_dates <- c(
  LZ0569 = 1097, LZ0279 = 1119, CS16 = 1328, LZ0066 = 1111,
  LZ0852 = 1216, LZ1209 = 1251, CS144 = 1262, LZ0563 = 1206,
  LZ0329 = 1076, LZ0005Q = 859, LZ0322 = 1109, LZ0067 = 863,
  LZ0578 = 1180, LZ0227 = 1104, LZ0610 = 1074
)

## Model the event and accumulation date for each assemblage
model <- event(zuni, zuni_dates, rank = 10)
plot(model, select = 1:10, event = TRUE, flip = TRUE)

---

Get or Set Parts of an Object

Description

Getters and setters to retrieve or set parts of an object.

Usage

## S4 method for signature 'AoristicSum'
weights(object, ...)

## S4 method for signature 'CountApportion'
weights(object, ...)

Arguments

object	An object from which to get or set element(s).
...	Currently not used.

Author(s)

N. Frerebeau

See Also

Other mutators: data.frame, series(), subset()

---

Permutation Order

Description

Returns the seriation order for rows and/or columns.

Usage

order_rows(object, ...)

order_columns(object, ...)

## S4 method for signature 'PermutationOrder'
order_rows(object)

## S4 method for signature 'PermutationOrder'
order_columns(object)

Arguments

object	A PermutationOrder object giving the permutation order for rows and columns.
...	Currently not used.

Value

An integer vector.

Author(s)

N. Frerebeau

See Also

Other seriation methods: as_seriation(), assess(), permute(), refine(), seriate_average(), seriate_rank()

Examples

## Replicates Desachy 2004 results
data("compiegne", package = "folio")

## Get seriation order for columns on EPPM using the reciprocal averaging method
## Expected column order: N, A, C, K, P, L, B, E, I, M, D, G, O, J, F, H
(indices <- seriate_rank(compiegne, EPPM = TRUE, margin = 2))

## Get permutation order
order_rows(indices)
order_columns(indices)

## Permute columns
(new <- permute(compiegne, indices))

---

Rearrange a Data Matrix

Description

Rearranges a data matrix according to a permutation order.

Usage

permute(object, order, ...)

## S4 method for signature 'data.frame,PermutationOrder'
permute(object, order)

## S4 method for signature 'matrix,PermutationOrder'
permute(object, order)

Arguments

object	A $m \times p$ numeric matrix or data.frame of count data (absolute frequencies giving the number of individuals for each category, i.e. a contingency table).
order	A PermutationOrder object giving the permutation order for rows and columns.
...	Currently not used.

Value

A permuted matrix or a permuted data.frame (the same as object).

Author(s)

N. Frerebeau

See Also

Other seriation methods: as_seriation(), assess(), order(), refine(), seriate_average(), seriate_rank()

Examples

## Replicates Desachy 2004 results
data("compiegne", package = "folio")

## Get seriation order for columns on EPPM using the reciprocal averaging method
## Expected column order: N, A, C, K, P, L, B, E, I, M, D, G, O, J, F, H
(indices <- seriate_rank(compiegne, EPPM = TRUE, margin = 2))

## Get permutation order
order_rows(indices)
order_columns(indices)

## Permute columns
(new <- permute(compiegne, indices))

---

Plot Aoristic Analysis

Description

Plot Aoristic Analysis

Usage

## S4 method for signature 'AoristicSum,missing'
plot(
  x,
  calendar = getOption("kairos.calendar"),
  type = c("bar"),
  flip = FALSE,
  ncol = NULL,
  main = NULL,
  sub = NULL,
  ann = graphics::par("ann"),
  axes = TRUE,
  frame.plot = axes,
  panel.first = NULL,
  panel.last = NULL,
  ...
)

## S4 method for signature 'AoristicSum'
image(x, calendar = getOption("kairos.calendar"), ...)

## S4 method for signature 'RateOfChange,missing'
plot(
  x,
  calendar = getOption("kairos.calendar"),
  level = 0.95,
  flip = FALSE,
  ncol = NULL,
  main = NULL,
  sub = NULL,
  ann = graphics::par("ann"),
  axes = TRUE,
  frame.plot = axes,
  panel.first = NULL,
  panel.last = NULL,
  ...
)

Arguments

x	An AoristicSum object.
calendar	An aion::TimeScale object specifying the target calendar (see calendar()).
type	A character string specifying whether bar or density should be plotted? It must be one of "bar" or "density". Any unambiguous substring can be given.
flip	A logical scalar: should the y-axis (ticks and numbering) be flipped from side 2 (left) to 4 (right) from series to series when facet is "multiple"?
ncol	An integer specifying the number of columns to use when facet is "multiple". Defaults to 1 for up to 4 series, otherwise to 2.
main	A character string giving a main title for the plot.
sub	A character string giving a subtitle for the plot.
ann	A logical scalar: should the default annotation (title and x and y axis labels) appear on the plot?
axes	A logical scalar: should axes be drawn on the plot?
frame.plot	A logical scalar: should a box be drawn around the plot?
panel.first	An expression to be evaluated after the plot axes are set up but before any plotting takes place. This can be useful for drawing background grids.
panel.last	An expression to be evaluated after plotting has taken place but before the axes, title and box are added.
...	Further parameters to be passed to panel (e.g. graphical parameters).
level	A length-one numeric vector giving the confidence level.

Value

plot() is called it for its side-effects: it results in a graphic being displayed (invisibly returns x).

Author(s)

N. Frerebeau

See Also

Other aoristic analysis: aoristic(), roc()

Examples

## Data from Husi 2022
data("loire", package = "folio")

## Get time range
loire_range <- loire[, c("lower", "upper")]

## Calculate aoristic sum (normal)
aorist_raw <- aoristic(loire_range, step = 50, weight = FALSE)
plot(aorist_raw, col = "grey")

## Calculate aoristic sum (weights)
aorist_weighted <- aoristic(loire_range, step = 50, weight = TRUE)
plot(aorist_weighted, col = "grey")

## Calculate aoristic sum (weights) by group
aorist_groups <- aoristic(loire_range, step = 50, weight = TRUE,
                          groups = loire$area)
plot(aorist_groups, flip = TRUE, col = "grey")
image(aorist_groups)

## Rate of change
roc_weighted <- roc(aorist_weighted, n = 30)
plot(roc_weighted)

## Rate of change by group
roc_groups <- roc(aorist_groups, n = 30)
plot(roc_groups, flip = TRUE)

---

Plot Event and Accumulation Dates

Description

Produces an activity or a tempo plot.

Usage

## S4 method for signature 'EventDate,missing'
plot(
  x,
  type = c("activity", "tempo"),
  event = FALSE,
  calendar = getOption("kairos.calendar"),
  select = 1,
  n = 500,
  eps = 1e-09,
  col.accumulation = "black",
  col.event = "red",
  flip = FALSE,
  ncol = NULL,
  xlab = NULL,
  ylab = NULL,
  main = NULL,
  sub = NULL,
  ann = graphics::par("ann"),
  axes = TRUE,
  frame.plot = axes,
  ...
)

Arguments

x	An EventDate object.
type	A character string indicating the type of plot. It must be one of "activity" (default) or "tempo" (see details). Any unambiguous substring can be given.
event	A logical scalar: should the distribution of the event date be displayed? Only used if type is "activity".
calendar	An aion::TimeScale object specifying the target calendar (see aion::calendar()).
select	A numeric or character vector giving the selection of the assemblage that are drawn.
n	A length-one non-negative numeric vector giving the desired length of the vector of quantiles for density computation.
eps	A length-one numeric value giving the cutoff below which values will be removed.
col.accumulation	A color specification for the accumulation density curve.
col.event	A color specification for the event density curve.
flip	A logical scalar: should the y-axis (ticks and numbering) be flipped from side 2 (left) to 4 (right) from series to series when facet is "multiple"?
ncol	An integer specifying the number of columns to use when facet is "multiple". Defaults to 1 for up to 4 series, otherwise to 2.
xlab, ylab	A character vector giving the x and y axis labels.
main	A character string giving a main title for the plot.
sub	A character string giving a subtitle for the plot.
ann	A logical scalar: should the default annotation (title and x and y axis labels) appear on the plot?
axes	A logical scalar: should axes be drawn on the plot?
frame.plot	A logical scalar: should a box be drawn around the plot?
...	Further parameters to be passed to panel (e.g. graphical parameters).

Value

plot() is called it for its side-effects: it results in a graphic being displayed (invisibly returns x).

Event and Acccumulation Dates

plot() displays the probability estimate density curves of archaeological assemblage dates (event and accumulation dates; Bellanger and Husi 2012). The event date is plotted as a line, while the accumulation date is shown as a grey filled area.

The accumulation date can be displayed as a tempo plot (Dye 2016) or an activity plot (Philippe and Vibet 2020):

tempo

A tempo plot estimates the cumulative occurrence of archaeological events, such as the slope of the plot directly reflects the pace of change.

activity

An activity plot displays the first derivative of the tempo plot.

Author(s)

N. Frerebeau

References

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.

Dye, T. S. (2016). Long-Term Rhythms in the Development of Hawaiian Social Stratification. Journal of Archaeological Science, 71, 1-9. doi:10.1016/j.jas.2016.05.006.

Philippe, A. & Vibet, M.-A. (2020). Analysis of Archaeological Phases Using the R Package ArchaeoPhases. Journal of Statistical Software, Code Snippets, 93(1), 1-25. doi:10.18637/jss.v093.c01.

See Also

event()

event()

Other event date tools: density_event(), event(), model_event, predict_event(), resample_event

Examples

## Data from Peeples and Schachner 2012
data("zuni", package = "folio")

## Assume that some assemblages are reliably dated (this is NOT a real example)
zuni_dates <- c(
  LZ0569 = 1097, LZ0279 = 1119, CS16 = 1328, LZ0066 = 1111,
  LZ0852 = 1216, LZ1209 = 1251, CS144 = 1262, LZ0563 = 1206,
  LZ0329 = 1076, LZ0005Q = 859, LZ0322 = 1109, LZ0067 = 863,
  LZ0578 = 1180, LZ0227 = 1104, LZ0610 = 1074
)

## Model the event and accumulation date for each assemblage
model <- event(zuni, zuni_dates, rank = 10)
plot(model, select = 1:10, event = TRUE, flip = TRUE)

---

Detection of Selective Processes

Description

Produces an abundance vs time diagram.

Usage

## S4 method for signature 'IncrementTest,missing'
plot(
  x,
  calendar = getOption("kairos.calendar"),
  col.neutral = "#004488",
  col.selection = "#BB5566",
  col.roll = "grey",
  flip = FALSE,
  ncol = NULL,
  xlab = NULL,
  ylab = NULL,
  main = NULL,
  sub = NULL,
  ann = graphics::par("ann"),
  axes = TRUE,
  frame.plot = axes,
  ...
)

Arguments

x	An IncrementTest object to be plotted.
calendar	An aion::TimeScale object specifying the target calendar (see calendar()).
col.neutral, col.selection, col.roll	A vector of colors.
flip	A logical scalar: should the y-axis (ticks and numbering) be flipped from side 2 (left) to 4 (right) from series to series when facet is "multiple"?
ncol	An integer specifying the number of columns to use when facet is "multiple". Defaults to 1 for up to 4 series, otherwise to 2.
xlab, ylab	A character vector giving the x and y axis labels.
main	A character string giving a main title for the plot.
sub	A character string giving a subtitle for the plot.
ann	A logical scalar: should the default annotation (title and x and y axis labels) appear on the plot?
axes	A logical scalar: should axes be drawn on the plot?
frame.plot	A logical scalar: should a box be drawn around the plot?
...	Further parameters to be passed to panel (e.g. graphical parameters).

Details

Results of the frequency increment test can be displayed on an abundance vs time diagram aid in the detection and quantification of selective processes in the archaeological record. If roll is TRUE, each time series is subsetted according to window to see if episodes of selection can be identified among decoration types that might not show overall selection. If so, shading highlights the data points where fit() identifies selection.

Value

plot() is called it for its side-effects: it results in a graphic being displayed (invisibly returns x).

Note

Displaying FIT results on an abundance vs time diagram is adapted from Ben Marwick's original idea.

Author(s)

N. Frerebeau

See Also

Other chronological analysis: apportion(), fit()

Examples

## Data from Crema et al. 2016
data("merzbach", package = "folio")

## Keep only decoration types that have a maximum frequency of at least 50
keep <- apply(X = merzbach, MARGIN = 2, FUN = function(x) max(x) >= 50)
counts <- merzbach[, keep]

## Group by phase
## We use the row names as time coordinates (roman numerals)
dates <- as.numeric(utils::as.roman(rownames(counts)))

## Frequency Increment Test
freq <- fit(counts, dates, calendar = NULL)

## Plot time vs abundance
plot(freq, calendar = NULL, ncol = 3, xlab = "Phases")

## Plot time vs abundance and highlight selection
freq <- fit(counts, dates, calendar = NULL, roll = TRUE, window = 5)
plot(freq, calendar = NULL, ncol = 3, xlab = "Phases")

---

MCD Plot

Description

MCD Plot

Usage

## S4 method for signature 'MeanDate,missing'
plot(
  x,
  calendar = getOption("kairos.calendar"),
  decreasing = TRUE,
  main = NULL,
  sub = NULL,
  ann = graphics::par("ann"),
  axes = TRUE,
  frame.plot = axes,
  panel.first = NULL,
  panel.last = NULL,
  ...
)

## S4 method for signature 'SimulationMeanDate,missing'
plot(
  x,
  calendar = getOption("kairos.calendar"),
  interval = "student",
  level = 0.8,
  decreasing = TRUE,
  main = NULL,
  sub = NULL,
  ann = graphics::par("ann"),
  axes = TRUE,
  frame.plot = axes,
  panel.first = NULL,
  panel.last = NULL,
  ...
)

Arguments

x	A MeanDate object.
calendar	An aion::TimeScale object specifying the target calendar (see calendar()).
decreasing	A logical scalar: should the sort be increasing or decreasing?
main	A character string giving a main title for the plot.
sub	A character string giving a subtitle for the plot.
ann	A logical scalar: should the default annotation (title and x, y and z axis labels) appear on the plot?
axes	A logical scalar: should axes be drawn on the plot?
frame.plot	A logical scalar: should a box be drawn around the plot?
panel.first	An an expression to be evaluated after the plot axes are set up but before any plotting takes place. This can be useful for drawing background grids.
panel.last	An expression to be evaluated after plotting has taken place but before the axes, title and box are added.
...	Further graphical parameters.
interval	A character string giving the type of confidence interval to be returned. It must be one "student" (the default), "normal", "percentiles" or "range" (min-max). Any unambiguous substring can be given.
level	A length-one numeric vector giving the confidence level. Only used if interval is not "range".

Value

plot() is called it for its side-effects: it results in a graphic being displayed (invisibly returns x).

Author(s)

N. Frerebeau

See Also

Other mean ceramic date tools: mcd(), resample_mcd

Examples

## Data from Peeples and Schachner 2012
data("zuni", package = "folio")

## Set the start and end dates for each ceramic type
dates <- list(
  LINO = c(600, 875), KIAT = c(850, 950), RED = c(900, 1050),
  GALL = c(1025, 1125), ESC = c(1050, 1150), PUBW = c(1050, 1150),
  RES = c(1000, 1200), TULA = c(1175, 1300), PINE = c(1275, 1350),
  PUBR = c(1000, 1200), WING = c(1100, 1200), WIPO = c(1125, 1225),
  SJ = c(1200, 1300), LSJ = c(1250, 1300), SPR = c(1250, 1300),
  PINER = c(1275, 1325), HESH = c(1275, 1450), KWAK = c(1275, 1450)
)

## Calculate date midpoints
mid <- vapply(X = dates, FUN = mean, FUN.VALUE = numeric(1))

## Calculate MCD
(mc_dates <- mcd(zuni[100:125, ], dates = mid))

## Get MCD in years CE
time(mc_dates, calendar = CE())

## Plot
plot(mc_dates)

## Bootstrap resampling
boot <- bootstrap(mc_dates, n = 30)
head(boot)

## Jackknife resampling
jack <- jackknife(mc_dates)
head(jack)

## Simulation
sim <- simulate(mc_dates, nsim = 30)
plot(sim, interval = "range", pch = 16)

---

Abundance vs Time Plot

Description

Produces an abundance vs time diagram.

Usage

plot_time(object, dates, ...)

## S4 method for signature 'data.frame,numeric'
plot_time(object, dates, calendar = getOption("kairos.calendar"), ...)

## S4 method for signature 'matrix,numeric'
plot_time(object, dates, calendar = getOption("kairos.calendar"), ...)

Arguments

object	A $m \times p$ numeric matrix or data.frame of count data (absolute frequencies giving the number of individuals for each category, i.e. a contingency table). A data.frame will be coerced to a numeric matrix via data.matrix().
dates	A numeric vector of dates.
...	Further parameters to be passed to aion::plot().
calendar	An aion::TimeScale object specifying the target calendar (see calendar()).

Value

plot_time() is called it for its side-effects: it results in a graphic being displayed (invisibly returns object).

Author(s)

N. Frerebeau

Examples

## Data from Crema et al. 2016
data("merzbach", package = "folio")

## Coerce the merzbach dataset to a count matrix
## Keep only decoration types that have a maximum frequency of at least 50
keep <- apply(X = merzbach, MARGIN = 2, FUN = function(x) max(x) >= 50)
counts <- merzbach[, keep]

## Set dates
## We use the row names as time coordinates (roman numerals)
dates <- as.numeric(utils::as.roman(rownames(counts)))

## Plot abundance vs time
plot_time(counts, dates, calendar = NULL, ncol = 3, xlab = "Phases")

---

Predict Event and Accumulation Dates

Description

Estimates the event and accumulation dates of an assemblage.

Usage

predict_event(object, data, ...)

predict_accumulation(object, data, ...)

## S4 method for signature 'EventDate,missing'
predict_event(
  object,
  margin = 1,
  level = 0.95,
  calendar = getOption("kairos.calendar")
)

## S4 method for signature 'EventDate,matrix'
predict_event(
  object,
  data,
  margin = 1,
  level = 0.95,
  calendar = getOption("kairos.calendar")
)

## S4 method for signature 'EventDate,missing'
predict_accumulation(
  object,
  level = 0.95,
  calendar = getOption("kairos.calendar")
)

## S4 method for signature 'EventDate,matrix'
predict_accumulation(
  object,
  data,
  level = 0.95,
  calendar = getOption("kairos.calendar")
)

Arguments

object	An EventDate object.
data	A numeric matrix or a data.frame of count data (absolute frequencies) for which to predict event and accumulation dates.
...	Further arguments to be passed to internal methods.
margin	A numeric vector giving the subscripts which the prediction will be applied over: 1 indicates rows, 2 indicates columns.
level	A length-one numeric vector giving the confidence level.
calendar	An aion::TimeScale object specifying the target calendar (see aion::calendar()). If NULL, rata die are returned.

Value

A data.frame.

Author(s)

N. Frerebeau

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.

See Also

Other event date tools: density_event(), event(), model_event, plot_event, resample_event

Examples

## Data from Peeples and Schachner 2012
data("zuni", package = "folio")

## Assume that some assemblages are reliably dated (this is NOT a real example)
zuni_dates <- c(
  LZ0569 = 1097, LZ0279 = 1119, CS16 = 1328, LZ0066 = 1111,
  LZ0852 = 1216, LZ1209 = 1251, CS144 = 1262, LZ0563 = 1206,
  LZ0329 = 1076, LZ0005Q = 859, LZ0322 = 1109, LZ0067 = 863,
  LZ0578 = 1180, LZ0227 = 1104, LZ0610 = 1074
)

## Model the event and accumulation date for each assemblage
model <- event(zuni, zuni_dates, rank = 10)
plot(model, select = 1:10, event = TRUE, flip = TRUE)

---

Refine CA-based Seriation

Description

Refine CA-based Seriation

Usage

refine(object, ...)

## S4 method for signature 'AveragePermutationOrder'
refine(object, cutoff, margin = 1, axes = 1, n = 30, ...)

## S4 method for signature 'BootstrapCA'
refine(object, cutoff, margin = 1, axes = 1, ...)

Arguments

object	A PermutationOrder object (typically returned by seriate_average()) or a dimensio::BootstrapCA object (typically returned by dimensio::bootstrap()).
...	Currently not used.
cutoff	A function that takes a numeric vector as argument and returns a single numeric value (see below).
margin	A length-one numeric vector giving the subscripts which the refinement will be applied over: 1 indicates rows, 2 indicates columns.
axes	An integer vector giving the subscripts of the CA axes to be used.
n	A non-negative integer giving the number of bootstrap replications.

Details

refine() allows to identify samples that are subject to sampling error or samples that have underlying structural relationships and might be influencing the ordering along the CA space.

This relies on a partial bootstrap approach to CA-based seriation where each sample is replicated n times. The maximum dimension length of the convex hull around the sample point cloud allows to remove samples for a given cutoff value.

According to Peebles and Schachner (2012), "[this] point removal procedure [results in] a reduced dataset where the position of individuals within the CA are highly stable and which produces an ordering consistent with the assumptions of frequency seriation."

See vignette("seriation").

Value

A list with the following elements:

length

A numeric vector giving the convex hull maximum dimension length.

cutoff

A numeric value giving the cutoff value for samples selection.

exclude

An integer vector giving the subscript of the observations to be removed.

margin

A numeric value specifying the dimension along which the refinement procedure has been applied: 1 indicates rows, 2 indicates columns.

Author(s)

N. Frerebeau

References

Peeples, M. A., & Schachner, G. (2012). Refining correspondence analysis-based ceramic seriation of regional data sets. Journal of Archaeological Science, 39(8), 2818-2827. doi:10.1016/j.jas.2012.04.040.

See Also

dimensio::bootstrap()

Other seriation methods: as_seriation(), assess(), order(), permute(), seriate_average(), seriate_rank()

---

Resample Event Dates

Description

• bootstrap() generate bootstrap estimations of an event.

• jackknife() generate jackknife estimations of an event.

Usage

## S4 method for signature 'EventDate'
jackknife(
  object,
  level = 0.95,
  calendar = getOption("kairos.calendar"),
  progress = getOption("kairos.progress"),
  verbose = getOption("kairos.verbose"),
  ...
)

## S4 method for signature 'EventDate'
bootstrap(
  object,
  level = 0.95,
  probs = c(0.05, 0.95),
  n = 1000,
  calendar = getOption("kairos.calendar"),
  progress = getOption("kairos.progress"),
  ...
)

Arguments

object	An EventDate object (typically returned by event()).
level	A length-one numeric vector giving the confidence level.
calendar	An aion::TimeScale object specifying the target calendar (see aion::calendar()). If NULL, rata die are returned.
progress	A logical scalar: should a progress bar be displayed?
verbose	A logical scalar: should R report extra information on progress?
...	Further arguments to be passed to internal methods.
probs	A numeric vector of probabilities with values in $[0,1]$.
n	A non-negative integer specifying the number of bootstrap replications.

Details

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).

lower

The lower boundary of the confidence interval.

upper

The upper boundary of the confidence interval.

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.

Value

A data.frame.

Author(s)

N. Frerebeau

See Also

Other event date tools: density_event(), event(), model_event, plot_event, predict_event()

---

Resample Mean Ceramic Dates

Description

• bootstrap() generate bootstrap estimations of an MCD.

• jackknife() generate jackknife estimations of an MCD.

Usage

## S4 method for signature 'MeanDate'
bootstrap(object, n = 1000, f = NULL, calendar = getOption("kairos.calendar"))

## S4 method for signature 'MeanDate'
jackknife(object, f = NULL, calendar = getOption("kairos.calendar"))

## S4 method for signature 'MeanDate'
simulate(object, nsim = 1000)

Arguments

object	A MeanDate object (typically returned by mcd()).
n	A non-negative integer specifying the number of bootstrap replications.
f	A function that takes a single numeric vector (the result of the resampling procedure) as argument.
calendar	An aion::TimeScale object specifying the target calendar (see calendar()).
nsim	A non-negative integer specifying the number of simulations.

Value

If f is NULL, bootstrap() and jackknife() return a data.frame with the following elements (else, returns the result of f applied to the n resampled values) :

original

The observed value.

mean

The bootstrap/jackknife estimate of mean.

bias

The bootstrap/jackknife estimate of bias.

error

The boostrap/jackknife estimate of standard erro.

Author(s)

N. Frerebeau

See Also

Other mean ceramic date tools: mcd(), plot_mcd

---

Rate of Change

Description

Computes the rate of change from an aoristic analysis.

Usage

roc(object, ...)

## S4 method for signature 'AoristicSum'
roc(object, n = 100)

Arguments

object	An AoristicSum object.
...	Currently not used.
n	A non-negative integer giving the number of replications (see details).

Value

A RateOfChange object.

Author(s)

N. Frerebeau

References

Baxter, M. J. & Cool, H. E. M. (2016). Reinventing the Wheel? Modelling Temporal Uncertainty with Applications to Brooch Distributions in Roman Britain. Journal of Archaeological Science, 66: 120-27. doi:10.1016/j.jas.2015.12.007.

Crema, E. R. (2012). Modelling Temporal Uncertainty in Archaeological Analysis. Journal of Archaeological Method and Theory, 19(3): 440-61. doi:10.1007/s10816-011-9122-3.

See Also

Other aoristic analysis: aoristic(), plot_aoristic

Examples

## Data from Husi 2022
data("loire", package = "folio")

## Get time range
loire_range <- loire[, c("lower", "upper")]

## Calculate aoristic sum (normal)
aorist_raw <- aoristic(loire_range, step = 50, weight = FALSE)
plot(aorist_raw, col = "grey")

## Calculate aoristic sum (weights)
aorist_weighted <- aoristic(loire_range, step = 50, weight = TRUE)
plot(aorist_weighted, col = "grey")

## Calculate aoristic sum (weights) by group
aorist_groups <- aoristic(loire_range, step = 50, weight = TRUE,
                          groups = loire$area)
plot(aorist_groups, flip = TRUE, col = "grey")
image(aorist_groups)

## Rate of change
roc_weighted <- roc(aorist_weighted, n = 30)
plot(roc_weighted)

## Rate of change by group
roc_groups <- roc(aorist_groups, n = 30)
plot(roc_groups, flip = TRUE)

---

Correspondence Analysis-Based Seriation

Description

Correspondence Analysis-Based Seriation

Usage

seriate_average(object, ...)

## S4 method for signature 'data.frame'
seriate_average(
  object,
  margin = c(1, 2),
  axes = 1,
  sup_row = NULL,
  sup_col = NULL,
  ...
)

## S4 method for signature 'matrix'
seriate_average(
  object,
  margin = c(1, 2),
  axes = 1,
  sup_row = NULL,
  sup_col = NULL,
  ...
)

Arguments

object	A $m \times p$ numeric matrix or data.frame of count data (absolute frequencies giving the number of individuals for each category, i.e. a contingency table). A data.frame will be coerced to a numeric matrix via data.matrix().
...	Currently not used.
margin	A numeric vector giving the subscripts which the rearrangement will be applied over: 1 indicates rows, 2 indicates columns, c(1, 2) indicates rows then columns, c(2, 1) indicates columns then rows.
axes	An integer vector giving the subscripts of the CA axes to be used.
sup_row	A vector specifying the indices of the supplementary rows (see dimensio::ca()).
sup_col	A vector specifying the indices of the supplementary columns (see dimensio::ca()).

Details

Correspondence analysis (CA) is an effective method for the seriation of archaeological assemblages. The order of the rows and columns is given by the coordinates along one dimension of the CA space, assumed to account for temporal variation. The direction of temporal change within the correspondence analysis space is arbitrary: additional information is needed to determine the actual order in time.

Value

An AveragePermutationOrder object.

Author(s)

N. Frerebeau

References

Ihm, P. (2005). A Contribution to the History of Seriation in Archaeology. In C. Weihs & W. Gaul (Eds.), Classification: The Ubiquitous Challenge. Berlin Heidelberg: Springer, p. 307-316. doi:10.1007/3-540-28084-7_34.

See Also

dimensio::ca()

Other seriation methods: as_seriation(), assess(), order(), permute(), refine(), seriate_rank()

Examples

## Replicates Desachy 2004 results
data("compiegne", package = "folio")

## Get seriation order for columns on EPPM using the reciprocal averaging method
## Expected column order: N, A, C, K, P, L, B, E, I, M, D, G, O, J, F, H
(indices <- seriate_rank(compiegne, EPPM = TRUE, margin = 2))

## Get permutation order
order_rows(indices)
order_columns(indices)

## Permute columns
(new <- permute(compiegne, indices))

---

Reciprocal Ranking Seriation

Description

Reciprocal Ranking Seriation

Usage

seriate_rank(object, ...)

## S4 method for signature 'data.frame'
seriate_rank(object, EPPM = FALSE, margin = c(1, 2), stop = 100)

## S4 method for signature 'matrix'
seriate_rank(object, EPPM = FALSE, margin = c(1, 2), stop = 100)

Arguments

object	A $m \times p$ numeric matrix or data.frame of count data (absolute frequencies giving the number of individuals for each category, i.e. a contingency table). A data.frame will be coerced to a numeric matrix via data.matrix().
...	Currently not used.
EPPM	A logical scalar: should the seriation be computed on EPPM instead of raw data?
margin	A numeric vector giving the subscripts which the rearrangement will be applied over: 1 indicates rows, 2 indicates columns, c(1, 2) indicates rows then columns, c(2, 1) indicates columns then rows.
stop	An integer giving the stopping rule (i.e. maximum number of iterations) to avoid infinite loop.

Details

This procedure iteratively rearrange rows and/or columns according to their weighted rank in the data matrix until convergence.

Note that this procedure could enter into an infinite loop. If no convergence is reached before the maximum number of iterations, it stops with a warning.

Value

A RankPermutationOrder object.

Author(s)

N. Frerebeau

References

Desachy, B. (2004). Le sériographe EPPM: un outil informatisé de sériation graphique pour tableaux de comptages. Revue archéologique de Picardie, 3(1), 39-56. doi:10.3406/pica.2004.2396.

Dunnell, R. C. (1970). Seriation Method and Its Evaluation. American Antiquity, 35(03), 305-319. doi:10.2307/278341.

Ihm, P. (2005). A Contribution to the History of Seriation in Archaeology. In C. Weihs & W. Gaul (Eds.), Classification: The Ubiquitous Challenge. Berlin Heidelberg: Springer, p. 307-316. doi:10.1007/3-540-28084-7_34.

See Also

Other seriation methods: as_seriation(), assess(), order(), permute(), refine(), seriate_average()

Examples

## Replicates Desachy 2004 results
data("compiegne", package = "folio")

## Get seriation order for columns on EPPM using the reciprocal averaging method
## Expected column order: N, A, C, K, P, L, B, E, I, M, D, G, O, J, F, H
(indices <- seriate_rank(compiegne, EPPM = TRUE, margin = 2))

## Get permutation order
order_rows(indices)
order_columns(indices)

## Permute columns
(new <- permute(compiegne, indices))

---

Sampling Times

Description

Get the times at which a time series was sampled.

Usage

## S4 method for signature 'EventDate'
time(x, calendar = NULL)

## S4 method for signature 'AoristicSum'
span(x, calendar = NULL)

Arguments

x	An R object.
calendar	An aion::TimeScale object specifying the target calendar (see aion::calendar()). If NULL (the default), rata die are returned.

Value

A numeric vector.

Author(s)

N. Frerebeau

See Also

Other mutators: data.frame, mutators, subset()

---

Extract or Replace Parts of an Object

Description

Operators acting on objects to extract or replace parts.

Usage

## S4 method for signature 'MeanDate'
x[i, j, k, drop = FALSE]

## S4 method for signature 'IncrementTest'
x[i, j, k, drop = FALSE]

## S4 method for signature 'PermutationOrder,ANY,missing'
x[[i]]

Arguments

x	An object from which to extract element(s) or in which to replace element(s).
i, j, k	Indices specifying elements to extract or replace.
drop	A logical scalar: should the result be coerced to the lowest possible dimension? This only works for extracting elements, not for the replacement.

Value

A subsetted object.

Author(s)

N. Frerebeau

See Also

Other mutators: data.frame, mutators, series()

---