Estimates the Mean Ceramic Date of an assemblage.
Usage
mcd(object, dates, ...)
# S4 method for numeric,numeric
mcd(object, dates)
# S4 method for data.frame,numeric
mcd(object, dates)
# S4 method for matrix,numeric
mcd(object, dates)
Arguments
- object
A length-\(p\)
numeric
vector, an \(m \times p\)numeric
matrix
ordata.frame
of count data (absolute frequencies).- dates
A length-\(p\)
numeric
vector of dates expressed in CE years (BCE years must be given as negative numbers).- ...
Currently not used.
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.
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).
References
South, S. A. (1977). Method and Theory in Historical Archaeology. New York: Academic Press.
See also
plot(), bootstrap(), jackknife(), simulate()
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)
head(mc_dates)
#> LZ0789 LZ0783 LZ0782 LZ0778 LZ0777 LZ0776
#> 1207 1128 1100 1227 1238 1028
## Plot
plot(mc_dates)
# \donttest{
## Bootstrap resampling
boot <- bootstrap(mc_dates, n = 30)
head(boot)
#> original mean bias error
#> LZ0789 1207 NaN NaN NA
#> LZ0783 1128 1128.733 0.7333333 18.31889
#> LZ0782 1100 NaN NaN NA
#> LZ0778 1227 1224.333 -2.6666667 35.95431
#> LZ0777 1238 NaN NaN NA
#> LZ0776 1028 973.600 -54.4000000 109.87096
## Jackknife resampling
jack <- jackknife(mc_dates)
head(jack)
#> original mean bias error
#> LZ0789 1207 1206.833 -2.833333 32.47264
#> LZ0783 1128 1128.111 1.888889 21.19778
#> LZ0782 1100 NaN NaN NaN
#> LZ0778 1227 1226.778 -3.777778 22.52269
#> LZ0777 1238 NaN NaN NaN
#> LZ0776 1028 1024.889 -52.888889 127.64539
## Simulation
sim <- simulate(mc_dates, n = 30, interval = "percentiles")
plot(sim)
# }