Estimates the Mean Ceramic Date of an assemblage.
Arguments
- object
A \(m \times p\)
numeric
matrix
ordata.frame
of count data (absolute frequencies giving the number of individuals for each category, i.e. a contingency table). Adata.frame
will be coerced to anumeric
matrix
viadata.matrix()
.- dates
A length-\(p\)
numeric
vector of dates expressed in years.- ...
Currently not used.
- calendar
An
aion::TimeScale
object specifying the calendar ofdates
(seeaion::calendar()
). Defaults to Gregorian Common Era.
Value
A MeanDate
object.
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.
References
South, S. A. (1977). Method and Theory in Historical Archaeology. New York: Academic Press.
See also
plot()
, bootstrap()
,
jackknife()
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))
#> 26 x 18 x 1 time series observed between 757.291 CE and 1259.38 CE
## Get MCD in years CE
time(mc_dates, calendar = CE())
#> [1] 757.2912 796.6659 797.4991 952.5855 996.2952 1016.0738 1027.5011
#> [8] 1059.5249 1073.6597 1075.5213 1089.5820 1092.8564 1100.0000 1127.7799
#> [15] 1137.1101 1200.0017 1204.3868 1207.1436 1219.4454 1227.3745 1235.4176
#> [22] 1237.5000 1238.8896 1253.1241 1256.2502 1259.3757
## Bootstrap resampling
boot <- bootstrap(mc_dates, n = 30)
#> Warning: Extreme order statistics used as endpoints.
#> Warning: Extreme order statistics used as endpoints.
#> Warning: Extreme order statistics used as endpoints.
#> Warning: Extreme order statistics used as endpoints.
#> Warning: Extreme order statistics used as endpoints.
#> Warning: Extreme order statistics used as endpoints.
#> Warning: Extreme order statistics used as endpoints.
#> Warning: Extreme order statistics used as endpoints.
#> Warning: Extreme order statistics used as endpoints.
#> Warning: Extreme order statistics used as endpoints.
#> Warning: Extreme order statistics used as endpoints.
#> Warning: Extreme order statistics used as endpoints.
#> Warning: Extreme order statistics used as endpoints.
#> Warning: Extreme order statistics used as endpoints.
#> Warning: Extreme order statistics used as endpoints.
#> Warning: Extreme order statistics used as endpoints.
#> Warning: Extreme order statistics used as endpoints.
#> Warning: Extreme order statistics used as endpoints.
#> Warning: Extreme order statistics used as endpoints.
#> Warning: Extreme order statistics used as endpoints.
#> Warning: Extreme order statistics used as endpoints.
#> Warning: Extreme order statistics used as endpoints.
#> Warning: Extreme order statistics used as endpoints.
#> Warning: Extreme order statistics used as endpoints.
#> Warning: Extreme order statistics used as endpoints.
#> Warning: Extreme order statistics used as endpoints.
head(boot)
#> original mean bias error lower upper
#> LZ0789 757.2917 755.6424 -1.649306 14.25382 707.8125 777.0833
#> LZ0783 796.6667 794.1667 -2.500000 31.23112 724.1667 845.0000
#> LZ0782 797.5000 799.9069 2.406863 12.30931 771.7647 826.0294
#> LZ0778 952.5862 956.4943 3.908046 31.53658 896.9828 1007.3276
#> LZ0777 996.2963 997.0833 0.787037 20.73150 953.0093 1040.5093
#> LZ0776 1016.0714 1012.0238 -4.047619 46.18264 933.9286 1133.0357
## Jackknife resampling
jack <- jackknife(mc_dates)
head(jack)
#> original mean bias error
#> LZ0789 757.2917 768.2870 186.921296 207.5535
#> LZ0783 796.6667 806.9974 175.621693 228.0861
#> LZ0782 797.5000 804.1715 113.415558 169.0563
#> LZ0778 952.5862 954.5205 32.882529 138.6064
#> LZ0777 996.2963 996.6640 6.251785 111.0144
#> LZ0776 1016.0714 1017.1652 18.594831 72.6602
## Plot
plot(mc_dates, decreasing = FALSE)
## Add bootstrap confidence intervals
segments(x0 = boot$lower, y0 = seq_len(nrow(boot)),
x1 = boot$upper, y1 = seq_len(nrow(boot)))