Simulate Mean Ceramic Dates

Simulates MCD from a multinomial distribution.

Usage

# S4 method for class 'MeanDate'
simulate(object, nsim = 1000, seed = NULL, ...)

Arguments

object

A MeanDate object (typically returned by mcd()).

nsim

A non-negative integer specifying the number of simulations.

seed

An object specifying if and how the random number generator should be initialized (see stats::simulate()).

...

Currently not used.

Value

A SimulationMeanDate object.

Details

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.

See also

mcd()

Other resampling methods: bootstrap.EventDate, bootstrap.MeanDate, jackknife.EventDate, jackknife.MeanDate

Author

N. Frerebeau

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

## Plot
plot(mc_dates)


## Bootstrap resampling
boot <- bootstrap(mc_dates, n = 30)
head(boot)
#>         original      mean     bias     error
#> LZ0789  757.2917  769.8849 12.59326  70.42264
#> LZ0783  796.6667  839.3107 42.64399 122.87193
#> LZ0782  797.5000       NaN      NaN        NA
#> LZ0778  952.5862  984.3342 31.74801 120.11099
#> LZ0777  996.2963 1020.3336 24.03729  81.34437
#> LZ0776 1016.0714 1039.5134 23.44199  65.77519

## 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

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