Generate jackknife estimations of an MCD.
Usage
# S4 method for class 'MeanDate'
jackknife(object, f = NULL, calendar = get_calendar())
Arguments
- object
- 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 (seeaion::calendar()
).
Value
If f
is NULL
, jackknife()
returns 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 jackknife estimate of mean.
bias
The jackknife estimate of bias.
error
The jackknife estimate of standard erro.
See also
Other resampling methods:
bootstrap.EventDate
,
bootstrap.MeanDate
,
jackknife.EventDate
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 753.9931 -3.298611 13.33587 727.6042 777.0833
#> LZ0783 796.6667 793.8889 -2.777778 29.43785 724.1667 855.8333
#> LZ0782 797.5000 795.9118 -1.588235 10.04839 774.7059 818.0882
#> LZ0778 952.5862 944.6983 -7.887931 39.95552 856.4655 1041.3793
#> LZ0777 996.2963 1002.0525 5.756173 22.96437 933.7963 1028.0093
#> LZ0776 1016.0714 1021.8452 5.773810 45.89424 926.7857 1141.9643
## 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)))