MCD Plot
Usage
# S4 method for class '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 class '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 (seecalendar()
).- 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 ifinterval
is not "range
".
Value
plot()
is called it for its side-effects: it results in a graphic being
displayed (invisibly returns x
).
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))
#> 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 NaN NaN NA
#> LZ0783 796.6667 858.4295 61.7628395 121.16457
#> LZ0782 797.5000 871.5139 74.0139084 98.12367
#> LZ0778 952.5862 952.3055 -0.2807372 109.08688
#> LZ0777 996.2963 1011.9250 15.6286638 87.38330
#> LZ0776 1016.0714 1012.1865 -3.8849198 75.93731
## 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)