resample_event {kairos} | R Documentation |
Resample Event Dates
Description
-
bootstrap()
generate bootstrap estimations of an event. -
jackknife()
generate jackknife estimations of an event.
Usage
## S4 method for signature 'EventDate'
jackknife(
object,
level = 0.95,
calendar = getOption("kairos.calendar"),
progress = getOption("kairos.progress"),
verbose = getOption("kairos.verbose"),
...
)
## S4 method for signature 'EventDate'
bootstrap(
object,
level = 0.95,
probs = c(0.05, 0.95),
n = 1000,
calendar = getOption("kairos.calendar"),
progress = getOption("kairos.progress"),
...
)
Arguments
object |
|
level |
A length-one |
calendar |
An |
progress |
A |
verbose |
A |
... |
Further arguments to be passed to internal methods. |
probs |
A |
n |
A non-negative |
Details
If jackknife()
is used, one type/fabric is removed at a
time and all statistics are recalculated. In this way, one can assess
whether certain type/fabric has a substantial influence on the date
estimate.
A three columns data.frame
is returned, giving the results of
the resampling procedure (jackknifing fabrics) for each assemblage (in rows)
with the following columns:
mean
The jackknife mean (event date).
lower
The lower boundary of the confidence interval.
upper
The upper boundary of the confidence interval.
If bootstrap()
is used, a large number of new bootstrap assemblages is
created, with the same sample size, by resampling each of the original
assemblage with replacement. Then, examination of the bootstrap statistics
makes it possible to pinpoint assemblages that require further
investigation.
A five columns data.frame
is returned, giving the bootstrap
distribution statistics for each replicated assemblage (in rows)
with the following columns:
min
Minimum value.
mean
Mean value (event date).
max
Maximum value.
Q5
Sample quantile to 0.05 probability.
Q95
Sample quantile to 0.95 probability.
Value
A data.frame
.
Author(s)
N. Frerebeau
See Also
Other event date tools:
density_event()
,
event()
,
model_event
,
plot_event
,
predict_event()