bootstrap()
generate bootstrap estimations of an event.
jackknife()
generate jackknife estimations of an event.
# S4 method for EventDate
jackknife(object, level = 0.95, progress = getOption("kairos.progress"), ...)# S4 method for EventDate
bootstrap(
object,
level = 0.95,
probs = c(0.05, 0.95),
n = 1000,
progress = getOption("kairos.progress"),
...
)
A data.frame
.
An EventDate
object (typically returned by event()
).
A length-one numeric
vector giving the confidence level.
A logical
scalar: should a progress bar be displayed?
Further arguments to be passed to internal methods.
A numeric
vector of probabilities with values in
\([0,1]\).
A non-negative integer
specifying the number of bootstrap
replications.
N. Frerebeau
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).
bias
The jackknife estimate of bias.
error
The standard error of predicted means.
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.
event()
Other resampling methods:
resample_mcd