• bootstrap() generate bootstrap estimations of an event.

• jackknife() generate jackknife estimations of an event.

## Usage

# 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"),
...
)

## Arguments

object

An EventDate object (typically returned by event()).

level

A length-one numeric vector giving the confidence level.

progress

A logical scalar: should a progress bar be displayed?

...

Further arguments to be passed to internal methods.

probs

A numeric vector of probabilities with values in $$[0,1]$$.

n

A non-negative integer specifying the number of bootstrap replications.

## Value

A data.frame.

## 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).

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.

Other resampling methods: resample_mcd