R: Resample Event Dates

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, progress = getOption("kairos.progress"), ...)

## S4 method for signature '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.

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.

Value

A data.frame.

Author(s)

N. Frerebeau