tsbootstrap: Generate time-series bootstrap replicates

In jrnold/resamplr: Resampling methods

Description

Generate R bootstrap replicates for time series. The replicate time series can be generated using either fixed or random block lengths.

Usage

tsbootstrap(data, ...)

## S3 method for class 'data.frame'
tsbootstrap(data, R = 1L, size = 1L, m = nrow(data),
  type = "fixed", ...)

## S3 method for class 'grouped_df'
tsbootstrap(data, R = 1L, size = 1L, type = "fixed",
  m = dplyr::n_groups(data), ...)

Arguments

data	A data frame
...	Arguments passed to methods
R	Number of bootstrap replicates
size	If sim is "fixed" then size is the fixed block length used in generating the replicate time series. If sim is "geom" then size is the mean of the geometric distribution used to generate the block lengths. size should be a positive integer less than the number of rows (data.frame) or groups grouped_df in data.
m	Lengths of time-series replicates
type	Type of simulation used to generate the replicate time series. The possible input values are "fixed" (block resampling with fixed block lengths of size), "geom" (block resampling with block lengths having a geometric distribution with mean size).

Details

If type is "fixed" then each replicate time series is found by taking blocks of length size, from the original time series and putting them end-to-end until a new series of length m is created. When type is "geom", a similar approach is taken except that the block lengths are sampled from a geometric distribution with mean size.

Value

A data frame with R rows and the following columns:

train

A list of resample objects. Training sets.

test

A list of resample objects. Test sets.

.id

An integer vector of identifiers

Methods (by class)

• data.frame: Generate time series replicates using blocks of rows in the data frame.

• grouped_df: Generate time series replicates using blocks of groups in the grouped data frame.

References

• Kunsch, H.R. (1989) The jackknife and the bootstrap for general stationary observations. Annals of Statistics

• Davison, A.C. and Hinkley, D.V. (1997) Bootstrap Methods and Their Application. Cambridge University Press.

• Politis, D.N. and Romano, J.P. (1994) "The stationary bootstrap. Journal of the American Statistical Association."

• Angelo Canty and Brian Ripley (2016). boot: Bootstrap R (S-Plus) Functions. R package version 1.3-18.

See Also

The boot function tsboot, from which this function is derived.

jrnold/resamplr documentation built on May 20, 2019, 1:05 a.m.