retrieveBootstrapSample: Construct the bootstrap data series from wavelet coefficients
In WiSEBoot: Wild Scale-Enhanced Bootstrap

Description Usage Arguments Details Value Author(s) References See Also Examples

Use the wavelet coefficients from the selected WiSE bootstrap model to construct smooth bootstrap series in the original time/space domain. This function may be of little use and creates large array objects within R. The wavelet coefficients are a preferable representation of the data, as they are sparse and contain the signal information.

1	retrieveBootstrapSample(WiSEObj)

WiSEObj

an object obtained from WiSEBoot. The "periodic" boundary condition for wavelets is required.

The wavethresh package is used to perform the inverse wavelet decomposition of each bootstrap sample of wavelet coefficients.

The bootstrap series will be smoothed to the selected threshold level, J0=j (see WiSEBoot).

BootSample

an array. The rows contain each bootstrap sample – dimension is the R supplied to the original WiSEBoot call. The columns contain the observations in the original time/space domain – dimension is the length of the data series, T. The 3rd dimension corresponds to the data series – If the user supplied a matrix of more than 1 data series, a bootstrap sample is generated for each series, in the order of the data series supplied.

Megan Heyman

For an overview of the wavelet methodology used in wavethresh, see "Wavelet Methods for Statistics in R," (Nason, 2008).

WiSEBoot, wavethresh-package

someData <- rnorm(2^5)

##Bootstrap sample of size 10 is not recommended. For demonstration only.
bootInfo <- WiSEBoot(someData, R=10, J0=2)
bootSeries <- retrieveBootstrapSample(bootInfo)$BootSample

bootSeries[1, , 1] #this is the first bootstrap series

 [1]  2.204351342  1.937835957  1.515348946  1.042177690  0.609267932
 [6]  0.331008928  0.177677229  0.062991827  0.039305356  0.128793110
[11]  0.271424095  0.443298897  0.434655578  0.053224648 -0.552051395
[16] -1.213780103 -1.626626823 -1.483350038 -0.961275506 -0.310489541
[21]  0.181555503  0.206816608 -0.091088505 -0.470670746 -0.760642634
[26] -0.763391857 -0.551838614 -0.289698203 -0.008726823  0.240752524
[31]  0.422044078  0.544960956