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 |

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 |

Megan Heyman

For an overview of the wavelet methodology used in `wavethresh`

, see "Wavelet Methods for Statistics in R," (Nason, 2008).

1 2 3 4 5 6 7 | ```
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
```

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