# wpt.test: Testing the Wavelet Packet Tree for White Noise In waveslim: Basic wavelet routines for one-, two- and three-dimensional signal processing

## Description

A wavelet packet tree, from the discrete wavelet packet transform (DWPT), is tested node-by-node for white noise. This is the first step in selecting an orthonormal basis for the DWPT.

## Usage

 ```1 2 3 4``` ```cpgram.test(y, p = 0.05, taper = 0.1) css.test(y) entropy.test(y) portmanteau.test(y, p = 0.05, type = "Box-Pierce") ```

## Arguments

 `y` wavelet packet tree (from the DWPT) `p` significance level `taper` weight of cosine bell taper (`cpgram.test` only) `type` `"Box-Pierce"` and `other` recognized (`portmanteau.test` only)

## Details

Top-down recursive testing of the wavelet packet tree is

## Value

Boolean vector of the same length as the number of nodes in the wavelet packet tree.

B. Whitcher

## References

Brockwell and Davis (1991) Time Series: Theory and Methods, (2nd. edition), Springer-Verlag.

Brown, Durbin and Evans (1975) Techniques for testing the constancy of regression relationships over time, Journal of the Royal Statistical Society B, 37, 149-163.

Percival, D. B., and A. T. Walden (1993) Spectral Analysis for Physical Applications: Multitaper and Conventional Univariate Techniques, Cambridge University Press.

`ortho.basis`.
 ``` 1 2 3 4 5 6 7 8 9 10``` ```data(mexm) J <- 6 wf <- "la8" mexm.dwpt <- dwpt(mexm[-(1:4)], wf, J) ## Not implemented yet ## plot.dwpt(x.dwpt, J) mexm.dwpt.bw <- dwpt.brick.wall(mexm.dwpt, wf, 6, method="dwpt") mexm.tree <- ortho.basis(portmanteau.test(mexm.dwpt.bw, p=0.025)) ## Not implemented yet ## plot.basis(mexm.tree) ```