# modwt: Maximum Overlap Discrete Wavelet Transform In wv: Wavelet Variance

## Description

Calculates the coefficients for the discrete wavelet transformation

## Usage

 `1` ```modwt(x, nlevels = floor(log2(length(x) - 1)), filter = "haar") ```

## Arguments

 `x` A `vector` with dimensions N x 1. `nlevels` A `integer` indicating the J levels of decomposition. `filter` A `string` indicating the filter name

## Details

Performs a level J decomposition of the time series using the pyramid algorithm. The default J is determined by floor(log2(length(x)))

## Value

A `field<vec>` that contains the wavelet coefficients for each decomposition level

## Author(s)

James Balamuta, Justin Lee and Stephane Guerrier

## Examples

 ```1 2 3 4 5 6 7``` ```set.seed(999) x = rnorm(100) ret = modwt(x) summary(ret) plot(ret) ```

### Example output

```Results of MODWT using haar filter with 6 levels:
Displaying only the first 6 coefficients...
Level 1 Wavelet Coefficients
-0.5154097 1.053872 -0.2625567 -0.2736885 -0.1443587 -0.6563173 ...
Level 2 Wavelet Coefficients
0.6648886 0.1275349 -0.4771462 -0.6093615 -0.5755298 0.05253529 ...
Level 3 Wavelet Coefficients
-0.4324668 -0.5193264 -0.6820166 0.05272893 0.4199327 0.7445505 ...
Level 4 Wavelet Coefficients
0.2871724 0.3948033 0.4591856 0.3995357 0.2700206 0.05856007 ...
Level 5 Wavelet Coefficients
0.05044408 0.04297379 0.02943181 -0.009765617 0.06226712 0.1188075 ...
Level 6 Wavelet Coefficients
0.0744104 0.06411294 -0.008083629 -0.009948806 0.05591394 0.05663235 ...
```

wv documentation built on Jan. 17, 2020, 1:07 a.m.