# DOG: Continuous Wavelet Transform with derivative of Gaussian In Rwave: Time-Frequency Analysis of 1-D Signals

 DOG R Documentation

## Continuous Wavelet Transform with derivative of Gaussian

### Description

Computes the continuous wavelet transform with for (complex-valued) derivative of Gaussian wavelets.

### Usage

```DOG(input, noctave, nvoice=1, moments, twoD=TRUE, plot=TRUE)
```

### Arguments

 `input` input signal (possibly complex-valued). `noctave` number of powers of 2 for the scale variable. `moments` number of vanishing moments of the wavelet (order of the derivative). `nvoice` number of scales in each octave (i.e. between two consecutive powers of 2) `twoD` logical variable set to T to organize the output as a 2D array (signal_size x nb_scales), otherwise, the output is a 3D array (signal_size x noctave x nvoice) `plot` if set to T, display the modulus of the continuous wavelet transform on the graphic device

### Details

The output contains the (complex) values of the wavelet transform of the input signal. The format of the output can be

2D array (signal_size x nb_scales)

3D array (signal_size x noctave x nvoice)

### Value

continuous (complex) wavelet transform

### References

See discussions in the text of “Practical Time-Frequency Analysis”.

`cwt`, `cwtp`, `cwtsquiz`, `cgt`.

### Examples

``` x <- 1:512
chirp <- sin(2*pi * (x + 0.002 * (x-256)^2 ) / 16)

DOG(chirp, noctave=5, nvoice=12, 3, twoD=TRUE, plot=TRUE)

```

Rwave documentation built on Oct. 22, 2022, 1:05 a.m.