wavDictionary: Constructor function for objects of class wavDictionary

Description Usage Arguments Details Value S3 METHODS See Also Examples

Description

Packs input information regarding a discrete wavelet transform into a dictionary list.

Usage

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wavDictionary(wavelet, dual, decimate, n.sample,
    attr.x, n.levels, boundary, conv, filters,
    fast, is.complex)

Arguments

wavelet

a character string denoting the type of wavelet used in the transform.

dual

a logical value. If TRUE, it signifies that a dual transform was performed.

decimate

a logical value. If TRUE, it signifies that a decimated transform was performed.

n.sample

an integer representing the number of samples in the original time series.

attr.x

a list of additional (arbitrary) attributes to append onto the output object.

n.levels

an integer denoting the number of decomposition levels.

boundary

a character string denoting the boundary extension type used in transform. Supported values are "zero", "periodic", "reflection", and "continue".

conv

a logical value. If TRUE, it signifies that a convolution style transform was performed (as opposed to correlation style).

filters

a list of vectors named "scaling" and "wavelet" containing the scaling and wavelet filter coefficients, respectively.

fast

a logical value. If TRUE, it signifies that a fast pyramidal scheme was used to develop the decimated transform as opposed to calculating the transform coefficients via an explicit matrix multiplication of the wavelet transform matrix and the original time series.

is.complex

a logical value. If TRUE, it signifies the transform was complex-valued.

Details

Used internally by the wavMODWT and wavDWT functions to package the transform contents into a dictionary list.

Value

an object of class wavDictionary.

S3 METHODS

print

print the dictionary.

Usage: print(x)

See Also

wavDWT, wavMODWT.

Examples

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## create a faux wavelet dictionary 
wavelet <- "s8"
wavDictionary(wavelet=wavelet, dual=FALSE,
    decimate=FALSE, n.sample=1024,
    attr.x=NULL, n.levels=3,
    boundary="periodic", conv=TRUE,
    filters=wavDaubechies(wavelet),
    fast=TRUE, is.complex=FALSE)


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