extractFDAWavelets: Discrete Wavelet transform features.

In mlr: Machine Learning in R

Discrete Wavelet transform features.

Description

The function extracts discrete wavelet transform coefficients from the raw functional data. See wavelets::dwt for more information.

Usage

extractFDAWavelets(filter = "la8", boundary = "periodic")

Arguments

filter	(character(1)) Specifies which filter should be used. Must be one of d|la|bl|c followed by an even number for the level of the filter. The level of the filter needs to be smaller or equal then the time-series length. For more information and acceptable filters see help(wt.filter). Defaults to la8.
boundary	(character(1)) Boundary to be used. “periodic” assumes circular time series, for “reflection” the series is extended to twice its length. Default is “periodic”.

Value

(data.frame).

See Also

Other fda_featextractor: extractFDABsignal(), extractFDADTWKernel(), extractFDAFPCA(), extractFDAFourier(), extractFDAMultiResFeatures(), extractFDATsfeatures()

mlr documentation built on June 22, 2024, 10:51 a.m.