fd.dfa: Detrended Fluctuation Analysis (DFA)
In FredHasselman/nlRtsa: nonlineaR time seRies analysis.

Description Usage Arguments Value Author(s) References See Also

fd.dfa

## S3 method for class 'dfa'
fd(y, fs = NULL, dtrend = "poly1", normalize = FALSE,
  sum.order = 1, scale.max = trunc(length(y)/4), scale.min = 4,
  scale.ratio = 2^(1/4), overlap = 0, plot = FALSE)

`y`	A numeric vector or time series object.
`normalize`	Normalize the series (default).
`plot`	Return the log-log spectrum with linear fit (default).
`detrend`	Subtract linear trend from the series (default).
`dmethod`	Method to use for detrending, see `DFA`.

Estimate of Hurst exponent (slope of log(bin) vs. log(RMSE)) and an FD estimate based on Hasselman(2013) A list object containing:

A data matrix PLAW with columns freq.norm, size and bulk.
Estimate of scaling exponent sap based on a fit over the standard range (fullRange), or on a user defined range fitRange.
Estimate of the the Fractal Dimension (FD) using conversion formula's reported in Hasselman(2013).
Information output by various functions.

Fred Hasselman

Hasselman, F. (2013). When the blind curve is finite: dimension estimation and model inference based on empirical waveforms. Frontiers in Physiology, 4, 75. http://doi.org/10.3389/fphys.2013.00075

Other FD estimators: fd.psd, fd.sda

FredHasselman/nlRtsa documentation built on May 6, 2019, 5:07 p.m.