SSP: Estimated self-similarity parameter

In GGIR: Raw Accelerometer Data Analysis

Estimated self-similarity parameter

Description

This function estimates the self-similarity parameter (SSP), also known as scaling exponent or alpha.

Usage

  SSP(data,scale = 2^(1/8),box_size = 4,m=1)

Arguments

data	Univariate time series (must be a vector or data frame)
scale	Specifies the ratio between successive box sizes (by default scale = 2^(1/8))
box_size	Vector of box sizes (must be used in conjunction with scale = "F")
m	An integer of the polynomial order for the detrending (by default m=1)

Details

The DFA fluctuation can be computed in a geometric scale or for different choices of boxes sizes.

Value

Estimated alpha is a real number between zero and two.

Note

It is not possible estimating alpha for multiple time series at once.

Author(s)

Ian Meneghel Danilevicz <[e-mail]> Victor Barreto Mesquita <[e-mail]>

References

C.-K. Peng, S.V. Buldyrev, S. Havlin, M. Simons, H.E. Stanley, A.L. Goldberger Phys. Rev. E, 49 (1994), p. 1685 Mesquita, Victor & Filho, Florencio & Rodrigues, Paulo. (2020). Detection of crossover points in detrended fluctuation analysis: An application to EEG signals of patients with epilepsy. Bioinformatics. 10.1093/bioinformatics/btaa955.

Examples

  # Estimate self-similarity of a very known time series available on R base: the sunspot.year.
  # Then the spend time with each method is compared.
  ## Not run: 
    ssp = SSP(sunspot.year)
  
## End(Not run)

GGIR documentation built on April 3, 2025, 6 p.m.