DFA (version 0.1.0)

DeltaDFA: DeltaDFA

Description

Applies the log-amplitude Detrended Fluctuation Analysis (DFA) to time series.

Usage

DeltaDFA(file,file2,m=1)

Arguments

file

Univariate time series (must be a vector)

file2

Univariate time series (must be a vector)

m

An integer of the polynomial order for the detrending (by default m=1).

Value

box_size

Size of the windows computed in a geometric scale.

Log_n

decimal logarithmic of the box size.

DeltaDFA

log-amplitude Fluctuation function.

Details

The DFA log-amplitude fluctuation is computed in a geometric scale.

References

G. F. Zebende, F. M. Oliveira Filho, J. A. L. Cruz, Auto-correlationin the motor/imaginary human eeg signals: A vision about the fdfafluctuations, PloS one 12 (9) (2017).

F. Oliveira Filho, J. L. Cruz, G. Zebende, Analysis of the eeg bio-signalsduring the reading task by dfa method, Physica A: Statistical Mechanicsand its Applications 525 (2019) 664-671.

S. R. Hirekhan, R. R. Manthalkar, The detrended fluctuation and cross-correlation analysis of eeg signals, International Journal of IntelligentSystems Design and Computing 2 (2) (2018) .

Examples

# NOT RUN {
library(DFA)
data("data1")
data("data2")
file = data1
file2= data2

DeltaDFA(file,file2,m=1)
# }
# NOT RUN {
# Example with different polynomial order.

library(DFA)
data("data3")
data("data4")
file = data3
file2= data4

DeltaDFA(file,file2,m=2)

# }