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2D Boxcount for 1D signal

Source: R/fd.R
fd_boxcount2D.Rd

2D Boxcount for 1D signal

Usage

fd_boxcount2D(
  y = NA,
  unitSquare = TRUE,
  image2D = NA,
  resolution = 1,
  removeTrend = FALSE,
  polyOrder = 1,
  standardise = c("none", "mean.sd", "median.mad")[1],
  adjustSumOrder = FALSE,
  scaleMin = 0,
  scaleMax = floor(log2(NROW(y) * resolution)),
  scaleS = NA,
  dataMin = 2^(scaleMin + 1),
  maxData = 2^(scaleMax - 1),
  doPlot = FALSE,
  returnPlot = FALSE,
  returnPLAW = FALSE,
  returnInfo = FALSE,
  returnLocalScaling = FALSE,
  silent = FALSE,
  noTitle = FALSE,
  tsName = "y"
)

Arguments

y

A numeric vector or time series object.

unitSquare

Create unit square image of y? This is required for estimating FD of time series (default = TRUE)

image2D

A matrix representing a 2D image, argument y and unitSquare will be ignored (default = NA)

resolution

The resolution used to embed the timeseries in 2D, a factor by which the dimensions the matrix will be multiplied (default = 1)

removeTrend

If TRUE, will call ts_detrend on y (default = FALSE)

polyOrder

Order of polynomial trend to remove if removeTrend = TRUE“

standardise

Standardise y using ts_standardise() with adjustN = FALSE (default = none)

adjustSumOrder

Adjust the order of the time series (by summation or differencing), based on the global scaling exponent, see e.g. https://www.frontiersin.org/files/Articles/23948/fphys-03-00141-r2/image_m/fphys-03-00141-t001.jpgIhlen (2012) (default = `FALSE“)

scaleMin

Minimium scale value (as 2^scale) to use (default = 0)

scaleMax

Maximum scale value (as 2^scale) to use (default = max of log2(nrows) and log2(ncols))

scaleS

If not NA, pass a numeric vector listing the scales (as a power of 2) on which to evaluate the boxcount. Arguments scaleMax, scaleMin, and scaleResolution will be ignored (default = NA)

dataMin

Minimum number of time/data points inside a box for it to be included in the slope estimation (default = 2^scaleMin)

maxData

Maximum number of time/data points inside a box for it to be included in the slope estimation (default = 2^scaleMax)

doPlot

Return the log-log scale versus bulk plot with linear fit (default = TRUE).

returnPlot

Return ggplot2 object (default = FALSE)

returnPLAW

Return the power law data (default = FALSE)

returnInfo

Return all the data used in DFA (default = FALSE)

returnLocalScaling

Return estimates of FD for each scale

silent

Silent-ish mode (default = TRUE)

noTitle

Do not generate a title (only the subtitle)

tsName

Name of y added as a subtitle to the plot (default = y)

Value

The boxcount fractal dimension and the 'local' boxcount fractal dimension

Note

This function was inspired by the Matlab function boxcount.m written by F. Moisy. Fred Hasselman adapted the function for R for the purpose of the unit square boxcount analysis for 1D time series. The original Matlab toolbox has more options and contains more functions (e.g. 1D and 3D boxcount).

Examples


fd_boxcount2D(y = rnorm(100))
#> 
#> 
#> Raterizing time series... Done!
#> Performing 2D boxcount...Done!
#> 
#> ~~~o~~o~~casnet~~o~~o~~~
#> 
#>  2D boxcount of 1D curve 
#> 
#>  Full range (n = 9)
#> FD = 1.52 
#> 
#>  Exclude scales (n = 7)
#> FD = 1.52
#> 
#> ~~~o~~o~~casnet~~o~~o~~~