inf_MSE: Multi-Scale Entropy

In FredHasselman/casnet: A Toolbox for Studying Complex Adaptive Systems and Networks

Multi-Scale Entropy

Description

Calculate the Multi-Scale (Sample) Entropy of a time series.

Usage

inf_MSE(
  y,
  summaryFunction = c("mean", "median", "min", "max")[1],
  retainLength = FALSE,
  m = 2,
  r = 0.2,
  D = NA,
  fs = NULL,
  transformBefore = TRUE,
  removeTrend = c("no", "poly", "adaptive", "bridge")[1],
  polyOrder = 1,
  standardise = c("none", "mean.sd", "median.mad")[1],
  adjustSumOrder = FALSE,
  scaleMin = 1,
  scaleMax = floor(NROW(y)/10),
  scaleS = NA,
  overlap = 0,
  dataMin = 4,
  relativeEntropy = FALSE,
  doPlot = FALSE,
  returnPlot = FALSE,
  returnPLAW = FALSE,
  returnInfo = FALSE,
  silent = FALSE,
  noTitle = FALSE,
  tsName = "y"
)

Arguments

y	A numeric vector
summaryFunction	How should the data be summarized in the bins? Can be "mean", "median", "min", "max", or, "maxFreq". Value "maxFreq" is for categorical data and will pick the most frequently occurring category within the bin (default = "mean")
retainLength	Return only the bin values (FALSE), or retain the length of the original series? (default = TRUE)
m	The size of the window in which tho evaluate whether a pattern repeats (default = 2)
r	A factor that will determine the threshold for similarity of values, calculated as r x D (default = 0.2)
D	Commonly the standard deviation of the time series, the similarity threshold will be calculated as r x D. Note that if the series is detrended and/or standardised and D = NA the standard deviation will be calculated after the transformations (default = NA)
fs	Sample rate
transformBefore	Detrend/standardise before coarse graining. If set to FALSE, each coarsegrained series will be detrended/standardised separately (default = TRUE)
removeTrend	Method to use for global detrending (default = "poly")
polyOrder	Order of global polynomial trend to remove if removeTrend = "poly". If removeTrend = "adaptive" polynomials 1 to polyOrder will be evaluated and the best fitting curve (R squared) will be removed (default = 1)
standardise	Standardise the series using ts_standardise() with adjustN = FALSE (default = "mean.sd")
adjustSumOrder	Adjust the time series (summation or difference), based on the global scaling exponent, see e.g. Ihlen (2012) (default = FALSE)
scaleMin	Minimum scale (in data points) to use for log-log regression (default = 4)
scaleMax	Maximum scale (in data points) to use for log-log regression. This value will be ignored if dataMin is not NA, in which case bins of size ⁠< dataMin⁠ will be removed (default = stats::nextn(floor(NROW(y)/4), factors = 2))
scaleS	If not NA, it should be a numeric vector listing the scales on which to evaluate the detrended fluctuations. Arguments ⁠scaleMax, scaleMin, scaleResolution⁠ and dataMin will be ignored (default = NA)
overlap	A number in ⁠[0 ... 1]⁠ representing the amount of 'bin overlap' when calculating the fluctuation. This reduces impact of arbitrary time series begin and end points. If length(y) = 1024 and overlap is .5, a scale of 4 will be considered a sliding window of size 4 with step-size floor(.5 * 4) = 2, so for scale 128 step-size will be 64 (default = NA)
relativeEntropy	The relative entropy, SampEn / (-1 * log(1/length(y))) will be returned (default = FALSE)
doPlot	Output the log-log scale versus fluctuation plot with linear fit by calling function plotFD_loglog() (default = TRUE)
returnPlot	Return ggplot2 object (default = FALSE)
returnPLAW	Return the power law data (default = FALSE)
returnInfo	Return all the data used in SDA (default = FALSE)
silent	Silent-ish mode (default = FALSE)
noTitle	Do not generate a title (only the subtitle) (default = FALSE)
tsName	Name of y added as a subtitle to the plot (default = "y")

Value

MSE

Note

The transformation settings (detrending and/or normalisation), transformBefore will determine the value of the product r x D if D = NA.

See Also

Other Information based complexity measures: inf_SampEn()

Examples

y <- rnorm(100)

out <- inf_MSE(y, r=.5, scaleMin=1, scaleMax=10, returnInfo = TRUE, doPlot = FALSE)

FredHasselman/casnet documentation built on April 20, 2024, 3:05 p.m.