Multi-Scale Entropy

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)
#> 
#> ~~~o~~o~~casnet~~o~~o~~~
#> 
#>  Multi Scale Entropy 
#> 
#>  Full range (n = 10)
#> Slope = -0.14 
#> 
#>  Fit range (n = 7)
#> Slope = -0.12
#> 
#> ~~~o~~o~~casnet~~o~~o~~~