inf_SampEn: Sample Entropy

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

Sample Entropy

Description

Sample Entropy

Usage

inf_SampEn(
  y,
  m = 2,
  r = 0.2,
  D = NA,
  fs = NULL,
  standardise = c("none", "mean.sd", "median.mad")[1],
  transformBefore = TRUE,
  removeTrend = c("no", "poly", "adaptive", "bridge")[1],
  polyOrder = 1,
  relativeEntropy = FALSE,
  returnInfo = FALSE,
  silent = FALSE
)

Arguments

y	A numeric vector or time series object.
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
standardise	Standardise the series using ts_standardise() with adjustN = FALSE (default = "mean.sd")
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)
relativeEntropy	The relative entropy, SampEn / (-1 * log(1/length(y))) will be returned (default = FALSE)
returnInfo	Return all the data used in SDA (default = FALSE)
silent	Silent-ish mode (default = FALSE)

Value

The sample entropy (SampEn) of the time series y.

See Also

info_MSE

Other Information based complexity measures: inf_MSE()

Examples

y <- rnorm(100)

# Similarity threshold is r x D = 0.2 * sd(y)
inf_SampEn(y)

# Similarity threshold is r = 0.2
inf_SampEn(y, D = 1)

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