mousetRajectory (version 0.2.1)

sampen: Sample entropy

Description

Computes the sample entropy (sampen), as given by Richman & Moorman (2000), tools:::Rd_expr_doi("10.1152/ajpheart.2000.278.6.H2039").

Usage

sampen(
  timeseries_array,
  dimensions = 2,
  tolerance = 0.2,
  standardize = TRUE,
  use_diff = FALSE
)

Value

Single number indicating the sampen for the given parameters (0 to +Inf).

Arguments

timeseries_array

Array of numbers over which the sampen is to be computed.

dimensions

Number of embedding dimensions for which to compute the sampen. Sometimes also called "template length".

tolerance

Tolerance for the comparisons of two number sequences.

standardize

Whether to standardize the timeseries_array.

use_diff

Whether to use the differences between adjacent points.

Details

As suggested by Richman & Moorman (2000), tools:::Rd_expr_doi("10.1152/ajpheart.2000.278.6.H2039"), the last possible vector of length dimensions is not considered because it has no corresponding vector of length dimensions + 1, ensuring a sampen estimation with a low bias introduced by the length of the timeseries_array. The function was deliberately implemented in R with C-style code. While this makes the function rather slow for large timeseries_arrays, it enables maximal transparency. For an overview over faster sampen functions in R that, however, are distributed in binary or need source compilation, see Chen et al. (2019), tools:::Rd_expr_doi("10.1093/biomethods/bpz016").

References

Wirth, R., Foerster, A., Kunde, W., & Pfister, R. (2020). Design choices: Empirical recommendations for designing two-dimensional finger tracking experiments. Behavior Research Methods, 52, 2394 - 2416. tools:::Rd_expr_doi("10.3758/s13428-020-01409-0")

Examples

x_vals <- rep(c(0, 0, 0, 0, 0, 1), 20)
sampen(x_vals, dimensions = 1, tolerance = 1 / 2, standardize = FALSE)
sampen(x_vals, dimensions = 3, tolerance = 1 / 2, standardize = FALSE)
sampen(x_vals,
  dimensions = 3, tolerance = 1 / 2, standardize = FALSE,
  use_diff = TRUE
)
sampen(x_vals, dimensions = 3, tolerance = 1, standardize = FALSE)