SEP_FIM: Fisher-Shannon method

In fishinfo/FiSh: Fisher-Shannon method

Description

Non-parametric estimates of the Shannon Entropy Power (SEP), the Fisher Information Measure (FIM) and the Fisher-Shannon Complexity (FSC), using kernel density estimators with Gaussian kernel.

Usage

SEP_FIM(x, h, log_trsf=FALSE, resol=1000, tol = .Machine$double.eps)

Arguments

x	Univariate data.
h	Value of the bandwidth for the density estimate
log_trsf	Logical flag: if TRUE the data are log-transformed (used for skewed data), in this case the data should be positive. By default, log_trsf = FALSE.
resol	Number of equally-spaced points, over which function approximations are computed and integrated.
tol	A tolerance to avoid dividing by zero values.

Value

A table with one row containing:

• SEP Shannon Entropy Power.

• FIM Fisher Information Measure.

• FSC Fisher-Shannon Complexity

References

F. Guignard, M. Laib, F. Amato, M. Kanevski, Advanced analysis of temporal data using Fisher-Shannon information : theoretical development and application to geoscience

Examples

library(KernSmooth)
x <- rnorm(1000)
h <- dpik(x)
SEP_FIM(x, h)

fishinfo/FiSh documentation built on Jan. 2, 2020, 2:24 p.m.