FiSh (version 1.1)

SEP_FIM: 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 in geosciences, 2020, 10.3389/feart.2020.00255Frontiers in Earth Science, 8:255.

Examples

# NOT RUN {
library(KernSmooth)
x <- rnorm(1000)
h <- dpik(x)
SEP_FIM(x, h)



# }