ent_hurlbert: Hurlbert Entropy of a Community

In divent: Entropy Partitioning to Measure Diversity

Hurlbert Entropy of a Community

Description

Estimate the Hurlbert entropy \insertCiteHurlbert1971divent of species from abundance or probability data. Several estimators are available to deal with incomplete sampling.

Usage

ent_hurlbert(x, k = 2, ...)

## S3 method for class 'numeric'
ent_hurlbert(
  x,
  k = 2,
  estimator = c("Hurlbert", "naive"),
  as_numeric = FALSE,
  ...,
  check_arguments = TRUE
)

## S3 method for class 'species_distribution'
ent_hurlbert(
  x,
  k = 2,
  estimator = c("Hurlbert", "naive"),
  as_numeric = FALSE,
  ...,
  check_arguments = TRUE
)

Arguments

x	An object, that may be a numeric vector containing abundances or probabilities, or an object of class abundances or probabilities.
k	the order of Hurlbert's diversity.
...	Unused.
estimator	An estimator of entropy.
as_numeric	if TRUE, a number or a numeric vector is returned rather than a tibble.
check_arguments	if TRUE, the function arguments are verified. Should be set to FALSE to save time when the arguments have been checked elsewhere.

Details

Bias correction requires the number of individuals. See div_hurlbert for estimators.

Hurlbert's entropy cannot be estimated at a specified level of interpolation or extrapolation, and entropy partitioning is not available.

Value

A tibble with the site names, the estimators used and the estimated entropy.

References

\insertAllCited

See Also

div_hurlbert

Examples

# Entropy of each community
ent_hurlbert(paracou_6_abd, k = 2)

divent documentation built on April 3, 2025, 7:40 p.m.