View source: R/ent_gen_simpson.R
ent_gen_simpson | R Documentation |
Estimate the Generalized Simpson's entropy of species from abundance or probability data.
ent_gen_simpson(x, ...)
## S3 method for class 'numeric'
ent_gen_simpson(
x,
k = 1,
estimator = c("Zhang", "naive"),
as_numeric = FALSE,
...,
check_arguments = TRUE
)
## S3 method for class 'species_distribution'
ent_gen_simpson(
x,
k = 1,
estimator = c("Zhang", "naive"),
gamma = FALSE,
as_numeric = FALSE,
...,
check_arguments = TRUE
)
x |
An object, that may be a numeric vector containing abundances or probabilities, or an object of class abundances or probabilities. |
... |
Unused. |
k |
the order of Hurlbert's diversity. |
estimator |
An estimator of entropy. |
as_numeric |
if |
check_arguments |
if |
gamma |
if |
The Generalized Simpson's Entropy \insertCiteZhang2010divent of order k
is,
in the species accumulation curve,the probability for the individual sampled
in rank k + 1
to belong to a new species.
It is a measure of diversity so long as k
is lower than the number
of species \insertCiteGrabchak2016divent.
Bias correction requires the number of individuals.
It is limited to orders r
less than or equal to the number of individuals
in the community \insertCiteZhang2014divent.
Generalized Simpson's diversity cannot be estimated at a specified level of interpolation or extrapolation, and diversity partitioning is not available.
A tibble with the site names, the estimators used and the estimated entropy.
The unbiased estimator is calculated by the EntropyEstimation::GenSimp.z function of the EntropyEstimation package.
div_gen_simpson
#' @references \insertAllCited
# Entropy of each community
ent_gen_simpson(paracou_6_abd, k = 50)
# gamma entropy
ent_gen_simpson(paracou_6_abd, k = 50, gamma = TRUE)
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