divent (version 0.5-3)

div_gen_simpson: Generalized Simpson's Diversity

Description

Estimate the diversity sensu stricto, i.e. the effective number of species Grabchak2016divent from abundance or probability data.

Usage

div_gen_simpson(x, k = 1, ...)
# S3 method for numeric
div_gen_simpson(
  x,
  k = 1,
  estimator = c("Zhang", "naive"),
  as_numeric = FALSE,
  ...,
  check_arguments = TRUE
)
# S3 method for species_distribution
div_gen_simpson(
  x,
  k = 1,
  estimator = c("Zhang", "naive"),
  as_numeric = FALSE,
  ...,
  check_arguments = TRUE
)

Value

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

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 asymptotic diversity.

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.

Estimation techniques are from Zhang2014;textualdivent. It is limited to orders \(k\) less than or equal to the number of individuals in the community.

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

References

See Also

ent_gen_simpson

Examples

# Diversity of each community
div_gen_simpson(paracou_6_abd, k = 50)