Estimate the entropy Simpson1949divent of species from abundance or probability data. Several estimators are available to deal with incomplete sampling.

ent_simpson(x, ...)

# S3 method for class 'numeric'
ent_simpson(
  x,
  estimator = c("Lande", "UnveilJ", "ChaoJost", "ChaoShen", "GenCov", "Grassberger",
    "Marcon", "UnveilC", "UnveiliC", "ZhangGrabchak", "naive", "Bonachela", "Holste"),
  level = NULL,
  probability_estimator = c("Chao2015", "Chao2013", "ChaoShen", "naive"),
  unveiling = c("geometric", "uniform", "none"),
  richness_estimator = c("jackknife", "iChao1", "Chao1", "naive"),
  jack_alpha = 0.05,
  jack_max = 10,
  coverage_estimator = c("ZhangHuang", "Chao", "Turing", "Good"),
  as_numeric = FALSE,
  ...,
  check_arguments = TRUE
)

# S3 method for class 'species_distribution'
ent_simpson(
  x,
  estimator = c("Lande", "UnveilJ", "ChaoJost", "ChaoShen", "GenCov", "Grassberger",
    "Marcon", "UnveilC", "UnveiliC", "ZhangGrabchak", "naive", "Bonachela", "Holste"),
  level = NULL,
  probability_estimator = c("Chao2015", "Chao2013", "ChaoShen", "naive"),
  unveiling = c("geometric", "uniform", "none"),
  richness_estimator = c("jackknife", "iChao1", "Chao1", "naive"),
  jack_alpha = 0.05,
  jack_max = 10,
  coverage_estimator = c("ZhangHuang", "Chao", "Turing", "Good"),
  gamma = 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.

...

Unused.

estimator

An estimator of entropy.

level

The level of interpolation or extrapolation. It may be a sample size (an integer) or a sample coverage (a number between 0 and 1). If not NULL, the asymptotic estimator is ignored.

probability_estimator

A string containing one of the possible estimators of the probability distribution (see probabilities). Used only for extrapolation.

unveiling

A string containing one of the possible unveiling methods to estimate the probabilities of the unobserved species (see probabilities). Used only for extrapolation.

richness_estimator

An estimator of richness to evaluate the total number of species, see div_richness. Used for interpolation and extrapolation.

jack_alpha

The risk level, 5% by default, used to optimize the jackknife order.

jack_max

The highest jackknife order allowed. Default is 10.

coverage_estimator

An estimator of sample coverage used by coverage.

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.

gamma

If TRUE, \(\gamma\) diversity, i.e. diversity of the metacommunity, is computed.

Value

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

Details

Bias correction requires the number of individuals. See div_hill for non-specific estimators.

Simpson-specific estimator is from Lande1996;textualdivent.

Entropy can be estimated at a specified level of interpolation or extrapolation, either a chosen sample size or sample coverage Chao2014divent, rather than its asymptotic value. See accum_tsallis for details.

References

Examples

# Entropy of each community
ent_simpson(paracou_6_abd)
#> # A tibble: 4 × 5
#>   site      weight estimator order entropy
#>   <chr>      <dbl> <chr>     <dbl>   <dbl>
#> 1 subplot_1   1.56 Lande         2   0.976
#> 2 subplot_2   1.56 Lande         2   0.978
#> 3 subplot_3   1.56 Lande         2   0.980
#> 4 subplot_4   1.56 Lande         2   0.972
# gamma entropy
ent_simpson(paracou_6_abd, gamma = TRUE)
#> # A tibble: 1 × 4
#>   site          estimator order entropy
#>   <chr>         <chr>     <dbl>   <dbl>
#> 1 Metacommunity Lande         2   0.979

# At 80% coverage
ent_simpson(paracou_6_abd, level = 0.8)
#> # A tibble: 4 × 6
#>   site      weight estimator order level entropy
#>   <chr>      <dbl> <chr>     <dbl> <dbl>   <dbl>
#> 1 subplot_1   1.56 Chao2014      2   304   0.973
#> 2 subplot_2   1.56 Chao2014      2   347   0.975
#> 3 subplot_3   1.56 Chao2014      2   333   0.977
#> 4 subplot_4   1.56 Chao2014      2   303   0.969