Estimate the entropy (Simpson 1949) of species from abundance or probability data. Several estimators are available to deal with incomplete sampling.
ent_simpson(x, ...)
# S3 method for 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 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
)An object, that may be a numeric vector containing abundances or probabilities, or an object of class abundances or probabilities.
Unused.
An estimator of entropy.
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.
A string containing one of the possible estimators of the probability distribution (see probabilities). Used only for extrapolation.
A string containing one of the possible unveiling methods to estimate the probabilities of the unobserved species (see probabilities). Used only for extrapolation.
An estimator of richness to evaluate the total number of species, see div_richness. Used for interpolation and extrapolation.
The risk level, 5% by default, used to optimize the jackknife order.
The highest jackknife order allowed. Default is 10.
An estimator of sample coverage used by coverage.
If TRUE, a number or a numeric vector is returned rather than a tibble.
If TRUE, the function arguments are verified.
Should be set to FALSE to save time when the arguments have been checked elsewhere.
If TRUE, \(\gamma\) diversity, i.e. diversity of the metacommunity, is computed.
A tibble with the site names, the estimators used and the estimated entropy.
Bias correction requires the number of individuals. See div_hill for non-specific estimators.
Simpson-specific estimator is from Lande (1996) .
Entropy can be estimated at a specified level of interpolation or extrapolation, either a chosen sample size or sample coverage (Chao et al. 2014) , rather than its asymptotic value. See accum_tsallis for details.
Chao A, Gotelli NJ, Hsieh TC, Sander EL, Ma KH, Colwell RK, Ellison AM (2014).
“Rarefaction and Extrapolation with Hill Numbers: A Framework for Sampling and Estimation in Species Diversity Studies.”
Ecological Monographs, 84(1), 45--67.
doi:10.1890/13-0133.1
.
Lande R (1996).
“Statistics and Partitioning of Species Diversity, and Similarity among Multiple Communities.”
Oikos, 76(1), 5--13.
doi:10.2307/3545743
.
Simpson EH (1949).
“Measurement of Diversity.”
Nature, 163(4148), 688.
doi:10.1038/163688a0
.
# 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