Diversity and Entropy Accumulation Curves represent the accumulation of entropy with respect to the sample size.

accum_ent_phylo(x, ...)

# S3 method for class 'numeric'
accum_ent_phylo(
  x,
  tree,
  q = 0,
  normalize = TRUE,
  levels = NULL,
  probability_estimator = c("Chao2015", "Chao2013", "ChaoShen", "naive"),
  unveiling = c("geometric", "uniform", "none"),
  richness_estimator = c("rarefy", "jackknife", "iChao1", "Chao1", "naive"),
  jack_alpha = 0.05,
  jack_max = 10,
  coverage_estimator = c("ZhangHuang", "Chao", "Turing", "Good"),
  n_simulations = 0,
  alpha = 0.05,
  show_progress = TRUE,
  ...,
  check_arguments = TRUE
)

# S3 method for class 'abundances'
accum_ent_phylo(
  x,
  tree,
  q = 0,
  normalize = TRUE,
  levels = NULL,
  probability_estimator = c("Chao2015", "Chao2013", "ChaoShen", "naive"),
  unveiling = c("geometric", "uniform", "none"),
  richness_estimator = c("rarefy", "jackknife", "iChao1", "Chao1", "naive"),
  jack_alpha = 0.05,
  jack_max = 10,
  coverage_estimator = c("ZhangHuang", "Chao", "Turing", "Good"),
  gamma = FALSE,
  n_simulations = 0,
  alpha = 0.05,
  show_progress = TRUE,
  ...,
  check_arguments = TRUE
)

accum_div_phylo(x, ...)

# S3 method for class 'numeric'
accum_div_phylo(
  x,
  tree,
  q = 0,
  normalize = TRUE,
  levels = NULL,
  probability_estimator = c("Chao2015", "Chao2013", "ChaoShen", "naive"),
  unveiling = c("geometric", "uniform", "none"),
  richness_estimator = c("rarefy", "jackknife", "iChao1", "Chao1", "naive"),
  jack_alpha = 0.05,
  jack_max = 10,
  coverage_estimator = c("ZhangHuang", "Chao", "Turing", "Good"),
  n_simulations = 0,
  alpha = 0.05,
  show_progress = TRUE,
  ...,
  check_arguments = TRUE
)

# S3 method for class 'abundances'
accum_div_phylo(
  x,
  tree,
  q = 0,
  normalize = TRUE,
  levels = NULL,
  probability_estimator = c("Chao2015", "Chao2013", "ChaoShen", "naive"),
  unveiling = c("geometric", "uniform", "none"),
  richness_estimator = c("rarefy", "jackknife", "iChao1", "Chao1", "naive"),
  jack_alpha = 0.05,
  jack_max = 10,
  coverage_estimator = c("ZhangHuang", "Chao", "Turing", "Good"),
  gamma = FALSE,
  n_simulations = 0,
  alpha = 0.05,
  show_progress = TRUE,
  ...,
  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.

tree

An ultrametric, phylogenetic tree. May be an object of class phylo_divent, ape::phylo, ade4::phylog or stats::hclust.

q

The order of diversity.

normalize

If TRUE, phylogenetic is normalized: the height of the tree is set to 1.

levels

The levels, i.e. the sample sizes of interpolation or extrapolation: a vector of integer values.

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.

n_simulations

The number of simulations used to estimate the confidence envelope.

alpha

The risk level, 5% by default.

show_progress

If TRUE, a progress bar is shown during long computations.

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 accumulated entropy or diversity at each level of sampling effort.

Details

accum_ent_phylo() or accum_div_phylo() estimate the phylogenetic diversity or entropy accumulation curve of a distribution. See ent_tsallis for details about the computation of entropy at each level of interpolation and extrapolation.

In accumulation curves, extrapolation if done by estimating the asymptotic distribution of the community and estimating entropy at different levels by interpolation.

Interpolation and extrapolation of integer orders of diversity are from Chao2014;textualdivent. The asymptotic richness is adjusted so that the extrapolated part of the accumulation joins the observed value at the sample size.

"accumulation" objects can be plotted. They generalize the classical Species Accumulation Curves (SAC) which are diversity accumulation of order \(q=0\).

References

Examples

# Richness accumulation up to the sample size. 
# 100 simulations only to save time.
autoplot(
  accum_div_phylo(mock_3sp_abd, tree = mock_3sp_tree, n_simulations = 100)
)