Estimate the diversity of species from abundance or probability data and a similarity matrix between species. Several estimators are available to deal with incomplete sampling. Bias correction requires the number of individuals.
div_similarity(x, similarities, q = 1, ...)
# S3 method for numeric
div_similarity(
x,
similarities = diag(length(x)),
q = 1,
estimator = c("UnveilJ", "Max", "ChaoShen", "MarconZhang", "UnveilC", "UnveiliC",
"naive"),
probability_estimator = c("Chao2015", "Chao2013", "ChaoShen", "naive"),
unveiling = c("geometric", "uniform", "none"),
jack_alpha = 0.05,
jack_max = 10,
coverage_estimator = c("ZhangHuang", "Chao", "Turing", "Good"),
sample_coverage = NULL,
as_numeric = FALSE,
...,
check_arguments = TRUE
)
# S3 method for species_distribution
div_similarity(
x,
similarities = diag(sum(!colnames(x) %in% non_species_columns)),
q = 1,
estimator = c("UnveilJ", "Max", "ChaoShen", "MarconZhang", "UnveilC", "UnveiliC",
"naive"),
probability_estimator = c("Chao2015", "Chao2013", "ChaoShen", "naive"),
unveiling = c("geometric", "uniform", "none"),
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.
If it is a numeric vector, then its length must equal the dimensions of the
similarities matrix: species are assumed to be in the same order.
A similarity matrix, that can be obtained by fun_similarity. Its default value is the identity matrix.
The order of diversity.
Unused.
An estimator of asymptotic diversity.
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.
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.
The sample coverage of x calculated elsewhere.
Used to calculate the gamma diversity of meta-communities, see details.
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 diversity.
All species of the species_distribution must be found in the matrix of
similarities if it is named.
If it is not, its size must equal the number of species.
Then, the order of species is assumed to be the same as that of the
species_distribution.
Similarity-Based diversity can't be interpolated of extrapolated as of the state of the art.
There are no references for Rd macro \insertAllCites on this help page.
# Similarity matrix
Z <- fun_similarity(paracou_6_fundist)
# Diversity of each community
div_similarity(paracou_6_abd, similarities = Z, q = 2)
#> # A tibble: 4 × 5
#> site weight estimator order diversity
#> <chr> <dbl> <chr> <dbl> <dbl>
#> 1 subplot_1 1.56 UnveilJ 2 1.31
#> 2 subplot_2 1.56 UnveilJ 2 1.33
#> 3 subplot_3 1.56 UnveilJ 2 1.32
#> 4 subplot_4 1.56 UnveilJ 2 1.30
# gamma diversity
div_similarity(paracou_6_abd, similarities = Z, q = 2, gamma = TRUE)
#> # A tibble: 4 × 5
#> site weight estimator order diversity
#> <chr> <dbl> <chr> <dbl> <dbl>
#> 1 subplot_1 1.56 UnveilJ 2 1.31
#> 2 subplot_2 1.56 UnveilJ 2 1.33
#> 3 subplot_3 1.56 UnveilJ 2 1.32
#> 4 subplot_4 1.56 UnveilJ 2 1.30