Estimate the quadratic entropy Rao1982divent of species from abundance or probability data. An estimator Lande1996divent is available to deal with incomplete sampling.
ent_rao(x, ...)
# S3 method for class 'numeric'
ent_rao(
x,
distances = NULL,
tree = NULL,
normalize = TRUE,
estimator = c("Lande", "naive"),
as_numeric = FALSE,
...,
check_arguments = TRUE
)
# S3 method for class 'species_distribution'
ent_rao(
x,
distances = NULL,
tree = NULL,
normalize = TRUE,
estimator = c("Lande", "naive"),
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.
A distance matrix or an object of class stats::dist
An ultrametric, phylogenetic tree. May be an object of class phylo_divent, ape::phylo, ade4::phylog or stats::hclust.
If TRUE, phylogenetic is normalized: the height of the tree is set to 1.
An estimator of entropy.
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.
Rao's entropy is phylogenetic or similarity-based entropy of order 2.
ent_phylo and ent_similarity with argument q = 2 provide more estimators
and allow estimating entropy at a chosen level.
All species of the species_distribution must be found in the matrix of
distances if it is named.
If it is not or if x is numeric, 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 or its numeric equivalent.
# Entropy of each community
ent_rao(paracou_6_abd, tree = paracou_6_taxo)
#> # A tibble: 4 × 5
#> site weight estimator order entropy
#> <chr> <dbl> <chr> <dbl> <dbl>
#> 1 subplot_1 1.56 Lande 2 0.970
#> 2 subplot_2 1.56 Lande 2 0.977
#> 3 subplot_3 1.56 Lande 2 0.973
#> 4 subplot_4 1.56 Lande 2 0.973
# Similar to (but estimators are not the same)
ent_phylo(paracou_6_abd, tree = paracou_6_taxo, q = 2)
#> # A tibble: 4 × 5
#> site weight estimator q entropy
#> <chr> <dbl> <chr> <dbl> <dbl>
#> 1 subplot_1 1.56 UnveilJ 2 0.943
#> 2 subplot_2 1.56 UnveilJ 2 0.953
#> 3 subplot_3 1.56 UnveilJ 2 0.951
#> 4 subplot_4 1.56 UnveilJ 2 0.939
# Functional entropy
ent_rao(paracou_6_abd, distances = paracou_6_fundist)
#> # A tibble: 4 × 5
#> site weight estimator order entropy
#> <chr> <dbl> <chr> <dbl> <dbl>
#> 1 subplot_1 1.56 Lande 2 0.365
#> 2 subplot_2 1.56 Lande 2 0.393
#> 3 subplot_3 1.56 Lande 2 0.383
#> 4 subplot_4 1.56 Lande 2 0.365
# gamma entropy
ent_rao(paracou_6_abd, tree = paracou_6_taxo, gamma = TRUE)
#> # A tibble: 1 × 4
#> site estimator order entropy
#> <chr> <chr> <dbl> <dbl>
#> 1 Metacommunity Lande 2 0.976