ent_rao {divent} | R Documentation |
Rao's Quadratic Entropy of a Community
Description
Estimate the quadratic entropy (Rao 1982) of species from abundance or probability data. An estimator (Lande 1996) is available to deal with incomplete sampling.
Usage
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
)
Arguments
x |
An object, that may be a numeric vector containing abundances or probabilities, or an object of class abundances or probabilities. |
... |
Unused. |
distances |
A distance matrix or an object of class stats::dist |
tree |
An ultrametric, phylogenetic tree. May be an object of class phylo_divent, ape::phylo, ade4::phylog or stats::hclust. |
normalize |
If |
estimator |
An estimator of entropy. |
as_numeric |
If |
check_arguments |
If |
gamma |
If |
Details
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.
Value
A tibble with the site names, the estimators used and the estimated entropy.
References
Lande R (1996).
“Statistics and Partitioning of Species Diversity, and Similarity among Multiple Communities.”
Oikos, 76(1), 5–13.
doi:10.2307/3545743.
Rao CR (1982).
“Diversity and Dissimilarity Coefficients: A Unified Approach.”
Theoretical Population Biology, 21, 24–43.
doi:10.1016/0040-5809(82)90004-1.
Examples
# Entropy of each community
ent_rao(paracou_6_abd, tree = paracou_6_taxo)
# Similar to (but estimators are not the same)
ent_phylo(paracou_6_abd, tree = paracou_6_taxo, q = 2)
# Functional entropy
ent_rao(paracou_6_abd, distances = paracou_6_fundist)
# gamma entropy
ent_rao(paracou_6_abd, tree = paracou_6_taxo, gamma = TRUE)