ShannonBeta | R Documentation |
Calculates the Shannon beta entropy of a community belonging to a metacommunity.
ShannonBeta(NorP, NorPexp = NULL, ...)
bcShannonBeta(Ns, Nexp, Correction = "Best", CheckArguments = TRUE)
## S3 method for class 'ProbaVector'
ShannonBeta(NorP, NorPexp = NULL, ...,
CheckArguments = TRUE, Ps = NULL, Pexp = NULL)
## S3 method for class 'AbdVector'
ShannonBeta(NorP, NorPexp = NULL, Correction = "Best", ...,
CheckArguments = TRUE, Ns = NULL, Nexp = NULL)
## S3 method for class 'integer'
ShannonBeta(NorP, NorPexp = NULL, Correction = "Best", ...,
CheckArguments = TRUE, Ns = NULL, Nexp = NULL)
## S3 method for class 'numeric'
ShannonBeta(NorP, NorPexp = NULL, Correction = "Best", ...,
CheckArguments = TRUE, Ps = NULL, Ns = NULL, Pexp = NULL, Nexp = NULL)
Ps |
The probability vector of species of the community. |
Pexp |
The probability vector of species of the metacommunity. |
Ns |
A numeric vector containing species abundances of the community. |
Nexp |
A numeric vector containing species abundances of the metacommunity. |
NorP |
A numeric vector, an integer vector, an abundance vector ( |
NorPexp |
A numeric vector, an integer vector, an abundance vector ( |
Correction |
A string containing one of the possible corrections: currently, |
... |
Additional arguments. Unused. |
CheckArguments |
Logical; if |
The derivation of Shannon beta entropy can be found in Marcon et al. (2012).
Bias correction requires the number of individuals to estimate sample Coverage
. Use bcShannonBeta
and choose the Correction
.
The functions are designed to be used as simply as possible. ShannonBeta
is a generic method. If its first argument is an abundance vector, an integer vector or a numeric vector which does not sum to 1, the bias corrected function bcShannonBeta
is called. Explicit calls to bcShannonBeta
(with bias correction) or to ShannonBeta.ProbaVector
(without correction) are possible to avoid ambiguity. The .integer
and .numeric
methods accept Ps
or Ns
arguments instead of NorP
for backward compatibility.
A number equal to the calculated entropy.
Marcon, E., Herault, B., Baraloto, C. and Lang, G. (2012). The Decomposition of Shannon's Entropy and a Confidence Interval for Beta Diversity. Oikos 121(4): 516-522.
Zhang, Z. and Grabchak M. (2014). Nonparametric Estimation of Kullback-Leibler Divergence. Neural computation 26(11): 2570-2593.
bcShannonBeta
# Load Paracou data (number of trees per species in two 1-ha plot of a tropical forest)
data(Paracou618)
# Ps is the vector of probabilities
Ps <- as.ProbaVector(Paracou618.MC$Ps)
# Probability distribution of the first plot
Ps1 <- as.ProbaVector(Paracou618.MC$Psi[, 1])
# Shannon beta entropy of the plot
ShannonBeta(Ps1, Ps)
# Ns is the vector of abundances of the metacommunity
Ns <- as.AbdVector(Paracou618.MC$Ns)
# Abundances in the first plot
Ns1 <- as.AbdVector(Paracou618.MC$Nsi[, 1])
# Reduced-bias estimator of Shannon beta entropy of the plot
bcShannonBeta(Ns1, Ns)
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