entropart (version 1.4-4)

Shannon: Shannon entropy of a community

Description

Calculates the Shannon entropy of a probability vector.

Usage

Shannon(NorP, Correction = "Best", CheckArguments = TRUE, 
  Ps = NULL, Ns = NULL)
bcShannon(Ns, Correction = "Best", CheckArguments = TRUE)
## S3 method for class 'ProbaVector':
Shannon(NorP, Correction = "Best", CheckArguments = TRUE, 
  Ps = NULL, Ns = NULL)
## S3 method for class 'AbdVector':
Shannon(NorP, Correction = "Best", CheckArguments = TRUE, 
  Ps = NULL, Ns = NULL)
## S3 method for class 'integer':
Shannon(NorP, Correction = "Best", CheckArguments = TRUE, 
  Ps = NULL, Ns = NULL)
## S3 method for class 'numeric':
Shannon(NorP, Correction = "Best", CheckArguments = TRUE, 
  Ps = NULL, Ns = NULL)

Arguments

Ps

A probability vector, summing to 1.

Ns

A numeric vector containing species abundances.

NorP

A numeric vector, an integer vector, an abundance vector (AbdVector) or a probability vector (ProbaVector). Contains either abundances or probabilities

Correction

A string containing one of the possible corrections: "None" (no correction), "ChaoShen", "GenCov", "Grassberger", "Grassberger2003", "Schurmann", "Holste", "B

CheckArguments

Logical; if TRUE, the function arguments are verified. Should be set to FALSE to save time when the arguments have been checked elsewhere.

Value

• A named number equal to the calculated entropy. The name is that of the bias correction used.

Details

Bias correction requires the number of individuals to estimate sample Coverage. Use bcShannon and choose the Correction. Correction techniques are from Miller (1955), Chao and Shen (2003), Grassberger (1988), Grassberger (2003), Schurmann (2003), Holste et al. (1998), Bonachela et al. (2008), Zhang (2012), Chao, Wang and Jost (2013). More estimators can be found in the entropy package. Using MetaCommunity mutual information, Chao, Wang and Jost (2013) calculate reduced-bias Shannon beta entropy (see the last example below) with better results than the Chao and Shen estimator, but community weights cannot be arbitrary: they must be proportional to the number of individuals. The functions are designed to be used as simply as possible. Shannon 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 bcShannon is called. Explicit calls to bcShannon (with bias correction) or to Shannon.ProbaVector (without correction) are possible to avoid ambiguity. The .integer and .numeric methods accept Ps or Ns arguments instead of NorP for backward compatibility.

References

Bonachela, J. A., Hinrichsen, H. and Munoz, M. A. (2008). Entropy estimates of small data sets. Journal of Physics A: Mathematical and Theoretical 41(202001): 1-9. Chao, A. and Shen, T. J. (2003). Nonparametric estimation of Shannon's index of diversity when there are unseen species in sample. Environmental and Ecological Statistics 10(4): 429-443. Chao, A., Wang, Y. T. and Jost, L. (2013). Entropy and the species accumulation curve: a novel entropy estimator via discovery rates of new species. Methods in Ecology and Evolution 4(11):1091-1100. Grassberger, P. (1988). Finite sample corrections to entropy and dimension estimates. Physics Letters A 128(6-7): 369-373. Grassberger, P. (2003). Entropy Estimates from Insufficient Samplings. ArXiv Physics e-prints 0307138. Holste, D., Grosse, I. and Herzel, H. (1998). Bayes' estimators of generalized entropies. Journal of Physics A: Mathematical and General 31(11): 2551-2566. Miller, G. (1955) Note on the bias of information estimates. In: Quastler, H., editor. Information Theory in Psychology: Problems and Methods: 95-100. Shannon, C. E. (1948). A Mathematical Theory of Communication. The Bell System Technical Journal 27: 379-423, 623-656. Schurmann, T. (2004). Bias analysis in entropy estimation. Journal of Physics A: Mathematical and Theoretical 37(27): L295-L301. Tsallis, C. (1988). Possible generalization of Boltzmann-Gibbs statistics. Journal of Statistical Physics 52(1): 479-487. Zhang, Z. (2012). Entropy Estimation in Turing's Perspective. Neural Computation 24(5): 1368-1389.

See Also

bcShannon, Tsallis

Examples

# Load Paracou data (number of trees per species in two 1-ha plot of a tropical forest)
  data(Paracou618)
  # Ns is the total number of trees per species
  Ns <- as.AbdVector(Paracou618.MC$Ns)
  # Species probabilities
  Ps <- as.ProbaVector(Paracou618.MC$Ns)
  # Whittaker plot
  plot(Ns)

  # Calculate Shannon entropy
  Shannon(Ps)

  # Calculate the best estimator of Shannon entropy
  Shannon(Ns)
  
  # Use metacommunity data to calculate reduced-bias Shannon beta as mutual information
  (bcShannon(Paracou618.MC$Ns) + bcShannon(colSums(Paracou618.MC$Nsi))
    - bcShannon(Paracou618.MC$Nsi))