Simpson {entropart} | R Documentation |

Calculates the Simpson entropy of a probability vector.

```
Simpson(NorP, ...)
bcSimpson(Ns, Correction = "Best", CheckArguments = TRUE)
## S3 method for class 'ProbaVector'
Simpson(NorP, ..., CheckArguments = TRUE,
Ps = NULL)
## S3 method for class 'AbdVector'
Simpson(NorP, Correction="Best", Level = NULL, ...,
CheckArguments = TRUE, Ns = NULL)
## S3 method for class 'integer'
Simpson(NorP, Correction="Best", Level = NULL, ...,
CheckArguments = TRUE, Ns = NULL)
## S3 method for class 'numeric'
Simpson(NorP, Correction="Best", Level = NULL, ...,
CheckArguments = TRUE, Ps = NULL, Ns = NULL)
```

`Ps` |
A probability vector, summing to 1. |

`Ns` |
A numeric vector containing species abundances. |

`NorP` |
A numeric vector, an integer vector, an abundance vector ( |

`Correction` |
A string containing one of the possible corrections accepted by |

`Level` |
The level of interpolation or extrapolation. It may be an a chosen sample size (an integer) or a sample coverage (a number between 0 and 1). |

`...` |
Additional arguments. Unused. |

`CheckArguments` |
Logical; if |

Lande's correction has been derived (Lande, 1996; Good, 1953) especially for Simpson entropy, while other corrections are for generalized Tsallis entropy. It is identical to the unbiased estimator proposed by Simpson, although arguments were different. It equals the plug-in etimator multiplied by n/(n-1) where n is the total number of individuals.

Bias correction requires the number of individuals to estimate sample `Coverage`

.

The functions are designed to be used as simply as possible. `Simpson`

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 `bcSimpson`

is called.

Entropy can be estimated at a specified level of interpolation or extrapolation, either a chosen sample size or sample coverage (Chao et al., 2014), rather than its asymptotic value. Simpson's extrapolated entropy estimator does not rely on the estimation of the asymptotic distribution.

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

Chao, A., Gotelli, N. J., Hsieh, T. C., Sander, E. L., Ma, K. H., Colwell, R. K., Ellison, A. M (2014). Rarefaction and extrapolation with Hill numbers: A framework for sampling and estimation in species diversity studies. *Ecological Monographs*, 84(1): 45-67.

Good, I. J. (1953). On the Population Frequency of Species and the Estimation of Population Parameters. *Biometrika* 40(3/4): 237-264.

Lande, R. (1996). Statistics and partitioning of species diversity, and similarity among multiple communities. *Oikos* 76: 5-13.

Simpson, E. H. (1949). Measurement of diversity. *Nature* 163(4148): 688.

```
# 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)
# Whittaker plot
plot(Ns)
# Calculate an unbiased estimator of Simpson's index of diversity
Simpson(Ns)
```

[Package *entropart* version 1.6-13 Index]