`heterogeneity()`

computes an heterogeneity or dominance index.`evenness()`

computes an evenness measure.

## Usage

```
heterogeneity(object, ...)
evenness(object, ...)
# S4 method for matrix
heterogeneity(
object,
...,
method = c("berger", "boone", "brillouin", "mcintosh", "shannon", "simpson")
)
# S4 method for data.frame
heterogeneity(
object,
...,
method = c("berger", "boone", "brillouin", "mcintosh", "shannon", "simpson")
)
# S4 method for matrix
evenness(
object,
...,
method = c("shannon", "brillouin", "mcintosh", "simpson")
)
# S4 method for data.frame
evenness(
object,
...,
method = c("shannon", "brillouin", "mcintosh", "simpson")
)
```

## Arguments

- object
A \(m \times p\)

`numeric`

`matrix`

or`data.frame`

of count data (absolute frequencies giving the number of individuals for each category, i.e. a contingency table). A`data.frame`

will be coerced to a`numeric`

`matrix`

via`data.matrix()`

.- ...
Further arguments to be passed to internal methods (see below).

- method
A

`character`

string specifying the index to be computed (see details). Any unambiguous substring can be given.- evenness
A

`logical`

scalar: should an evenness measure be computed instead of an heterogeneity/dominance index?

## Value

`heterogeneity()`

returns an HeterogeneityIndex object.`evenness()`

returns an EvennessIndex object.

## Details

*Diversity* measurement assumes that all individuals in a specific
taxa are equivalent and that all types are equally different from each
other (Peet 1974). A measure of diversity can be achieved by using indices
built on the relative abundance of taxa. These indices (sometimes referred
to as non-parametric indices) benefit from not making assumptions about the
underlying distribution of taxa abundance: they only take relative
abundances of the species that are present and species richness into
account. Peet (1974) refers to them as indices of *heterogeneity*.

Diversity indices focus on one aspect of the taxa abundance and emphasize
either *richness* (weighting towards uncommon taxa) or
*dominance* (weighting towards abundant taxa; Magurran 1988).

*Evenness* is a measure of how evenly individuals are distributed across the
sample.

## Heterogeneity and Evenness Measures

The following heterogeneity index and corresponding evenness measures are available (see Magurran 1988 for details):

`berger`

`boone`

`brillouin`

`mcintosh`

`shannon`

`simpson`

The `berger`

, `mcintosh`

and `simpson`

methods return a *dominance* index,
not the reciprocal or inverse form usually adopted, so that an increase in
the value of the index accompanies a decrease in diversity.

## References

Magurran, A. E. (1988). *Ecological Diversity and its Measurement*.
Princeton, NJ: Princeton University Press.
doi:10.1007/978-94-015-7358-0
.

Peet, R. K. (1974). The Measurement of Species Diversity. *Annual Review of
Ecology and Systematics*, 5(1), 285-307.
doi:10.1146/annurev.es.05.110174.001441
.

## See also

`index_berger()`

, `index_boone()`

, `index_brillouin()`

,
`index_mcintosh()`

, `index_shannon()`

, `index_simpson()`

Other diversity measures:
`occurrence()`

,
`plot_diversity`

,
`plot_rarefaction`

,
`profiles()`

,
`rarefaction()`

,
`richness()`

,
`she()`

,
`similarity()`

,
`simulate()`

,
`turnover()`