Heterogeneity

Computes an heterogeneity or a dominance index.

Usage

heterogeneity(object, ...)

# S4 method for class 'matrix'
heterogeneity(
  object,
  ...,
  method = c("shannon", "simpson", "berger", "boone", "brillouin", "mcintosh")
)

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

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.

Value

An HeterogeneityIndex object.

Details

The following heterogeneity index are available (see Magurran 1988 for details):

berger

Berger-Parker dominance index.

boone

Boone heterogeneity measure.

brillouin

Brillouin diversity index.

mcintosh

McIntosh dominance index.

shannon

Shannon-Wiener diversity index.

simpson

Simpson dominance index.

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 .

See also

index_berger(), index_boone(), index_brillouin(), index_mcintosh(), index_shannon(), index_simpson()

Other diversity measures: diversity(), evenness(), occurrence(), plot.DiversityIndex(), plot.RarefactionIndex(), profiles(), rarefaction(), richness(), she(), similarity(), simulate(), turnover()

Author

N. Frerebeau

Examples

## Data from Conkey 1980, Kintigh 1989
data("cantabria")

## Alpha diversity
diversity(cantabria)
#>                  size observed  shannon brillouin    simpson     berger
#> Altamira          152       38 3.269200  2.927046 0.04934211 0.09868421
#> Cueto de la Mina   69       27 2.955298  2.495839 0.07162361 0.17391304
#> El Juyo            53       19 2.491683  2.086441 0.11854753 0.22641509
#> El Cierro          35       15 2.485604  2.011085 0.10204082 0.20000000
#> La Paloma          23       12 2.329187  1.799103 0.11153119 0.17391304
#>                  menhinick margalef    chao1      ace  squares
#> Altamira          3.082207 7.364825 58.98602 48.27865 46.52101
#> Cueto de la Mina  3.250418 6.140611 40.87923 42.71952 40.06780
#> El Juyo           2.609851 4.533672 31.26415 39.99480 31.71478
#> El Cierro         2.535463 3.937730 18.49714 20.77674 18.96476
#> La Paloma         2.502173 3.508219 17.73913 17.49418 16.64770

## Shannon diversity index
(h <- heterogeneity(cantabria, method = "shannon"))
#> [1] 3.269200 2.955298 2.491683 2.485604 2.329187
(e <- evenness(cantabria, method = "shannon"))
#> [1] 0.8987278 0.8966760 0.8462335 0.9178574 0.9373336

as.data.frame(h)
#>                  size observed singleton doubleton    index
#> Altamira          152       38        13         4 3.269200
#> Cueto de la Mina   69       27        13         6 2.955298
#> El Juyo            53       19        10         4 2.491683
#> El Cierro          35       15         6         5 2.485604
#> La Paloma          23       12         6         3 2.329187