tabula (version 3.3.0)

simulate: Measure Diversity by Comparing to Simulated Assemblages

Description

Measure Diversity by Comparing to Simulated Assemblages

Usage

# S4 method for DiversityIndex
simulate(
  object,
  nsim = 1000,
  seed = NULL,
  step = 1,
  level = 0.8,
  interval = "percentiles",
  progress = getOption("tabula.progress"),
  ...
)

Value

Returns a DiversityIndex object.

Arguments

object

A DiversityIndex object.

nsim

A non-negative integer specifying the number of simulations.

seed

An object specifying if and how the random number generator should be initialized (see stats::simulate()).

step

An integer giving the increment of the sample size.

level

A length-one numeric vector giving the confidence level.

interval

A character string giving the type of confidence interval to be returned. Currently, only "percentiles" is supported (sample quantiles, as described in Kintigh 1984)..

progress

A logical scalar: should a progress bar be displayed?

...

Currently not used.

Author

N. Frerebeau

References

Baxter, M. J. (2001). Methodological Issues in the Study of Assemblage Diversity. American Antiquity, 66(4), 715-725. tools:::Rd_expr_doi("10.2307/2694184").

Kintigh, K. W. (1984). Measuring Archaeological Diversity by Comparison with Simulated Assemblages. American Antiquity, 49(1), 44-54. tools:::Rd_expr_doi("10.2307/280511").

See Also

bootstrap(), jackknife()

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

Examples

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

## Assemblage diversity size comparison
## Warning: this may take a few seconds!
h <- heterogeneity(cantabria, method = "shannon")
h_sim <- simulate(h)
plot(h_sim)

r <- richness(cantabria, method = "observed")
r_sim <- simulate(r)
plot(r_sim)
# }