calc_comm_div_ci: Compute non-parametric confidence intervals across diversity...
In MoBiodiv/mobr: Measurement of Biodiversity

calc_comm_div_ci

R Documentation

Compute non-parametric confidence intervals across diversity indices.

Description

This function take a list of community matrices and returns the central tendency and the confidence interval for each diversity index of interest across that list of communities.

Usage

calc_comm_div_ci(
  samples,
  cent_stat = "median",
  ci = c(0.025, 0.975),
  index,
  effort = NA,
  extrapolate = TRUE,
  return_NA = FALSE,
  rare_thres = 0.05,
  scales = c("alpha", "gamma", "beta"),
  PIE_replace = FALSE,
  C_target_gamma = NA,
  ...
)

Arguments

`samples`	a list of community matrices (i.e., the output of `get_samples`)
`cent_stat`	a string that is either 'mean' or 'median' which specifies the measure of central tendency. Defaults to 'median'.
`ci`	a numeric vector of two numbers specifying the lower and upper quantiles of the distribution to return. The default is to report the 0.025 and 0.975 quantiles in other words a 95 percent interval.
`index`	The calculated biodiversity indices. The options are `N` ... Number of individuals (total abundance) `S` ... Number of species `S_n` ... Rarefied or extrapolated number of species for n individuals `S_C` ... Estimate species richness of a given level of coverage by `C_target_gamma` `S_asymp` ... Estimated asymptotic species richness `f_0` ... Estimated number of undetected species `pct_rare` ... The percent of rare species as defined by `rare_thres` `PIE` ... Hurlbert's PIE (Probability of Interspecific Encounter) `S_PIE` ... Effective number of species based on PIE See Details for additional information on the biodiversity statistics.
`effort`	The standardized number of individuals used for the calculation of rarefied species richness. This must be a single integer.
`extrapolate`	Boolean which specifies if richness should be extrapolated when effort is larger than the number of individuals using the chao1 method (Chao 1984, 1987). Defaults to TRUE.
`return_NA`	Boolean in which the rarefaction function returns the observed S when `effort` is larger than the number of individuals. If set to TRUE then NA is returned. Note that this argument is only relevant when `extrapolate = FALSE`.
`rare_thres`	The threshold that determines how the metric `pct_rare` is computed. It can range from (0, 1] and defaults to 0.05 which specifies that any species with less than or equal to 5 abundance in a sample is considered rare. It can also be specified as "N/S" which results in using average abundance as the threshold which McGill (2011) found to have the best small sample behavior.
`scales`	The scales to compute the diversity indices for: `alpha` ... for each row of the site x species community matrix `gamma` ... for the entire site x species community matrix `beta` ... the ratio of diversity at the `gamma` and `alpha` scales. Defaults to all three scales: `c('alpha', 'gamma', 'beta')`
`PIE_replace`	Used for `PIE` and `SPIE`. If TRUE, sampling with replacement is used. Otherwise, sampling without replacement (default).
`C_target_gamma`	When computing coverage based richness (`S_C`) then this argument can be used to specify the coverage to be used for the gamma scale richness estimate. This defaults to `NA` in which case the target cover is computed by `calc_C_target` (i.e., the largest allowable sample size).
`...`	additional arguments that can be passed to `calc_div`

Details

The measure of central tendency can be the median (default) or mean, and the range of the confidence interval can be specified but defaults to a 95 confidence interval.

Value

a data.frame that has the lower, middle, and upper quantiles of the sampled distribution of each diversity index.

Examples

data(tank_comm)
samples <- get_samples(tank_comm, algo = 'loo')
calc_comm_div_ci(samples, index = 'S_PIE')
samples <- get_samples(tank_comm, algo = 'boot', n_boot = 20)
calc_comm_div_ci(samples, index = 'S_PIE')
# compute ci for average diversity (rather than median)
calc_comm_div_ci(samples, index = 'S_PIE', cent_stat = 'avg')

MoBiodiv/mobr documentation built on Oct. 26, 2024, 10:51 a.m.