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Sample completeness profiles

Source: R/completeness.R
div_completeness.Rd

Estimates sample completeness of order q for each site (Chao et al. 2020). Completeness at q = 0 is the proportion of species richness detected, at q = 1 it is the sample coverage, and higher orders give increasing weight to abundant species. Values lie between 0 and 1, except in samples made up only of singletons, where estimates for q > 0 are unreliable and can be negative (as in iNEXT.4steps); a warning flags such sites.

Usage

div_completeness(
  data,
  site = site,
  species = species,
  abundance = NULL,
  q = seq(0, 2, 0.2),
  conf = 0.95,
  nboot = 50,
  .by = NULL
)

Arguments

data

A data frame in long format, with one row per record.

site, species

<tidy-select> The column holding site identifiers and the column holding species names.

abundance

<tidy-select> The column holding counts. If NULL (the default), a column called abundance is used when present, otherwise every row counts as one individual.

q

Orders of completeness, non-negative numbers. Defaults to a profile from 0 to 2.

conf

Confidence level of the intervals.

nboot

Number of bootstrap replicates. Use 0 to skip them.

.by

<tidy-select> Optional grouping columns, kept in the output. Grouped data frames are also respected.

Value

A td_completeness tibble with the grouping columns, the site column, q, completeness, se, lower and upper.

References

Chao, A., Kubota, Y., Zeleny, D., Chiu, C.-H., Li, C.-F., Kusumoto, B., Yasuhara, M., Thorn, S., Wei, C.-L., Costello, M. J. and Colwell, R. K. (2020). Quantifying sample completeness and comparing diversities among assemblages. Ecological Research, 35, 292-314. doi:10.1111/1440-1703.12102

Examples

div_completeness(png_ants, q = c(0, 1, 2), .by = season, nboot = 0)
#> # A tibble: 30 x 7
#>    season site       q completeness    se lower upper
#>    <chr>  <chr>  <dbl>        <dbl> <dbl> <dbl> <dbl>
#>  1 dry    MW0200     0        0.867    NA    NA    NA
#>  2 dry    MW0200     1        0.898    NA    NA    NA
#>  3 dry    MW0200     2        0.948    NA    NA    NA
#>  4 wet    MW0200     0        0.638    NA    NA    NA
#>  5 wet    MW0200     1        0.857    NA    NA    NA
#>  6 wet    MW0200     2        0.987    NA    NA    NA
#>  7 dry    MW0700     0        0.179    NA    NA    NA
#>  8 dry    MW0700     1        0.482    NA    NA    NA
#>  9 dry    MW0700     2        0.948    NA    NA    NA
#> 10 wet    MW0700     0        0.372    NA    NA    NA
#> # i 20 more rows