coverage() calculates an estimator of the sample coverage of a community
described by its abundance vector.
coverage_to_size() estimates the sample size corresponding to the chosen
sample coverage.
coverage(x, ...)
# S3 method for numeric
coverage(
x,
estimator = c("ZhangHuang", "Chao", "Turing", "Good"),
level = NULL,
as_numeric = FALSE,
...,
check_arguments = TRUE
)
# S3 method for abundances
coverage(
x,
estimator = c("ZhangHuang", "Chao", "Turing", "Good"),
level = NULL,
...,
check_arguments = TRUE
)
coverage_to_size(x, ...)
# S3 method for numeric
coverage_to_size(
x,
sample_coverage,
estimator = c("ZhangHuang", "Chao", "Turing", "Good"),
as_numeric = FALSE,
...,
check_arguments = TRUE
)
# S3 method for abundances
coverage_to_size(
x,
sample_coverage,
estimator = c("ZhangHuang", "Chao", "Turing", "Good"),
...,
check_arguments = TRUE
)An object.
Unused.
An estimator of the sample coverage.
"ZhangHuang" is the most accurate but does not allow choosing a level.
"Good"'s estimator only allows interpolation, i.e. estimation of the coverage
of a subsample.
"Chao" allows estimation at any level, including extrapolation.
"Turing" is the simplest estimator, equal to 1 minus the number of singletons
divided by the sample size.
The level of interpolation or extrapolation, i.e. an abundance.
If TRUE, a number or a numeric vector is returned rather than a tibble.
If TRUE, the function arguments are verified.
Should be set to FALSE to save time when the arguments have been checked elsewhere.
The target sample coverage.
coverage() returns a named number equal to the calculated sample coverage.
The name is that of the estimator used.
coverage_to_size() returns a number equal to the sample size corresponding
to the chosen sample coverage.
The sample coverage \(C\) of a community is the total probability of occurrence of the species observed in the sample. \(1-C\) is the probability for an individual of the whole community to belong to a species that has not been sampled.
The historical estimator is due to Turing (Good 1953) . It only relies on singletons (species observed only once). Chao's (Chao and Shen 2010) estimator uses doubletons too and Zhang-Huang's (Chao et al. 1988; Zhang and Huang 2007) uses the whole distribution.
If level is not NULL, the sample coverage is interpolated or extrapolated.
Interpolation by the Good estimator relies on the equality between sampling
deficit and the generalized Simpson entropy (Good 1953)
.
The Chao et al. (2014)
estimator allows extrapolation,
reliable up a level equal to the double size of the sample.
Chao A, Gotelli NJ, Hsieh TC, Sander EL, Ma KH, Colwell RK, Ellison AM (2014).
“Rarefaction and Extrapolation with Hill Numbers: A Framework for Sampling and Estimation in Species Diversity Studies.”
Ecological Monographs, 84(1), 45--67.
doi:10.1890/13-0133.1
.
Chao A, Lee S, Chen T (1988).
“A Generalized Good's Nonparametric Coverage Estimator.”
Chinese Journal of Mathematics, 16, 189--199.
43836340.
Chao A, Shen T (2010).
Program SPADE: Species Prediction and Diversity Estimation. Program and User's Guide..
CARE.
Good IJ (1953).
“The Population Frequency of Species and the Estimation of Population Parameters.”
Biometrika, 40(3/4), 237--264.
doi:10.1093/biomet/40.3-4.237
.
Zhang Z, Huang H (2007).
“Turing's Formula Revisited.”
Journal of Quantitative Linguistics, 14(2-3), 222--241.
doi:10.1080/09296170701514189
.
coverage(paracou_6_abd)
#> # A tibble: 4 × 4
#> site weight estimator coverage
#> <chr> <dbl> <chr> <dbl>
#> 1 subplot_1 1.56 ZhangHuang 0.911
#> 2 subplot_2 1.56 ZhangHuang 0.893
#> 3 subplot_3 1.56 ZhangHuang 0.912
#> 4 subplot_4 1.56 ZhangHuang 0.902
coverage_to_size(paracou_6_abd, sample_coverage = 0.9)
#> # A tibble: 4 × 4
#> site weight sample_coverage size
#> <chr> <dbl> <dbl> <dbl>
#> 1 subplot_1 1.56 0.9 819
#> 2 subplot_2 1.56 0.9 940
#> 3 subplot_3 1.56 0.9 826
#> 4 subplot_4 1.56 0.9 778