ds.rSAC | R Documentation |
ds.rSAC
predicts the expected number of species represented at least
r times in a random sample, based on the initial sample.
ds.rSAC(n, r=1, mt=20)
n |
A two-column matrix. The first column is the frequency j = 1,2,…; and the second column is N_j, the number of species with each species represented exactly j times in the initial sample. The first column must be sorted in an ascending order. |
mt |
An positive integer constraining possible rational function approximations. Default is 20. |
r |
A positive integer. Default is 1. |
The estimator is based on an empirical Bayes approach using rational function approximation (RFA), as described in the paper in the references section.
ds.rSAC
is the fast version of ds.rSAC.bootstrap
.
The function does not provide the confidence interval. To obtain the
confidence interval along with the estimates, one should use the function
ds.rSAC.bootstrap
.
The estimator for the r-SAC. The input of the estimator is a vector of sampling efforts t, i.e., the relative sample sizes comparing with the initial sample. For example, t = 2 means a random sample that is twice the size of the initial sample.
Chao Deng
Deng, C., Daley, T., Calabrese, P., Ren, J., & Smith, A.D. (2016). Estimating the number of species to attain sufficient representation in a random sample. arXiv preprint arXiv:1607.02804v3.
## load library library(preseqR) ## import data data(FisherButterfly) ## construct the estimator for SAC ds1 <- ds.rSAC(FisherButterfly, r=1) ## The number of species represented at least once in a sample, ## when the sample size is 10 or 20 times of the initial sample ds1(c(10, 20)) ## construct the estimator for r-SAC ds2 <- ds.rSAC(FisherButterfly, r=2) ## The number of species represented at least twice in a sample, ## when the sample size is 50 or 100 times of the initial sample ds2(c(50, 100))
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