| cs.rSAC {preseqR} | R Documentation |
cs.rSAC predicts the expected number of species represented at least
r times in a random sample, based on the initial sample.
The estimator was originally proposed by Chao and Shen (2004) for estimating
the SAC. We generalize this estimator for predicting the r-SAC.
cs.rSAC(n, r=1, k=10)
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. |
r |
A positive integer. Default is 1. |
k |
A cutoff for common species. Default is 10. |
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
Chao, A., & Shen, T. J. (2004). Nonparametric prediction in species sampling. Journal of agricultural, biological, and environmental statistics, 9(3), 253-269.
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 chao1 <- cs.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 chao1(c(10, 20)) ## construct the estimator for r-SAC chao2 <- cs.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 chao2(c(50, 100))