| Pred.Qk.unweighted {RSE} | R Documentation |
Incidence-based data: Unweighted Estimator
Description
Unweighted Estimator derived from Chao et al. (2015)'s paper using incidence/quadrat data for predicting the number of new rare species in an additional ecological sample
Usage
Pred.Qk.unweighted(Q, nT, u, b, Q0, k.show = 3)
Arguments
Q |
A vector of species frequency counts, i.e., the number of species dectected once (in only one quadrat), the number of species dectected twice (in exactly two quadrats), and so forth. |
nT |
The number of quadrats of the original sample |
u |
The number of quadrats of an additional sample |
b |
A vector of two estimated parameters for obtaining the estimated relative species abundances by Chao et al.'s (2015) method. |
Q0 |
The estimated number of unseen species in the original sample by Chao 2 estimator (Chao 1987) |
k.show |
Display the estimating results of the numbers of new rare species detected in the number of quadrats <= k.show in the additional sample |
Value
The numbers of new rare species detected in the number of quadrats <= k.show are estimated by the incidence-based unweighted estimator derived from Chao et al. (2015)'s paper and returned.
Author(s)
Youhua Chen & Tsung-Jen Shen
References
Chao A, Hsieh T, Chazdon R, Colwell R, Gotelli N. 2015. Unveiling the species-rank abundance distribution by generalizing the Good-Turing sample coverage theory. Ecology 96:1189-1201.
Chao A. 1987. Estimating the population size for capture-recapture data with unequal catchability. Biometrics 43:783-791.
Shen TJ, Chen YH (2018) A Bayesian weighted approach to predicting the number of newly discovered rare species. Conservation Biology, In press.
See Also
Examples
## As an example, Canadian-mite data are used here.
data(CanadaMite)
## two columns represent two samples of incidence counts
X.merge = CanadaMite
## the first column is treated as the original sample
X.col1 = X.merge[,1]
Xi = X.col1
## Convert species incidence count data to frequency counts data
Q = X.to.f(Xi)
## the number of quadrats in the first sample
nT = 16
## the number of quadrats in the additional sample (i.e., the second column)
u = 16
b = DetInc(Xi, nT)
Pred.Qk.unweighted(Q=Q, nT=nT, u=u, b=b[1:2], Q0=b[3])