Pred.Qk.unweighted: 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

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.

The number of quadrats of the original sample

The number of quadrats of an additional sample

A vector of two estimated parameters for obtaining the estimated relative species abundances by Chao et al.'s (2015) method.

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.

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.

Examples

Run this code

# NOT RUN {
## 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])		
# }