Pred.Qk.BW: Incidence-based data: Bayesian-weight estimator
In RSE: Number of Newly Discovered Rare Species Estimation

Description Usage Arguments Value Author(s) References See Also Examples

Bayesian-weight estimator for predicting the number of new rare species using incidence/quadrat data

1	Pred.Qk.BW(Q, nT, u, b, k.show = 3)

`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.
`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

The numbers of new rare species detected in the number of quadrats <= k.show are estimated by the incidence-based Bayesian-weight estimator and returned.

Youhua Chen & Tsung-Jen Shen

Shen TJ, Chen YH (2018) A Bayesian weighted approach to predicting the number of newly discovered rare species. Conservation Biology, In press.

Pred.Fk.BW

## 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(y=Xi, nT=nT)	
Pred.Qk.BW(Q=Q, nT=nT, u=u, b=b[1:2])