| Title: | Estimate the Population Size for the Mb Capture-Recapture Model |
|---|---|
| Description: | Applies an objective Bayesian method to the Mb capture-recapture model to estimate the population size N. The Mb model is a class of capture-recapture methods used to account for variations in capture probability due to animal behavior. Under the Mb formulation, the initial capture of an animal may effect the probability of subsequent captures due to their becoming "trap happy" or "trap shy." |
| Authors: | Dan Zheng |
| Maintainer: | John Snyder <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0.0 |
| Built: | 2025-03-12 03:12:49 UTC |
| Source: | https://github.com/cran/OBMbpkg |
Applies an objective Bayesian method on to the Mb capturere-capture model to estimate the population size N.
OBMb(k, n, M, x, CI1 = 0.025, CI2 = 0.975, max = 10000, IFMLE = TRUE)OBMb(k, n, M, x, CI1 = 0.025, CI2 = 0.975, max = 10000, IFMLE = TRUE)
k |
Number of sampling occasions |
n |
Total number of distinct animals captured |
M |
Number of marked animals captured in all sampling occasions |
x |
The number of new animals captured at each sampling occasion |
CI1 |
Lower confidence level |
CI2 |
Upper confidence level |
max |
The maximum of function evaluations used for computing the integrated likelihood L(N|X) |
IFMLE |
Logical, will also print MLE results if TRUE |
EMEAN: Posterior mean for N
EMEDIAN: Posterior median for N
OBCI: Credible interval values based on the quantiles specified by CI1 and CI2
MLE: If IFMLE==TRUE, this is the frequentist MLE for N
Ep: If IFMLE==TRUE, the frequentist estimate of the initial capture probability p
MLECI: If IFMLE==TRUE, confidence interval for the MLE quantile specified by CI2
# Data simulation example k=10 tN=600 #True N p=0.06 JN=rep(0,k+1) N=rep(0,k) x=rep(0,k) for (j in 1:k){ N[j]=tN-JN[j] x[j]=rbinom(1,N[j],p) JN[j+1]=JN[j]+x[j] } M=sum(JN[1:k]) n=JN[k+1] OBMb(k=k,n=n,M=M,x=x) #Deer mouse example from Otis et al 1978 Data<-c(15, 8, 6, 3, 3, 3) #new animals captured at each sampling occasion OBMb(k=6,n=38,M=134,x=Data)# Data simulation example k=10 tN=600 #True N p=0.06 JN=rep(0,k+1) N=rep(0,k) x=rep(0,k) for (j in 1:k){ N[j]=tN-JN[j] x[j]=rbinom(1,N[j],p) JN[j+1]=JN[j]+x[j] } M=sum(JN[1:k]) n=JN[k+1] OBMb(k=k,n=n,M=M,x=x) #Deer mouse example from Otis et al 1978 Data<-c(15, 8, 6, 3, 3, 3) #new animals captured at each sampling occasion OBMb(k=6,n=38,M=134,x=Data)