Uses of this program include:
There are two modes of operation of the program - Deterministic and Stochastic. Under deterministic mode, the number of animals is retained throughout the process as a fractional number and is multiplied by the parameters to get the final number of animals with each capture-history. This results in two things: capture-history frequencies are not integers, and parameter estimates will match input parameters exactly (almost). In stochastic mode, the number of animals is achieved via a simulation function (binomial) on the parameters. In this mode, the number of animals with each capture-history will be integers, and the data (and resulting estimates) will be different for each program run.
number of years(K): number new animals N(i): survival rates φ(i): capture rates p(i): recapture rates c(i): tag retention rate θ(i): theta age-specific theta time-specific
Capture-history generation options: Determinsitic(expected values) Stochastic(simulated values)
Recruitment options: In addition to (or instead of) adding new animals using the N(i)'s above, new animals may be added before each sampling occasion using one of the following functions: None binomial normal
# load output from gencaph1 into char variable, 'a' a<-'' a<-unlist(strsplit(unlist(strsplit(a,'\n')),' ')) # split into separate fields a<-matrix(a,ncol=2,byrow=T) # format as a matrix with 2 columns library(RMark) # Load RMark package # create data-frame with 2 fields: ch (capture-histories) and freq (frequencies) data<-data.frame(ch=a[,1],freq=as.numeric(a[,2])) # create RMark processed data object... pd<-process.data(data,model='CJS') # create formula for constant survival/capture probs f1<-list(formula=~1) # run mark model, phi(.)p(.) - constant survival and capture probs m1<-mark(data=pd,model.parameters=list(Phi=f1,p=f1)) # output of phi and p should match Gencaph1 input parameters if # expected values option was selected