otis: Fit some basic capture-recapture models based on Otis et al....

In jaroyle/oSCR: Multi-Session Sex-Structured Spatial Capture-Recapture Models

Fit some basic capture-recapture models based on Otis et al. (1978)

Description

This functions fits models M0, Mt, Mh, and Mb and variations of those models.

Usage

otis(y2d,mhst=NULL, cov=NULL)

Arguments

y2d	nind x K (occasions) matrix of encounter histories. A standard closed population capture-recapture data set.
mhst	Starting values for the logit-normal model Mh in order: (logit(p0), log(mu), log(n0)) where n0 = N - n. If no starting values are provided, the function does a grid search to find a decent starting value (this can take some time).
cov	This is a K x 1 vector of some covariate that might explain variation in detection probability over time. e.g., a linear trend covariate or weather conditions. If supplied then models which include this covariate will be fitted.

Details

Default setting (cov=NULL) will fit 4 models: M0, Mb, Mh (logit-normal) and Mh (2-point finite mixture).

If cov= is specified then Mcov and Mb+cov are fitted

Soon we will fit other models too.

Value

Returns an AIC table including other model summaries (Nhat) and a table of model coefficients.

Author(s)

Andy Royle

References

Otis et al. (1978)

Examples

# see also ?peromyscus 
library(scrbook)
data(beardata)
trapmat<-beardata$trapmat
nind<-dim(beardata$bearArray)[1]
K<-dim(beardata$bearArray)[3]
ntraps<-dim(beardata$bearArray)[2]
bearArray<-beardata$bearArray

# Convert 3d array to 2d array
y<- flatten(bearArray)

# Fit some basic CR models 
otis(y)


# A simulation study:


N<- 100
K<- 8
nsims<- 1
for(sim in 1:nsims){
y<- matrix(0,nrow=N,ncol=K)
for(i in 1:N){
  p<- plogis(rnorm(1,-1.85, 0.5))
  y[i,]<- rbinom(K, 1, p)
  }

y<- y[apply(y,1,sum)>0,]
otis(y, cov=c(1,1,1,1,0,0,0,0))
}

jaroyle/oSCR documentation built on Sept. 23, 2023, 12:46 p.m.