RMark (version 2.1.1)

crdms: Example data for Closed Robust Design Multistrata

Description

Data and Script to simulate the MSCRD example of 15.7.1 from the MARK book Cooch and White

source

This example was constructed by Andrew Paul who is with Fish and Wildlife Division of the Alberta Provincial Government

References

For Cooch and White book see http://www.phidot.org/software/mark/

Examples

#Script to simulate the MSCRD
#example of 15.7.1 from the MARK
#book
#created by AJP 21 Dec 2010

#cleanup the R environment
graphics.off()
rm(list=ls())

#cleanup files
cleanup(ask=FALSE)

#convert .inp data - only needed to create crdms
#ch.data<-convert.inp("rd_simple1.inp")
data(crdms)
#set time intervals
#4 primary periods each with 3 secondary occasions
t.int<-c(rep(c(0,0,1),3),c(0,0))

#process data for RMark
crdms.data<-process.data(crdms,model="CRDMS",time.interval=t.int,
			strata.labels=c("1","U"))
#change Psi parameters that are obtained by subtraction
crdms.ddl<-make.design.data(crdms.data,
		parameters=list(Psi=list(subtract.stratum=c("1","1"))))

#create grouping index for unobserved p and c (i.e., always zero)
up=as.numeric(row.names(crdms.ddl$p[crdms.ddl$p$stratum=="U",]))

#create grouping index to fix Psi for unobs to unbos at time 1
#this isn't necessary but it allows this Psi to be fixed to a value
#that can be flagged and not erroneously interpreted
Psiuu1=as.numeric(row.names(crdms.ddl$Psi[crdms.ddl$Psi$stratum=="U"&
			crdms.ddl$Psi$time==1,]))

#create dummy variable for constraining last Psi in Markovian model
#variable is called ctime for constrained time
crdms.ddl$Psi$ctime=crdms.ddl$Psi$time
crdms.ddl$Psi$ctime[crdms.ddl$Psi$time==3]=2

#Initial assumptions
S.dot=list(formula=~1)  #S equal for both states and constant over time
p.session=list(formula=~session, share=TRUE,  #p=c varies with session
		fixed=list(index=up,value=0)) #p set to zero for unobs

#Model 1 - Markovian movement
Psi.markov=list(formula=~ctime+stratum,
		fixed=list(index=Psiuu1,value=9e-99)) #9e-99 is a flag
model.1=mark(crdms.data,ddl=crdms.ddl,
	model.parameters=list(S=S.dot,
		p=p.session,
		Psi=Psi.markov))

#Model 2 - Random movement
Psi.rand=list(formula=~time)
model.2=mark(crdms.data,ddl=crdms.ddl,
	model.parameters=list(S=S.dot,
		p=p.session,
		Psi=Psi.rand))

#Model 3 - No movement
Psi.fix=list(formula=~1,fixed=0)
model.3=mark(crdms.data,ddl=crdms.ddl,
	model.parameters=list(S=S.dot,
		p=p.session,
		Psi=Psi.fix))

#collect and store models
crdms.res<-collect.models()

print(crdms.res)

#final cleanup
cleanup(ask=FALSE)