I am relatively new to RMark but have been using Mark a bit longer. I read the useful Appendix C and the Package RMark, but I still have a few doubts about the heterogeneity models.
I did run my data on the robust design with the three types of emigration models (random, markovian and no emigration) and I would like to see if my best models chance when I include the heterogeneity model (pi.session).
I don’t know why I am getting the same results with pi constant and with pi session (same estimates, AICc and Deviance)
Any thoughts?
- Code: Select all
#convert inp file
rd.data=convert.inp("C:/R/RD/Qi 2-3 (5th fortnight-all days)_without calves and sightings(identified dolphins).inp",use.comments=T)
#Process data specifying primary and secondary capture occasions
time.intervals=c(0,0,0,0,0,3,0,0,3,0,3,0,0,0,3,0,0,0,0,0,3,0,0,0,0,3,0,0,0,0,3,0,0,0,12,0,0,0,0,0,0,3,0,0,3,0,0,3,0,0,0,0,0)
rd.process=process.data(rd.data,begin.time=1,model="RDFullHet",time.intervals=time.intervals)
#Create the design data
rd.ddl=make.design.data(rd.process)
#Markovian emigration
rd.markovian.models=function()
{
S.dot=list(formula=~1)
S.time=list(formula=~time)
p.time.session=list(formula=~-1+session:time,share=TRUE)
pi.session=list(formula=~session)
pi.dot=list(formula=~1)
GammaDoublePrime.dot=list(formula=~1)
GammaDoublePrime.time=list(formula=~time)
GammaPrime.dot=list(formula=~1)
GammaPrime.time=list(formula=~time)
N.session=list(formula=~session)
cml=create.model.list("RDFullHet")
results=mark.wrapper(cml,data=rd.process,ddl=rd.ddl,adjust=FALSE)
return(results)
}
rd.markovian.results=rd.markovian.models()
Markovian emigration
model npar AICc DeltaAICc weight Deviance
7 S(~1)Gamma''(~time)Gamma'(~time)pi(~1)p(~-1 + session:time)c()N(~session) 71 786.3054 0.000000 4.828916e-01 1378.507
8 S(~1)Gamma''(~time)Gamma'(~time)pi(~session)p(~-1 + session:time)c()N(~session) 71 786.3054 0.000000 4.828916e-01 1378.507
15 S(~time)Gamma''(~time)Gamma'(~time)pi(~1)p(~-1 + session:time)c()N(~session) 80 792.9870 6.681551 1.709894e-02 1358.612
16 S(~time)Gamma''(~time)Gamma'(~time)pi(~session)p(~-1 + session:time)c()N(~session) 80 792.9870 6.681551 1.709894e-02 1358.612
5 S(~1)Gamma''(~time)Gamma'(~1)pi(~1)p(~-1 + session:time)c()N(~session) 71 807.9944 21.688960 9.422190e-06 1400.196
6 S(~1)Gamma''(~time)Gamma'(~1)pi(~session)p(~-1 + session:time)c()N(~session) 71 807.9944 21.688960 9.422190e-06 1400.196
11 S(~time)Gamma''(~1)Gamma'(~time)pi(~1)p(~-1 + session:time)c()N(~session) 76 822.1561 35.850708 7.924417e-09 1399.760
12 S(~time)Gamma''(~1)Gamma'(~time)pi(~session)p(~-1 + session:time)c()N(~session) 76 822.1561 35.850708 7.924417e-09 1399.760
3 S(~1)Gamma''(~1)Gamma'(~time)pi(~1)p(~-1 + session:time)c()N(~session) 74 826.9691 40.663666 7.142442e-10 1410.461
4 S(~1)Gamma''(~1)Gamma'(~time)pi(~session)p(~-1 + session:time)c()N(~session) 74 826.9691 40.663666 7.142442e-10 1410.461
1 S(~1)Gamma''(~1)Gamma'(~1)pi(~1)p(~-1 + session:time)c()N(~session) 67 852.2580 65.952612 2.303679e-15 1455.847
2 S(~1)Gamma''(~1)Gamma'(~1)pi(~session)p(~-1 + session:time)c()N(~session) 67 852.2580 65.952612 2.303679e-15 1455.847
13 S(~time)Gamma''(~time)Gamma'(~1)pi(~1)p(~-1 + session:time)c()N(~session) 70 862.9490 76.643613 0.000000e+00 1458.021
14 S(~time)Gamma''(~time)Gamma'(~1)pi(~session)p(~-1 + session:time)c()N(~session) 70 862.9490 76.643613 0.000000e+00 1458.021
9 S(~time)Gamma''(~1)Gamma'(~1)pi(~1)p(~-1 + session:time)c()N(~session) 71 863.0373 76.731910 0.000000e+00 1455.239
10 S(~time)Gamma''(~1)Gamma'(~1)pi(~session)p(~-1 + session:time)c()N(~session) 71 863.0373 76.731910 0.000000e+00 1455.239
#Random emigration
rd.random.models=function()
{
S.dot=list(formula=~1)
S.time=list(formula=~time)
p.time.session=list(formula=~-1+session:time,share=TRUE)
pi.session=list(formula=~session)
pi.dot=list(formula=~1)
GammaDoublePrime.GammaPrime.random=list(formula=~1,share=T)
GammaDoublePrime.GammaPrime.session.random=list(formula=~time,share=T)
N.session=list(formula=~session)
cml=create.model.list("RDFullHet")
results=mark.wrapper(cml,data=rd.process,ddl=rd.ddl,adjust=FALSE)
return(results)
}
rd.random.results=rd.random.models()
Random emigration
model npar AICc DeltaAICc weight Deviance
7 S(~time)Gamma''(~time)Gamma'()pi(~1)p(~-1 + session:time)c()N(~session) 75 806.9297 0.000000 3.993942e-01 1387.486
8 S(~time)Gamma''(~time)Gamma'()pi(~session)p(~-1 + session:time)c()N(~session) 75 806.9297 0.000000 3.993942e-01 1387.486
3 S(~1)Gamma''(~time)Gamma'()pi(~1)p(~-1 + session:time)c()N(~session) 72 809.6872 2.757478 1.006058e-01 1399.002
4 S(~1)Gamma''(~time)Gamma'()pi(~session)p(~-1 + session:time)c()N(~session) 72 809.6872 2.757478 1.006058e-01 1399.002
6 S(~time)Gamma''(~1)Gamma'()pi(~session)p(~-1 + session:time)c()N(~session) 70 849.5659 42.636192 2.203283e-10 1444.638
1 S(~1)Gamma''(~1)Gamma'()pi(~1)p(~-1 + session:time)c()N(~session) 67 855.2729 48.343131 1.270060e-11 1458.862
2 S(~1)Gamma''(~1)Gamma'()pi(~session)p(~-1 + session:time)c()N(~session) 67 855.2729 48.343131 1.270060e-11 1458.862
Many Thanks
Sergi