I'm running a CJS analysis
I would like to reclassify my age variable, and create 3 age classes:
chicks 0-1 years (<1)
juveniles: 1-2 (>=1 <2)
adults: 2-... (>=2)
I realise that if I coded:
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dipper.ddl <- add.design.data(dipper.proc,
dipper.ddl,
"Phi",
type = "age",
bins = c(0, 1, 2, 7),
name = "ageclass",
replace=T)
It seems that for column "age" == 0 and "age" == 1, both are included in the category [0, 1]; which it is not my purpose:
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head(dipper.ddl)
$Phi
par.index model.index group cohort age time occ.cohort Cohort Age Time sex ageclass
1 1 1 F 1 0 1 1 0 0 0 F [0,1]
2 2 2 F 1 1 2 1 0 1 1 F [0,1]
3 3 3 F 1 2 3 1 0 2 2 F (1,2]
4 4 4 F 1 3 4 1 0 3 3 F (2,7]
So, maybe should I code?:
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dipper.ddl <- add.design.data(dipper.proc,
dipper.ddl,
"Phi",
type = "age",
bins = c(0, 0.9, 1.9, 7),
name = "ageclass",
replace=T)
And now "age" == 0, is classified in one category, "age" == 1 in another different category, and "age" >= 2 in another different one category:
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head(dipper.ddl)
$Phi
par.index model.index group cohort age time occ.cohort Cohort Age Time sex ageclass
1 1 1 F 1 0 1 1 0 0 0 F [0,0.9]
2 2 2 F 1 1 2 1 0 1 1 F (0.9,1.9]
3 3 3 F 1 2 3 1 0 2 2 F (1.9,7]
4 4 4 F 1 3 4 1 0 3 3 F (1.9,7]
Please, can you confirm me which coding I have to use for reclassifying the data according to my ecological preferences?
I have seen in some demos/vignettes option 1, but I think that this is not correct...
Additionally, I would like to ask how the number of parameters (npar) is estimated in Rmark
Thanks a lot
Pablo