I've been running just a CJS and I am getting what I think are quite large standard errors. They're not so massive that I think there might be an issue with identifiability ect. and can follow advice given in the handbook. I was just wondering if anyone had any suggestions on what I might do to try and reduce these?
The output for my top model is below, with the largest SE being around 0.75.
Many thanks!
Output summary for CJS model
Name : Phi(~time + tsm + injury)p(~1)
- Code: Select all
Npar : 8
-2lnL: 272.0516
AICc : 288.8428
Beta
estimate se lcl ucl
Phi:(Intercept) 1.1913346 0.3825554 0.4415261 1.9411431
Phi:time2015 0.6786781 0.7595055 -0.8099528 2.1673090
Phi:time2016 -1.0533295 0.6412413 -2.3101624 0.2035035
Phi:time2017 0.4496663 0.7566217 -1.0333123 1.9326448
Phi:time2018 -0.6089459 0.7529656 -2.0847584 0.8668666
Phi:tsm 0.7522087 0.5328636 -0.2922040 1.7966214
Phi:injurynone -1.9734351 0.4467178 -2.8490020 -1.0978681
p:(Intercept) 1.9795454 0.3193550 1.3536096 2.6054811