Any discrepancy between MARK and RMark is reported in the model summary. Below is an example for Phi(t)p(t) with the dipper data.
- Code: Select all
> mark(dipper,model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)))
Note: only 11 parameters counted of 12 specified parameters
AICc and parameter count have been adjusted upward
Output summary for CJS model
Name : Phi(~time)p(~time)
Npar : 12 (unadjusted=11)
-2lnL: 656.9502
AICc : 681.7057 (unadjusted=679.58789)
You can use the MARK parameter counts by setting adjust=FALSE in your call to either mark or mark.wrapper as shown below:
- Code: Select all
> mark(dipper,model.parameters=list(Phi=list(formula=~time),p=list(formula=~time)),adjust=FALSE)
Output summary for CJS model
Name : Phi(~time)p(~time)
Npar : 11
-2lnL: 656.9502
AICc : 679.5879
In some cases neither parameter count is correct when there is a mix of both parameters at boundaries and non-identifiable parameters as in phi(t)p(t). In those cases you can use the function adjust.parameter.count to adjust the number of estimated parameters and the resulting AIC.
regards --jeff