Using prior information: MAP estimation and icoef?

Estimation
1

Hello,

I am in a learning stage and I wanted to confirm my understanding of the MAP estimation and icoef() compared to NONMEM keyword.

Simply, icoef() is equivalent to POSTHOC from NONMEM in terms of estimating individual parameters (EBEs) given the fixed population parameters (from the input param).

MAP estimation: fit(method = MAP(FOCE())) is equivalent to NONMEM’s PRIOR subroutine where the population parameters are estimated given the prior information.

Question: In a scenario, I want to estimate one parameter θa (under @param) with MAP estimation.

I specify θa with distributional domains (e.g., θa ~ Normal(0.0, 1.0)) and leave the rest of the parameters with RealDomain?

And do I fix the parameters with constantcoef?
e.g., fpm = fit(model, data, param, Pumas.MAP(foce()); constantcoef=(θb=4.0))