achieve a stable parameters estimation

[vbartolino]

I'm using a Gadget model to evaluate different aspects of a sampling design. Unfortunately, I'm having problem in achieving a sufficiently stable estimation of the model parameters to evaluate if differences are the result of the sampling design.

I've tried the following with limited success:

• restrict the low-up bound for some of the parameters
• increase the number of iteration of all optimisers simann, hooke, bfgs. I'm not too familiar with the several other parameters here.

Any other suggestion? Thankful as always

[vbartolino]

In this specific case I found that the issue was in fact related to overfitting the acoustic survey index. This was causing major problem in the reweighting procedure with the index getting very high weight (but not always the same!) compared to the other likelihood components.

The argument 'cv.floor' in Rgadget helped to avoid overfitting and solved the issue.