Gim statistic, model comparisions

[Hana Majerova]

Hi Ryan, 

 

I am using dadi-cli to generate my model data, successfully I hope! However, as statistics is quite new to me, I have some questions regarding the results. It would be very helpful to get some rules of thumb on how to handle the following:

Briefly, I am working with pseudo-diploidized, subsampled, and composite RadSeq data that has been cleared of paralogues. I have tested many models, and most yield similar results to the one appended here.

1. Uncertainty in theta: For all my parameters except theta, I get reasonable confidence intervals (CIs). Theta, however, is always 'unconfident' (wide CIs). Is this acceptable? I understand theta is used primarily to calculate real-world values (times, population sizes, etc.), but can I still trust the rest of my demographic parameters? Is there a detectable reason why theta is consistently so uncertain? I have seen this behavior even with datasets containing paralogues, as well as on projected and unlinked datasets.
2. Step Sizes: The confidence intervals for the parameters differ by an order of magnitude depending on the step size used. Is this normal? Should they be approximately the same, or is it okay to select one step size and present that as the result?
3. Small CIs: Sometimes the confidence intervals are so small they seem biologically unrealistic. Is that okay? I’ve heard this can be due to a 'mathematical collapse' when a likelihood peak is too sharp, but I am unsure how to interpret this.
4. Model Comparison: Is there a way to compare models generated by dadi-cli on composite data using their log-likelihoods? I know it should be possible to use CLAIC for non-nested models and LRT for nested models, but I cannot find a way to perform these in dadi-cli. Can these be inferred from the GIM (Godambe Information Matrix) results? If dadi-cli doesn’t support this directly, what is the best alternative?                 

Any suggestions or guidance would be greatly appreciated.

Thank you in advance and all the best,

Hana