UWSojourner
Contributor
Leadduck
You are absolutely correct that any useful model must have a higher (less negative likelihood) than the null models such as the 1-step or 6-step sets. This same point was made earlier in this thread (back in post 424 on page 43) by Daniel Mewes. It is not clear what Wienke means by no fit value will be better than the 6-step set.
I made the same point in a lengthy commentary on this Deep stops model correlation manuscript (before it was published) and an earlier paper on another forum 2 years ago (still waiting to hear back from the author).
Correlation of popular diving models with computer profile data and outcomes
I will repost that commentary here, although it makes much the same point as you do, I have a bit of commentary on risk functions etc. that you might find interesting. I will have to split it up to stay under the 100000 character limit.
Any interpretation can only be based on what is written - and most of what is written is unclear - but from what is written it appears that the four models (USN, ZHL-6, VPM, RGBM) fit to the data set in the paper 1) are not actually the four algorithms of those names that are used to produce decompression schedules; 2) they differ from each other only by having different half-time compartments; and 3) none of them fit the data better than simply assigning identical risk to all dives in certain depth ranges, irrespective of how long the bottom time, how long the decompression, and what breathing gases are used (as you point out). This paper contributes nothing to the debate about whether deep stops or shallow stop schedules are more efficient.
David Doolette
Thanks for posting this. It certainly makes sense of some things for me.