I am interested in understanding what is going on here, and if I can expect any systematic error/mischief in the xsphere data, or progress those with confidence. Has anyone seen this before or have any insight?
@taw I feel bad bugging you all the time, but: who else would know???
Interesting plots. Now that you mention it, I think I’ve seen this effect myself although I never paid attention to it.
It’s just a fact of modelling that adding degrees of freedom to a fit will always “improve” the figures of merit. I think that’s what’s going on here: the extra degrees of freedom have absorbed some of the difference between the two half-datasets, creating a weak correlation.
To test this, you could try manually splitting the stream (needs a bit of script hacking since scripts/alternate-stream doesn’t work on recent data) and running partialator twice for each model (four times in total). I think the correlation will go away.
Your account makes sense as well, thanks! Basically at low resolution, xsphere might be able to overfit to noise (if I understood correctly).
Not sure if that overfitting would have any significant negative effect on the data. It makes me a little nervous but on the other hand: the maps and R-factors look good.