Positions of common approximations within FS

[diazrenata]

A number of functions are popular for fitting the SAD, but it's not clear how much of the support for them comes from the fact that they generate hollow curves vs. they meaningfully predict observed vectors above and beyond the constraint imposed by the feasible set.

The logic here has some nuance/chirality to it, and probably needs more thought. But:

Where do vectors drawn from the fitted distribution (lognormal, geometric, etc), that have been constrained/selected to have the correct S and N, fall in the feasible set compared to empirical distributions?

This has some nuance to it because constraining the samples to fall within the feasible set (have the right S and N) may drag them away from what is likely for the function. An alternative might be to calculate the likelihood of each of the FS samples | the function, and see if the empirical vector has an especially high likelihood compared to the builk of the FS.

[diazrenata]

https://github.com/diazrenata/scads/tree/goff goodness of fit functions

[diazrenata]

:bangbang: https://github.com/diazrenata/scads/blob/goff/reports/goffs.md looks like fitted dists are v close to SAD relative to FS? Caveats: 1) fitted dists except for METE have v specific parameters 2) plotted draw from fitted dist is one of few elements of draws from fitted dist that sums to S and N.