Ian Tickle wrote:
For that to be true it would have to be possible to arrive at a different unbiased Rfree from another starting point. But provided your starting point wasn't a local maximum LL and you haven't gotten into a local maximum along the way, convergence will be to a unique global maximum of the LL, so the Rfree must be the same whatever starting point is used (within the radius of convergence of course).
But if you're using a different set of data the minima and maxima of the function aren't necessarily going to be in the same place. Rfree is supposed to inform about overfitting. In an overfitting situation there are multiple possible models which describe the data well and which overfit solution you end up with could be sensitive to the data set used. The provisions that you haven't gotten stuck in a local maximum and are within radius of convergence don't seem safe considering historical situations that led to the introduction of Rfree. What algorithm is going to converge main chain tracing errors to the correct maximum? Thinking about that situation, isn't part of the goal of Rfree to give you a hint in situations where you have, in fact, gotten stuck in a local maximum due to a significant error in the model that places it outside the radius of convergence of the refinement algorithm?
-Eric --
