We don't currently have a really good measure of that point where adding the extra shell of data adds "significant" information (whatever that means. However, my rough trials (see http://www.ncbi.nlm.nih.gov/pubmed/23793146) suggested that the exact cutoff point was not very critical, presumably as the "information content" fades out slowly, so it probably isn't something to agonise over too much. K & D's paired refinement may be useful though.
I would again caution against looking too hard at CC* rather than CC1/2: they are exactly equivalent, but CC* changes very rapidly at small values, which may be misleading. The purpose of CC* is for comparison with CCcryst (i.e. Fo to Fc). I would remind any users of Scala who want to look back at old log files to see the statistics for the outer shell at the cutoff they used, that CC1/2 has been calculated in Scala for many years under the name CC_IMEAN. It's now called CC1/2 in Aimless (and Scala) following Kai's excellent suggestion. Phil On 28 Aug 2013, at 08:21, Bernhard Rupp <hofkristall...@gmail.com> wrote: > >Based on the simulations I've done the data should be "cut" at CC1/2 = 0. > >Seriously. Problem is figuring out where it hits zero. > > But the real objective is – where do data stop making an improvement to the > model. The categorical statement that all data is good > is simply not true in practice. It is probably specific to each data set & > refinement, and as long as we do not always run paired refinement ala KD > or similar in order to find out where that point is, the yearning for a > simple number will not stop (although I believe automation will make the KD > approach or similar eventually routine). > > >As for the "resolution of the structure" I'd say call that where |Fo-Fc| > >(error in the map) becomes comparable to Sigma(Fo). This is I/Sigma = 2.5 if > >Rcryst is 20%. That is: |Fo-Fc| / Fo = 0.2, which implies |Io-Ic|/Io = 0.4 > >or Io/|Io-Ic| = Io/sigma(Io) = 2.5. > > Makes sense to me... > > As long as it is understood that this ‘model resolution value’ derived via > your argument from I/sigI is not the same as a <I/sigI> data cutoff (and that > Rcryst and Rmerge have nothing in common)…. > > -James Holton > MAD Scientist > > > Best, BR > > > > > > > On Aug 27, 2013, at 5:29 PM, Jim Pflugrath <jim.pflugr...@rigaku.com> wrote: > > I have to ask flamingly: So what about CC1/2 and CC*? > > Did we not replace an arbitrary resolution cut-off based on a value of Rmerge > with an arbitrary resolution cut-off based on a value of Rmeas already? And > now we are going to replace that with an arbitrary resolution cut-off based > on a value of CC* or is it CC1/2? > > I am asked often: What value of CC1/2 should I cut my resolution at? What > should I tell my students? I've got a course coming up and I am sure they > will ask me again. > > Jim > > From: CCP4 bulletin board [CCP4BB@JISCMAIL.AC.UK] on behalf of Arka > Chakraborty [arko.chakrabort...@gmail.com] > Sent: Tuesday, August 27, 2013 7:45 AM > To: CCP4BB@JISCMAIL.AC.UK > Subject: Re: [ccp4bb] Resolution, R factors and data quality > > Hi all, > does this not again bring up the still prevailing adherence to R factors and > not a shift to correlation coefficients ( CC1/2 and CC*) ? (as Dr. Phil > Evans has indicated).? > The way we look at data quality ( by "we" I mean the end users ) needs to be > altered, I guess. > > best, > > Arka Chakraborty > > On Tue, Aug 27, 2013 at 9:50 AM, Phil Evans <p...@mrc-lmb.cam.ac.uk> wrote: > The question you should ask yourself is "why would omitting data improve my > model?" > > Phil
