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Hi Ian,

thanks for very detailed reply!

[log in to unmask]" type="cite"> 
- do you think it is better than looking at three values {map CC, 2mFo-DFc,
mFo-DFc} and why?
    

Yes, because all the information you need is encapsulated in 1 number
per region of interest!

I agree it's a good reason.

[log in to unmask]" type="cite"> 
But I don't understand what you mean by
2mFo-DFc & mFo-DFc being counted each as 1 number.

Given the map and model, you can get the map value at (x,y,z) position, for example, at the center of atom. This is what phenix.model_vs_data reports. For each atom you get three numbers: map CC, and the values of 2mFo-DFc and mFo-DFc maps at the atomic position.

[log in to unmask]" type="cite"> 
By suggesting to use {map CC, 2mFo-DFc, mFo-DFc} I was assuming that:
- map CC will tell you about similarities of shapes and it will not tell you
about how strong the density is, indeed.  So, using map CC alone is clearly
insufficient. Also, we more or less have feeling about the values, which is
helpful.
- 2mFo-DFc will tell you about the strength of the density. I mean, if you
get 2.5sigma at the center of atom A -  it's good (provided that map CC is
good), and if it is 0.3sigma you should get puzzled.
- Having excess of +/- mFo-DFc density will tell you something too.
    

The problem is how is all this information quantified in an objective
and statistically justifiable way in order to arrive at a firm
conclusion?

We can more or less relate these values to the map appearance and model-to-map fit quality. Looking at these numbers one can approximately tell whether it's good, so so, or bad. It's like crystallographic reciprocal space R-factor. If I see R=35% for a structure at 2A resolution - it's not good, and R=17% is much better.

Anyway I will code that formula and play with it.

Thanks again!
Pavel.