Hi Edward.
For 68 residues, i get this:
# Parameter description: The population for state A.
# mol_name res_num res_name spin_num spin_name value
error
None 10 G None N
0.995627205128479 None
# Parameter description: The exchange rate.
# mol_name res_num res_name spin_num spin_name value
error
None 10 G None N
843.138159024003 None
# Parameter description: The chemical shift difference between states
A and B (in ppm).
#
# mol_name res_num res_name spin_num spin_name value
error
None 10 G None N
5.66240190353847 None
None 11 D None N
7.24018919066445 None
None 15 Q None N
1.27388427761583 None
None 16 G None N
1.70637956735682 None
None 37 G None N
1.39439146332152 None
None 41 G None N
2.00489971256184 None
None 42 L None N
1.14631824779138 None
None 43 H None N
4.11812700604344 None
None 46 H None N
6.74721119884288 None
None 47 V None N
18.6941069719393 None
None 49 E None N
7.41204467390198 None
None 50 E None N
6.73571806460759 None
None 51 E None N
2.6906237906698 None
None 53 N None N
3.43636822580998 None
None 54 T None N
1.72434404944155 None
None 56 G None N
7.38662030427091 None
None 57 C None N
1.88126168471543 None
None 58 T None N
6.61594197477923 None
None 61 G None N
3.42205122284923 None
None 67 L None N
4.00714078384803 None
None 68 S None N
3.02933093965657 None
None 70 K None N
2.65894254799687 None
None 72 G None N
4.01752138022632 None
None 73 G None N
3.10502419263122 None
None 75 K None N
5.52331683531287 None
None 78 E None N
2.39121460031728 None
None 79 R None N
2.95565292785431 None
None 80 H None N
10.6521951761457 None
None 81 V None N
6.46552900214463 None
None 82 G None N
5.48378904252769 None
None 85 G None N
4.72783895083071 None
None 86 N None N
2.2535643167938 None
None 87 V None N
3.42430152185329 None
None 102 S None N
1.33719888517455 None
None 103 V None N
1.78945522230369 None
None 104 I None N
2.1193021535956 None
None 105 S None N
1.20023816089299 None
None 111 A None N
3.68849791596676 None
None 112 I None N
1.92921136977377 None
None 115 R None N
2.1336531230742 None
None 118 V None N
1.1301287075642 None
None 121 E None N
1.68619193009267 None
None 123 A None N
4.91019478151119 None
None 126 L None N
7.6255827307843 None
None 127 G None N
4.89765215595432 None
None 128 K None N
2.26502557102985 None
None 129 G None N
1.79003350167683 None
None 130 G None N
1.74650398353974 None
None 131 N None N
4.91476102864345 None
None 133 E None N
1.02559032422555 None
None 134 S None N
0.842131709855722 None
None 135 T None N
9.20627022843478 None
None 137 T None N
8.00007213116674 None
None 138 G None N
1.9412902050166 None
None 139 N None N
6.51160366265863 None
None 140 A None N
8.89216425477085 None
None 141 G None N
2.354941400505 None
None 142 S None N
3.50895251891688 None
None 143 R None N
2.65884864234097 None
None 146 C None N
2.92485233744021 None
None 147 G None N
4.71130879214043 None
So dw is moving fine.
But we do though think that dw has high values.
There is a 18 ppm and 10 ppm in there.
Now trying with ShereKhan.
Best
Troels
2014-04-30 10:45 GMT+02:00 Edward d'Auvergne <[email protected]>:
> Hi,
>
> I should expand on the statistics a bit more. Maybe using AIC would
> clarify the noise vs. real data components. Here is a short table:
>
> Set Chi2 k AIC
> Individual 32.97 10 52.97
> Cluster 48.79 8 64.79
>
> So even using AIC, the individual fit is better. Statistically it is
> not that you are just fitting more noise in the non-clustered fit.
> That is significant! One thing I noticed is that dw is the same for
> both spins in the clustered fit. Could you check if this is the case
> for other clustering cases? It must be different for each spin.
> Maybe there is an important bug there.
>
> Regards,
>
> Edward
>
>
>
> On 30 April 2014 10:16, Edward d'Auvergne <[email protected]> wrote:
>>> I tried to generate sherekhan output, but since I have time_T2 of 0.04
>>> and 0.06, for the two fields,
>>> I cannot generate the input files for ShereKhan.
>>
>> ShereKhan should support this, and it would be a good test for relax.
>> The second line of the input file has this time. Was it that relax
>> could not create the input files rather than ShereKhan not handling
>> this?
>>
>>
>>> My problem origins from that I would like to compare results from Igor
>>> Pro script.
>>> Yet, another software solution.
>>
>> Have you run the Igor Pro script to compare to relax? With the same
>> input data, all software solutions should give the same result. This
>> is important - you need to determine if the issue is with relax or
>> with the data itself. It is best to first assume that the problem is
>> with relax, then see if other software produces a different result
>> (the more comparisons here the better). Maybe relax is not handling
>> the two different times correctly. Otherwise if everything has the pA
>> = 0.5 problem then the solution, if one exists, will be very
>> different.
>>
>>
>>> I now got the expected pA values of 0.97 if I did a cluster of two residues.
>>
>> This could indicate that the pA = 0.5 issue is in the data itself,
>> probably due to noise. You should confirm this by comparing to other
>> software though. Comparing to the 'NS CPMG 2-site expanded' might
>> also be useful.
>>
>>
>>> If I do an initial Grid inc of 21, use
>>> relax_disp.set_grid_r20_from_min_r2eff(force=False) I get this.
>>
>> As I mentioned before
>> (http://thread.gmane.org/gmane.science.nmr.relax.scm/20597/focus=5390),
>> maybe it would be better to shorten this user function name as it is a
>> little misleading - it is about custom value setting and not the grid
>> search, despite it being useful for the later.
>>
>>
>>> :10@N GRID r2600=20.28 r2500=18.48 dw=1.0 pA=0.900 kex=2000.80
>>> chi2=28.28 spin_id=:10@N resi=10 resn=G
>>> :10@N MIN r2600=19.64 r2500=17.88 dw=0.7 pA=0.500 kex=2665.16
>>> chi2=14.61 spin_id=:10@N resi=10 resn=G
>>> :10@N Clust r2600=18.43 r2500=16.98 dw=2.7 pA=0.972 kex=3831.77
>>> chi2=48.79 spin_id=:10@N resi=10 resn=G
>>>
>>> :11@N GRID r2600=19.54 r2500=17.96 dw=1.0 pA=0.825 kex=3500.65
>>> chi2=47.22 spin_id=:11@N resi=11 resn=D
>>> :11@N MIN r2600=14.98 r2500=15.08 dw=1.6 pA=0.760 kex=6687.15
>>> chi2=18.36 spin_id=:11@N resi=11 resn=D
>>> :11@N Clust r2600=18.19 r2500=17.31 dw=2.7 pA=0.972 kex=3831.77
>>> chi2=48.79 spin_id=:11@N resi=11 resn=D
>>
>> If you sum the chi-squared values, which is possible as these are all
>> the same model, then you can compare the individual fits and the
>> clustered fit. The individual fit total chi-squared value is 32.97.
>> The cluster value is 48.79. This is very important - the individual
>> fit is much, much better. You should make a plot of the fitted curves
>> for both and compare. Note that a better fit does not mean a better
>> result, as you are fitting both a data component and noise component.
>> So the better fit might be due to the noise component. This is why
>> clustering exists.
>>
>>
>>> Ideally, I would like to cluster 68 residues.
>>>
>>> But as you can see, if several of my residues start out with dw/pA far
>>> from the Clustered result, this minimisation takes
>>> hilarious long time.
>>
>> I can see how this would be a problem for you mass screening
>> exercises. This will probably require a lot of investigation on your
>> part to solve, as I have not seen any solution published in the
>> literature. Though if you could find a solution in the literature,
>> that would probably save you a lot of time. You could also ask others
>> in the field. If you remember
>> (http://thread.gmane.org/gmane.science.nmr.relax.devel/4647/focus=4648),
>> you changed the parameter averaging to the parameter median for the
>> clustering. So maybe that is having an effect. Anyway, you need to
>> first compare to other software or models and see if there is a
>> problem in relax first, before trying to invent a solution.
>>
>> Regards,
>>
>> Edward
_______________________________________________
relax (http://www.nmr-relax.com)
This is the relax-devel mailing list
[email protected]
To unsubscribe from this list, get a password
reminder, or change your subscription options,
visit the list information page at
https://mail.gna.org/listinfo/relax-devel
