Re: Relaxation curve fitting
Hi Edward, Thank you for your suggestions. I have tried to open qtgrace first and then open the intensities file, but I can not seen any curve in qtgrace. I have submitted the bug report. Regards, Mengjun Citat af Edward d'Auvergne edw...@nmr-relax.com: Hi Mengjun, This looks like a bug in relax on Windows with spaces in the directory name! I thought I fixed this many, many years ago - maybe it has resurfaced in a new place. Could you please submit a bug report with this issue? Actually, before you do that, can you open qtgrace and then open this file? If the error is in qtgrace, then the bug report is not needed as there is nothing relax can do to fix it. You can submit a bug using the link https://gna.org/bugs/?func=additemgroup=relax. You can also attach the file there. Note that you should not have your data files in the same directory as relax. You should always keep your data files separate from the software files. Mixing the files together is quite dangerous and might result in program files or directories being overwritten by data and results files. If you place your files into a directory on the C:\ drive without any spaces, this problem will not appear. Regards, Edward On 4 March 2014 18:38, mengjun@mailbox.tu-berlin.de wrote: Hi Edward, Thank you very much for your suggestion. As you suggested, I have started grace.view user function under Relax Gui, and select qtgrace.exe file and intensities.agr file, but a error occurs: [Error] Can't stat file C:\\Program [Error] Can't stat file Files\\relax-3.1.5\\grace\\intensities.agr Please find the intensities.agr file (which include 3 residues for test) in the attachment. Thank you. With best regards, Mengjun Xue Citat af Edward d'Auvergne edw...@nmr-relax.com: Hi Mengjun, If you are using the GUI, you don't need to change the qtgrace.exe file. The grace.view user function window allows you to choose the Grace executable file. Just click on the Select the file button and select the qtgrace.exe file. Regards, Edward On 3 March 2014 17:51, mengjun@mailbox.tu-berlin.de wrote: Hi Troels and Martin, Thank you so much for your responses. According to your suggestions, the raw intensities data can be extracted from results.bz file or rx.save.bz2 file now. For the xmgrace installation, I have downloaded qtgrace at http://sourceforge.net/projects/qtgrace/, and uppack it to C:\Python27\qtgrace_windows_binary, in the C:\Python27\qtgrace_windows_binary\bin folder, I found qtgrace.exe,but I did not find xmgrace.exe, how to put both qtgrace.exe and xmgrace.exe in the same bin folder? xmgrace.exe should be downloaded from internet and then put it in the same bin file? Thank you so much. Best regards, Mengjun Quoting Troels Emtekær Linnet tlin...@nmr-relax.com: Dear Mengjun. For xmgrace installation, follow this: http://wiki.nmr-relax.com/Installation_windows_Python_x86-32_Visual_Studio_Express_for_Windows_Desktop#xmgrace_-_for_the_plotting_results_of_NMR-relax In short. 1 ) Download and install 2) Copy qtgrace.exe to xmgrace.exe in same folder 3) Add to your windows path, the path to where xmgrace.exe resides. 4) Test it with opening cmd and write xmgrace. (You may need to restart computer to update PATH) Or if you have matplob lib, try this tutorial: http://wiki.nmr-relax.com/Matplotlib_example 2014-03-03 16:11 GMT+01:00 mengjun@mailbox.tu-berlin.de: Hi Edward, I have tried to use relax_fit.py to extract R1 data, I have got 3 files: rx.out file (R1 values), rx.save.bz2 file, and results.bz2 file, as Xmgrace is not available on my computer, I want to display the intensity decay curves in others software, so how to extract the raw data from the output (results.bz2) of relax_fit.py? It seems rx.save.bz2 file is same to results.bz2 file. Thank you very much. With best regards, Mengjun Xue ___ relax (http://www.nmr-relax.com) This is the relax-users mailing list relax-users@gna.org 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-users ___ relax (http://www.nmr-relax.com) This is the relax-users mailing list relax-users@gna.org 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-users
Re: Relaxation curve fitting
Dear Mengjun. For xmgrace installation, follow this: http://wiki.nmr-relax.com/Installation_windows_Python_x86-32_Visual_Studio_Express_for_Windows_Desktop#xmgrace_-_for_the_plotting_results_of_NMR-relax In short. 1 ) Download and install 2) Copy qtgrace.exe to xmgrace.exe in same folder 3) Add to your windows path, the path to where xmgrace.exe resides. 4) Test it with opening cmd and write xmgrace. (You may need to restart computer to update PATH) Or if you have matplob lib, try this tutorial: http://wiki.nmr-relax.com/Matplotlib_example 2014-03-03 16:11 GMT+01:00 mengjun@mailbox.tu-berlin.de: Hi Edward, I have tried to use relax_fit.py to extract R1 data, I have got 3 files: rx.out file (R1 values), rx.save.bz2 file, and results.bz2 file, as Xmgrace is not available on my computer, I want to display the intensity decay curves in others software, so how to extract the raw data from the output (results.bz2) of relax_fit.py? It seems rx.save.bz2 file is same to results.bz2 file. Thank you very much. With best regards, Mengjun Xue ___ relax (http://www.nmr-relax.com) This is the relax-users mailing list relax-users@gna.org 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-users ___ relax (http://www.nmr-relax.com) This is the relax-users mailing list relax-users@gna.org 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-users
Re: Relaxation curve fitting
Dear Mengjun. Let me extend the previous explanation. Use the GUI to load results.bz2 file. Then use the User function: Value write, to write a text file with the desired results. These are just flat text files as: rx.out and can include intensities instead, of normalized intensities. Use these files to plot in any program. Or use the User function: Grace write, to make grace files. Best Troels 2014-03-03 16:33 GMT+01:00 Troels Emtekær Linnet tlin...@nmr-relax.com: Dear Mengjun. For xmgrace installation, follow this: http://wiki.nmr-relax.com/Installation_windows_Python_x86-32_Visual_Studio_Express_for_Windows_Desktop#xmgrace_-_for_the_plotting_results_of_NMR-relax In short. 1 ) Download and install 2) Copy qtgrace.exe to xmgrace.exe in same folder 3) Add to your windows path, the path to where xmgrace.exe resides. 4) Test it with opening cmd and write xmgrace. (You may need to restart computer to update PATH) Or if you have matplob lib, try this tutorial: http://wiki.nmr-relax.com/Matplotlib_example 2014-03-03 16:11 GMT+01:00 mengjun@mailbox.tu-berlin.de: Hi Edward, I have tried to use relax_fit.py to extract R1 data, I have got 3 files: rx.out file (R1 values), rx.save.bz2 file, and results.bz2 file, as Xmgrace is not available on my computer, I want to display the intensity decay curves in others software, so how to extract the raw data from the output (results.bz2) of relax_fit.py? It seems rx.save.bz2 file is same to results.bz2 file. Thank you very much. With best regards, Mengjun Xue ___ relax (http://www.nmr-relax.com) This is the relax-users mailing list relax-users@gna.org 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-users ___ relax (http://www.nmr-relax.com) This is the relax-users mailing list relax-users@gna.org 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-users
Re: relaxation curve fitting
. This is the model for the very old-school inversion
recovery type R1 experiments whereby the magnetisation returns to the
Boltzmann equilibrium. I'm guessing you should be using the 'exp'
model instead. This is the standard 2 parameter exponential fit
whereby the magnetisation goes to zero. This is the standard nowadays
as it is considered far more accurate for the extraction of the rates
(simply by having one less parameter to fit).
If you have collected the old-school data, there is a relax branch
created by Sébastien Morin for handling this experiment type. This is
the 'inversion-recovery' branch located at
http://svn.gna.org/viewcvs/relax/branches/inversion-recovery/.
However this branch is not complete and will require someone willing
to dive into C code to complete it (see
http://www.mail-archive.com/relax-devel@gna.org/msg03353.html). Note
that if someone does know C, completing this will require about 50
lines of code changed in the maths_fns/relax_fit.c and
maths_fns/exponential.c files (my rough guess anyway). It should be
incredibly trivial for someone with C knowledge. Anyway, I hope some
of this info helps.
Regards,
Edward
On 30 June 2012 18:01, Romel Bobby rbob...@aucklanduni.ac.nz wrote:
Dear all,
I've been trying to use the curve fitting routine for R1 and R2 in
relax
using the sample script relax_fit.py. I managed to read in the
spectra
and
obtain a value for the uncertainty. However, once it gets to the
point
of
performing a grid_search that's where it fails (see below). Has
anyone
had a
similar problem?
[?1034h
relax 2.0.0
Molecular dynamics by NMR data analysis
Copyright (C) 2001-2006 Edward
d'Auvergne
Copyright (C) 2006-2012 the relax
development
team
This is free software which you are welcome to modify and
redistribute
under
the conditions of the
GNU General Public License (GPL). This program, including all
modules,
is
licensed under the GPL
and comes with absolutely no warranty. For details type 'GPL' within
the
relax prompt.
Assistance in using the relax prompt and scripting interface can be
accessed
by typing 'help' within
the prompt.
Processor fabric: Uni-processor.
script = 'relax_fit.py'
###
#
#
# Copyright (C) 2004-2012 Edward d'Auvergne
#
#
#
# This file is part of the program relax.
#
#
#
# relax is free software; you can redistribute it and/or modify
#
# it under the terms of the GNU General Public License as published
by
#
# the Free Software Foundation; either version 2 of the License, or
#
# (at your option) any later version.
#
#
#
# relax is distributed in the hope that it will be useful,
#
# but WITHOUT ANY WARRANTY; without even the implied warranty of
#
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
#
# GNU General Public License for more details.
#
#
#
# You should have received a copy of the GNU General Public License
#
# along with relax; if not, write to the Free Software
#
# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA
02111-1307
USA
#
#
#
###
Script for relaxation curve fitting.
# Create the 'rx' data pipe.
pipe.create('rx', 'relax_fit')
# Load the backbone amide 15N spins from a PDB file.
structure.read_pdb('IL6_mf.pdb')
structure.load_spins(spin_id='@N')
# Spectrum names.
names = [
'T1_1204.04',
'T1_1504.04',
'T1_1804.04',
'T1_2104.04',
'T1_2404.04',
'T1_2754.04',
'T1_304.04',
'T1_304.040',
'T1_54.04',
'T1_604.04',
'T1_604.040',
'T1_904.04',
]
# Relaxation times (in seconds).
times = [
1.204,
1.504,
1.804,
2.104,
2.404,
2.754,
0.304,
0.304,
0.054,
0.604,
0.604,
0.904
]
# Loop over the spectra.
for i in xrange(len(names)):
# Load the peak intensities.
spectrum.read_intensities(file=names[i]+'.list', dir='',
spectrum_id=names[i], int_method='height')
# Set the relaxation times.
relax_fit.relax_time(time=times[i], spectrum_id=names[i])
# Specify the duplicated spectra.
spectrum.replicated(spectrum_ids=['T1_304.04', 'T1_304.040'])
spectrum.replicated(spectrum_ids=['T1_604.04', 'T1_604.040'])
# Peak
Re: relaxation curve fitting
Hi Romel,
The problem is that unfortunately the 'inv' model is simply not
implemented yet. This is the model for the very old-school inversion
recovery type R1 experiments whereby the magnetisation returns to the
Boltzmann equilibrium. I'm guessing you should be using the 'exp'
model instead. This is the standard 2 parameter exponential fit
whereby the magnetisation goes to zero. This is the standard nowadays
as it is considered far more accurate for the extraction of the rates
(simply by having one less parameter to fit).
If you have collected the old-school data, there is a relax branch
created by Sébastien Morin for handling this experiment type. This is
the 'inversion-recovery' branch located at
http://svn.gna.org/viewcvs/relax/branches/inversion-recovery/.
However this branch is not complete and will require someone willing
to dive into C code to complete it (see
http://www.mail-archive.com/relax-devel@gna.org/msg03353.html). Note
that if someone does know C, completing this will require about 50
lines of code changed in the maths_fns/relax_fit.c and
maths_fns/exponential.c files (my rough guess anyway). It should be
incredibly trivial for someone with C knowledge. Anyway, I hope some
of this info helps.
Regards,
Edward
On 30 June 2012 18:01, Romel Bobby rbob...@aucklanduni.ac.nz wrote:
Dear all,
I've been trying to use the curve fitting routine for R1 and R2 in relax
using the sample script relax_fit.py. I managed to read in the spectra and
obtain a value for the uncertainty. However, once it gets to the point of
performing a grid_search that's where it fails (see below). Has anyone had a
similar problem?
[?1034h
relax 2.0.0
Molecular dynamics by NMR data analysis
Copyright (C) 2001-2006 Edward d'Auvergne
Copyright (C) 2006-2012 the relax development team
This is free software which you are welcome to modify and redistribute under
the conditions of the
GNU General Public License (GPL). This program, including all modules, is
licensed under the GPL
and comes with absolutely no warranty. For details type 'GPL' within the
relax prompt.
Assistance in using the relax prompt and scripting interface can be accessed
by typing 'help' within
the prompt.
Processor fabric: Uni-processor.
script = 'relax_fit.py'
###
#
#
# Copyright (C) 2004-2012 Edward d'Auvergne
#
#
#
# This file is part of the program relax.
#
#
#
# relax is free software; you can redistribute it and/or modify
#
# it under the terms of the GNU General Public License as published by
#
# the Free Software Foundation; either version 2 of the License, or
#
# (at your option) any later version.
#
#
#
# relax is distributed in the hope that it will be useful,
#
# but WITHOUT ANY WARRANTY; without even the implied warranty of
#
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
#
# GNU General Public License for more details.
#
#
#
# You should have received a copy of the GNU General Public License
#
# along with relax; if not, write to the Free Software
#
# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
#
#
#
###
Script for relaxation curve fitting.
# Create the 'rx' data pipe.
pipe.create('rx', 'relax_fit')
# Load the backbone amide 15N spins from a PDB file.
structure.read_pdb('IL6_mf.pdb')
structure.load_spins(spin_id='@N')
# Spectrum names.
names = [
'T1_1204.04',
'T1_1504.04',
'T1_1804.04',
'T1_2104.04',
'T1_2404.04',
'T1_2754.04',
'T1_304.04',
'T1_304.040',
'T1_54.04',
'T1_604.04',
'T1_604.040',
'T1_904.04',
]
# Relaxation times (in seconds).
times = [
1.204,
1.504,
1.804,
2.104,
2.404,
2.754,
0.304,
0.304,
0.054,
0.604,
0.604,
0.904
]
# Loop over the spectra.
for i in xrange(len(names)):
# Load the peak intensities.
spectrum.read_intensities(file=names[i]+'.list', dir='',
spectrum_id=names[i], int_method='height')
# Set the relaxation times.
relax_fit.relax_time(time=times[i], spectrum_id=names[i])
# Specify the duplicated spectra.
spectrum.replicated(spectrum_ids=['T1_304.04', 'T1_304.040'])
spectrum.replicated(spectrum_ids=['T1_604.04', 'T1_604.040'])
# Peak intensity error analysis.
spectrum.error_analysis()
# Deselect unresolved spins.
# deselect.read(file='unresolved', mol_name_col=1, res_num_col=2,
res_name_col=3, spin_num_col=4, spin_name_col=5)
# Set the relaxation curve type.
relax_fit.select_model('inv')
# Grid search
Re: Curve fitting
the peak volume as a measure of peak intensity,
then we have a statistical problem. Until someone finds a reference
or derives the formula for how the RMSD of the base plane noise
relates to volume error, then peak heights will be essential for error
analysis. I'm also willing to be corrected on any of the statistics
above as I'm not an expert in this and may have missed some
fundamental connections between theories. And there may be a less
'dirty' way of doing the dirty part of the statistics.
On Fri, Oct 17, 2008 at 1:59 AM, Chris MacRaild [EMAIL PROTECTED] wrote:
On Thu, Oct 16, 2008 at 8:07 PM, Edward d'Auvergne
[EMAIL PROTECTED] wrote:
On Thu, Oct 16, 2008 at 7:02 AM, Chris MacRaild [EMAIL PROTECTED] wrote:
On Thu, Oct 16, 2008 at 3:11 PM, Sébastien Morin
[EMAIL PROTECTED] wrote:
Hi,
I have a general question about curve fitting within relax.
Let's say I proceed to curve fitting for some relaxation rates
(exponential decay) and that I have a duplicate delay for error
estimation.
delays
0.01
0.01
0.02
0.04
...
Will the mean value (for delay 0.01) be used for curve fitting and rate
extraction ?
Or will both values at delay 0.01 be used during curve fitting, hence
giving more weight on delay 0.01 ?
In other words, will the fit only use both values at delay 0.01 for
error estimation or also for rate extraction, giving more weight for
this duplicate point ?
How is this handled in relax ?
Instinctively, I would guess that the man value must be used for
fitting, as we don't want the points that are not in duplicate to count
less in the fitting procedure... Am I right ?
I would argue not. If we have gone to the trouble of measuring
something twice (or, equivalently, measuring it with greater
precision) then we should weight it more strongly to reflect that.
So we should include both duplicate points in our fit, or we should
just use the mean value, but weight it to reflect the greater
certainty we have in its value.
As I type this I realise this is likely the source of the sqrt(2)
factor Tyler and Edward have been debating on a parallel thread - the
uncertainty in height of any one peak is equal to the RMS noise, but
the std error of the mean of duplicates is less by a factor of
sqrt(2).
At the moment, relax simply uses the mean value in the fit. Despite
the higher quality of the duplicated data, all points are given the
same weight. This is only because of the low data quantity. As for
dividing the sd of differences between duplicate spectra by sqrt(2),
this is not done in relax anymore. Because some people have collected
triplicate spectra, although rare, relax calculates the error from
replicated spectra differently. I'm prepared to be told that this
technique is incorrect though. The procedure relax uses is to apply
the formula:
sd^2 = sum({Ii - Iav}^2) / (n - 1),
where n is the number of spectra, Ii is the intensity in spectrum i,
Iav is the average intensity, sd is the standard deviation, and sd^2
is the variance. This is for a single spin. The sample number is so
low that this value is completely meaningless. Therefore the variance
is averaged across all spins (well due to a current bug the standard
deviation is averaged instead). Then another averaging takes place if
not all spectra are duplicated. The variances across all duplicated
spectra are averaged to give a single error value for all spins across
all spectra (again the sd averaging bug affects this). The reason for
using this approach is that you are not limited to duplicate spectra.
It also means that the factor of sqrt(2) is not applicable. If only
single spectra are collected, then relax's current behaviour of not
using sqrt(2) seems reasonable.
Here is how I understand the sqrt(2) issue:
The sd of duplicate (or triplicate, or quadruplicate, or ... ) peak
heights is assumed to give a good estimate of the precision with which
we can measure the height of a single peak. So for peak heights that
have not been measured in duplicate (ie relaxation times that have not
been duplicated in our current set of spectra), sd is a good estimate
of the uncertainty associated with that height.
For peaks we have measured more than once, we can calculate a mean
peak height. The precision with which we know that mean value is given
by the std error of the mean ie. sd/sqrt(n) where n is the number of
times we have measured that specific relaxation time. I think this is
the origin of the sqrt(2) for duplicate data.
A made up example:
T I
0 1.00
100.90
100.86
200.80
400.75
700.72
700.68
100 0.55
150 0.40
200 0.30
The std deviation of our duplicates is 0.04 so the uncertainty on each
value above is 0.04
BUT, the uncertainty on the mean values for our duplicate time points
(10 and 70) is 0.04/sqrt(2) = 0.028
So if we use
Re: Curve fitting
the formula for how the RMSD of the base plane noise
relates to volume error, then peak heights will be essential for error
analysis. I'm also willing to be corrected on any of the statistics
above as I'm not an expert in this and may have missed some
fundamental connections between theories. And there may be a less
'dirty' way of doing the dirty part of the statistics.
On Fri, Oct 17, 2008 at 1:59 AM, Chris MacRaild [EMAIL PROTECTED] wrote:
On Thu, Oct 16, 2008 at 8:07 PM, Edward d'Auvergne
[EMAIL PROTECTED] wrote:
On Thu, Oct 16, 2008 at 7:02 AM, Chris MacRaild [EMAIL PROTECTED] wrote:
On Thu, Oct 16, 2008 at 3:11 PM, Sébastien Morin
[EMAIL PROTECTED] wrote:
Hi,
I have a general question about curve fitting within relax.
Let's say I proceed to curve fitting for some relaxation rates
(exponential decay) and that I have a duplicate delay for error estimation.
delays
0.01
0.01
0.02
0.04
...
Will the mean value (for delay 0.01) be used for curve fitting and rate
extraction ?
Or will both values at delay 0.01 be used during curve fitting, hence
giving more weight on delay 0.01 ?
In other words, will the fit only use both values at delay 0.01 for
error estimation or also for rate extraction, giving more weight for
this duplicate point ?
How is this handled in relax ?
Instinctively, I would guess that the man value must be used for
fitting, as we don't want the points that are not in duplicate to count
less in the fitting procedure... Am I right ?
I would argue not. If we have gone to the trouble of measuring
something twice (or, equivalently, measuring it with greater
precision) then we should weight it more strongly to reflect that.
So we should include both duplicate points in our fit, or we should
just use the mean value, but weight it to reflect the greater
certainty we have in its value.
As I type this I realise this is likely the source of the sqrt(2)
factor Tyler and Edward have been debating on a parallel thread - the
uncertainty in height of any one peak is equal to the RMS noise, but
the std error of the mean of duplicates is less by a factor of
sqrt(2).
At the moment, relax simply uses the mean value in the fit. Despite
the higher quality of the duplicated data, all points are given the
same weight. This is only because of the low data quantity. As for
dividing the sd of differences between duplicate spectra by sqrt(2),
this is not done in relax anymore. Because some people have collected
triplicate spectra, although rare, relax calculates the error from
replicated spectra differently. I'm prepared to be told that this
technique is incorrect though. The procedure relax uses is to apply
the formula:
sd^2 = sum({Ii - Iav}^2) / (n - 1),
where n is the number of spectra, Ii is the intensity in spectrum i,
Iav is the average intensity, sd is the standard deviation, and sd^2
is the variance. This is for a single spin. The sample number is so
low that this value is completely meaningless. Therefore the variance
is averaged across all spins (well due to a current bug the standard
deviation is averaged instead). Then another averaging takes place if
not all spectra are duplicated. The variances across all duplicated
spectra are averaged to give a single error value for all spins across
all spectra (again the sd averaging bug affects this). The reason for
using this approach is that you are not limited to duplicate spectra.
It also means that the factor of sqrt(2) is not applicable. If only
single spectra are collected, then relax's current behaviour of not
using sqrt(2) seems reasonable.
Here is how I understand the sqrt(2) issue:
The sd of duplicate (or triplicate, or quadruplicate, or ... ) peak
heights is assumed to give a good estimate of the precision with which
we can measure the height of a single peak. So for peak heights that
have not been measured in duplicate (ie relaxation times that have not
been duplicated in our current set of spectra), sd is a good estimate
of the uncertainty associated with that height.
For peaks we have measured more than once, we can calculate a mean
peak height. The precision with which we know that mean value is given
by the std error of the mean ie. sd/sqrt(n) where n is the number of
times we have measured that specific relaxation time. I think this is
the origin of the sqrt(2) for duplicate data.
A made up example:
T I
0 1.00
100.90
100.86
200.80
400.75
700.72
700.68
100 0.55
150 0.40
200 0.30
The std deviation of our duplicates is 0.04 so the uncertainty on each
value above is 0.04
BUT, the uncertainty on the mean values for our duplicate time points
(10 and 70) is 0.04/sqrt(2) = 0.028
So if we use the mean values as points in our fit, we should use 0.028
as the uncertainty on those values (while all other peaks have
uncertainty 0.04
Re: Curve fitting
was saying sigma was the variance, but just
ignore that. Next we need to average the variance across all spins,
simply because the sample size is so low for each peak and hence the
error estimate is horrible. Whether this estimator of the true
variance is good or bad is debatable (well, actually, it's bad), but
it is unavoidable. It also has the obvious disadvantage in that the
peak height error is, in reality, different for each peak.
Now, if not all spectra are replicated, then the approach needs to be
modified to give us errors for the peaks of the single spectra. Each
averaged replicated time point (spectrum) has a single error
associated with it, the average variance. These are usually different
for different time points, in some cases weakly decreasing
exponentially. So I think we should average the average variances and
have a single measure of spread for all peaks in all spectra. This
estimator of the variance is again bad. Interpolation might be
better, but is still not great.
Cheers,
Edward
P.S. None of this affects an analysis using peak heights of
non-replicated spectra and the RMSD of the baseplane noise. But if
someone wants to use the peak volume as a measure of peak intensity,
then we have a statistical problem. Until someone finds a reference
or derives the formula for how the RMSD of the base plane noise
relates to volume error, then peak heights will be essential for error
analysis. I'm also willing to be corrected on any of the statistics
above as I'm not an expert in this and may have missed some
fundamental connections between theories. And there may be a less
'dirty' way of doing the dirty part of the statistics.
On Fri, Oct 17, 2008 at 1:59 AM, Chris MacRaild [EMAIL PROTECTED] wrote:
On Thu, Oct 16, 2008 at 8:07 PM, Edward d'Auvergne
[EMAIL PROTECTED] wrote:
On Thu, Oct 16, 2008 at 7:02 AM, Chris MacRaild [EMAIL PROTECTED] wrote:
On Thu, Oct 16, 2008 at 3:11 PM, Sébastien Morin
[EMAIL PROTECTED] wrote:
Hi,
I have a general question about curve fitting within relax.
Let's say I proceed to curve fitting for some relaxation rates
(exponential decay) and that I have a duplicate delay for error
estimation.
delays
0.01
0.01
0.02
0.04
...
Will the mean value (for delay 0.01) be used for curve fitting and rate
extraction ?
Or will both values at delay 0.01 be used during curve fitting, hence
giving more weight on delay 0.01 ?
In other words, will the fit only use both values at delay 0.01 for
error estimation or also for rate extraction, giving more weight for
this duplicate point ?
How is this handled in relax ?
Instinctively, I would guess that the man value must be used for
fitting, as we don't want the points that are not in duplicate to count
less in the fitting procedure... Am I right ?
I would argue not. If we have gone to the trouble of measuring
something twice (or, equivalently, measuring it with greater
precision) then we should weight it more strongly to reflect that.
So we should include both duplicate points in our fit, or we should
just use the mean value, but weight it to reflect the greater
certainty we have in its value.
As I type this I realise this is likely the source of the sqrt(2)
factor Tyler and Edward have been debating on a parallel thread - the
uncertainty in height of any one peak is equal to the RMS noise, but
the std error of the mean of duplicates is less by a factor of
sqrt(2).
At the moment, relax simply uses the mean value in the fit. Despite
the higher quality of the duplicated data, all points are given the
same weight. This is only because of the low data quantity. As for
dividing the sd of differences between duplicate spectra by sqrt(2),
this is not done in relax anymore. Because some people have collected
triplicate spectra, although rare, relax calculates the error from
replicated spectra differently. I'm prepared to be told that this
technique is incorrect though. The procedure relax uses is to apply
the formula:
sd^2 = sum({Ii - Iav}^2) / (n - 1),
where n is the number of spectra, Ii is the intensity in spectrum i,
Iav is the average intensity, sd is the standard deviation, and sd^2
is the variance. This is for a single spin. The sample number is so
low that this value is completely meaningless. Therefore the variance
is averaged across all spins (well due to a current bug the standard
deviation is averaged instead). Then another averaging takes place if
not all spectra are duplicated. The variances across all duplicated
spectra are averaged to give a single error value for all spins across
all spectra (again the sd averaging bug affects this). The reason for
using this approach is that you are not limited to duplicate spectra.
It also means that the factor of sqrt(2) is not applicable. If only
single spectra are collected, then relax's current
Curve fitting
Hi, I have a general question about curve fitting within relax. Let's say I proceed to curve fitting for some relaxation rates (exponential decay) and that I have a duplicate delay for error estimation. delays 0.01 0.01 0.02 0.04 ... Will the mean value (for delay 0.01) be used for curve fitting and rate extraction ? Or will both values at delay 0.01 be used during curve fitting, hence giving more weight on delay 0.01 ? In other words, will the fit only use both values at delay 0.01 for error estimation or also for rate extraction, giving more weight for this duplicate point ? How is this handled in relax ? Instinctively, I would guess that the man value must be used for fitting, as we don't want the points that are not in duplicate to count less in the fitting procedure... Am I right ? Thanks for clarifying this... Séb ___ relax (http://nmr-relax.com) This is the relax-users mailing list relax-users@gna.org 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-users
Re: Curve fitting
On Thu, Oct 16, 2008 at 3:11 PM, Sébastien Morin [EMAIL PROTECTED] wrote: Hi, I have a general question about curve fitting within relax. Let's say I proceed to curve fitting for some relaxation rates (exponential decay) and that I have a duplicate delay for error estimation. delays 0.01 0.01 0.02 0.04 ... Will the mean value (for delay 0.01) be used for curve fitting and rate extraction ? Or will both values at delay 0.01 be used during curve fitting, hence giving more weight on delay 0.01 ? In other words, will the fit only use both values at delay 0.01 for error estimation or also for rate extraction, giving more weight for this duplicate point ? How is this handled in relax ? Instinctively, I would guess that the man value must be used for fitting, as we don't want the points that are not in duplicate to count less in the fitting procedure... Am I right ? I would argue not. If we have gone to the trouble of measuring something twice (or, equivalently, measuring it with greater precision) then we should weight it more strongly to reflect that. So we should include both duplicate points in our fit, or we should just use the mean value, but weight it to reflect the greater certainty we have in its value. As I type this I realise this is likely the source of the sqrt(2) factor Tyler and Edward have been debating on a parallel thread - the uncertainty in height of any one peak is equal to the RMS noise, but the std error of the mean of duplicates is less by a factor of sqrt(2). Chris ___ relax (http://nmr-relax.com) This is the relax-users mailing list relax-users@gna.org 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-users
One set data for curve-fitting!
Hello, I have tried to use the relax standard script to do curve-fitting for T1. However I just got one set of relaxation data(/i.e./ 5ms X 1, 120ms X 1, 240ms X1 ..), is it possible to use the script to fit the data? Could anybody give me some suggestion how to do it? Thanks! -- Xia,Wei Department of Chemistry The University of Hong Kong Pokfulam Road, Hong Kong P.R.China ___ relax (http://nmr-relax.com) This is the relax-users mailing list relax-users@gna.org 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-users
