Hi,
You have to be careful with error analysis as the errors are as
important as the data itself! By 4 sets of R1 values, I would assume
that you have these all at the same field strength. With this data
there are a number of ways the data can be used in relax and a number
of ways its error can be calculated.
For the error calculation there are a number of methods that can be
used. For example for a single R1 experiment, either the RMSD of the
base plane noise or duplicate spectra can be used. This allows you to
calculated an average error for all peaks in a single spectra (which
can then be averaged over all spectra if not all time points are
duplicated). Alternatively with triplicate spectra you can calculate
one error value per peak per spectrum. Both approaches have good and
bad points. The single average value for all peaks per spectrum
suffers from bias as not all peaks are affected by the noise in the
same way (bigger peaks are less affected). The single value for all
peaks for all spectra (averaging previous values), introduces a second
bias as the noise tends to slightly decrease, exponentially, across
the time points (R2 is most affected by this). Whereas the triplicate
spectra approach with one error per peak per spectrum is bias free but
suffers from being noisy. These errors are then propagated to the R1
error through Monte Carlo simulation.
So in your case you can apply a different approach. You can calculate
4 R1 values for all spins you have data for, and ignore the normal
error analysis approaches. Then you can calculate a mean and error
from those 4 values. This error cannot be averaged across all spins
though. In the previous paragraph this was possible because the error
was for a single time point in the R1 curve, whereas this error is for
the R1 value itself which will be different for each spin system. If
temperature calibration has been done correctly, then this error per
spin system should be bias free, although the error estimate will be
noisy! I hope this helps.
Regards,
Edward
P.S. Oh, I also have to warn you about loading 4 separate R1 data
sets into one analysis - you will bias model-free analysis by given
too much weight to the R1 values. And these are not the only methods
you can use for error analysis.
On 9/3/07, Sebastien Morin <sebastien.morin.1@xxxxxxxxx> wrote:
Hi !
I recorded 4 sets of R1 and would like to use them all and, so, extract
a mean value and also an associated error...
I would like to get the opinion of someone maybe more used with
statistics than me...
I thought about :
1. calculating the mean error
2. calculating the standard error (should be the best way, no)
3. calculating the standard deviation
4. extracting an error by calculating the extremes the value can reach
in every dataset based on the error of each dataset
What would the best error to use in a statistical point of view, but
also in a model-free point of view..?
Also, is there a way to use both the errors in the datasets and a error
extrated for the observed deviation of data..?
Note that the errors from each datasets were calculated directly from
the fits, here using the 'autoFit.tcl' script from NMRPipe with data
processed as Gaussian lines.
Also, in the case of duplicates or triplicates, should one use the same
approcah ?
Thanks !
Séb :)
--
______________________________________
_______________________________________________
| |
|| Sebastien Morin ||
||| Etudiant au PhD en biochimie |||
|||| Laboratoire de resonance magnetique nucleaire ||||
||||| Dr Stephane Gagne |||||
|||| CREFSIP (Universite Laval, Quebec, CANADA) ||||
||| 1-418-656-2131 #4530 |||
|| ||
|_______________________________________________|
______________________________________
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Re: Error propagation for duplicates, triplicates, quadriplicates...