[RASMB] difference of p = 0.95, 0.68 and 0.55, the confidence level in the sedfit c(s) distribution

[Jacob Lebowitz]

Since Peter is on a long vacation, I will attempt to answer your 
question.  You can integrate the peaks by pressing the ctrl and I keys 
simultaneously which will give you a dialog box that states that you should 
hold the right mouse button down and draw a rectangle to cover the s range 
to be integrated. You will see once you do the integration you will obtain 
both the % of the loading signal in the integration range and the weight 
average s value. Also the results box states that this integration it is 
best done without regularization, confidence level of zero. Regularization 
gives the most parsimonious distribution for  the confidence level that you 
set. Total removal of regularization may give you too many peaks that will 
merge at higher confidence levels. You can still integrate over multiple 
peaks in the s range you have selected and compare the result with 
integration of the distribution you obtain at higher confidence levels. In 
my experience the integration results over the same s range are comparable 
from no regularization to using settings of p = 0.68 to 0.95.  At the 
latter p selections you have the more realistic description of the 
sedimenting species. Hope that the above is clear.

Jack Lebowitz


At 10:29 PM 7/12/2004 +0800, medakachou wrote:
>Dear all,
>
>Recently, I'm analyzing the sedimentation velocity spectra by continuous 
>c(s) distribution (SEDFIT). I've analyzed my data in three kind of 
>confidence level: p = 0.95, 0.68 and 0.55 and the regularization method is 
>maximum entropy. The s limit is 0.1 to 25S. I found every species is not 
>well seperated (they just fuse together) in p = 0.95. In p = 0.68, the 
>situation is better and the peaks are more significant. p= 0.55 can give 
>me the highest resolution and every peak is very clear cut. Now the 
>question is: if I want to calculate the area of peaks by Origin peak 
>fitting module, which results should I use? I've check Schuck's paper and 
>he suggests using p = 0.68 to 0.95 is enough. How about 0.55? I appreciate 
>your response and suggestion.
>
>Sincerely,
>
>
>Chi-Yuan Chou
>PhD student, the Institutes of Life sciences, National Defense Medical 
>Center, Taipei, Taiwan
>e-mail: <mailto:r6243023 at yahoo.com.tw>r6243023 at yahoo.com.tw
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