Maik,<br>
<br>
The stdev/sqrt(n-1) will be an underestimate of the true error and even
of what your estimate *should* be, because it neglects the time
correlation in the data series. Depending on what you're doing the
correlation time can be long (for example, I have some binding affinity
calculations where the ligand can undergo large-scale changes in
orientation with a timescale on the order of 1ns). Anyway, the point
is, you really need to take this correlation time into account, because
otherwise, you *do* have a lot of samples, but they're not independent.
What you really want is to compute something like std/sqrt(n_eff) where
n_eff is the effective number of independent samples you have.<br>
<br>
Anyway, the best way to do this is to compute the effective statistical
inefficiency g from the normalized autocorrelation function. Then you
can compute stdev/sqrt((n-1)/g) or some such.<br>
<br>
Check out this recent paper from our group for some information
on this:
<a href="http://www.dillgroup.ucsf.edu/dl_papers/replica-exchange-wham.pdf">http://www.dillgroup.ucsf.edu/dl_papers/replica-exchange-wham.pdf</a>. Most
of the paper is of course not relevant to what you are doing, but see
especially section 2.4 and 5.2. I've personally been using some of the
techniques in this paper (and those it references) to do exactly the
sort of error analysis you're trying to do, in collaboration with J.
Chodera, so get back to us if you need any help or further information
after reading the relevant sections of the paper. <br>
<br>
I want to emphasize that this careful accounting of error is *very*
important. One thing I've found when doing careful free energy
calculations with good error analysis in this way is that it's hard to
get good results, and probably a lot of the published results without
careful error analysis actually have very large error which the authors
are unaware of. <br>
<br>
Thanks,<br>
David<br>
<br><br><div><span class="gmail_quote">On 1/9/06, <b class="gmail_sendername">Maik Goette</b> <<a href="mailto:mgoette@mpi-bpc.mpg.de">mgoette@mpi-bpc.mpg.de</a>> wrote:</span><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
Dear all<br><br>I was asking myself how to get an adequate error for FEP simulations.<br>g_analyze spits out two values:<br>The standard deviation (which seems to be not good for a correct error<br>estimation) and std.dev./sqrt
(n-1).<br>I now think, that the 2nd one is the method, Berk derived in his PhD<br>thesis and therefore the better one(?). But I am not really sure about this.<br>Any comment?<br><br>Thank you<br><br>--<br>Maik Goette, Dipl. Biol.
<br>Max Planck Institute for Biophysical Chemistry<br>Theoretical & computational biophysics department<br>Am Fassberg 11<br>37077 Goettingen<br>Germany<br>Tel. : ++49 551 201 2310<br>Fax : ++49 551 201 2302<br>Email : mgoette[at]mpi-
<a href="http://bpc.mpg.de">bpc.mpg.de</a><br> mgoette2[at]gwdg.de<br>WWW : <a href="http://www.mpibpc.gwdg.de/groups/grubmueller/">http://www.mpibpc.gwdg.de/groups/grubmueller/</a><br>_______________________________________________
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