[Biodevelopers] Re: delta RMS

[J.W. Bizzaro]

And, in the structural world, root mean square distance fluctuation is given by

  deltaRMSD = sqrt[(x2 - x1)^2 + (y2 - y1)^2 + (z2 - z1)^2]

where x, y and z are 3-dimensional Cartesian coordinates, usually the position
of an atom.  This might be used to determine the difference between two protein
structures, for example.

Jeff

Allen Henry wrote:
> 
> I'm only a student in the BioInfo biz, but am very
> good at math and come from an image processing
> background where we called this the Root Mean Square
> Error (RMSE). I recognized this right away because one
> of my first 'bugs' that I fixed was in this very same
> function. I found something on Google's groups with a
> similar question as well as the equation:
> 
> http://thesaurus.maths.org/dictionary/map/word/3701
> 
> I hope this of help
> 
> :-)
> 
> =======================================================
> 
> Hi all,
> 
> Given a set of of tiepoints for images and/or maps,
> how should one go about calculating the RMSE?  This is
> with respect to surface fitting models for image
> registration
> and rectification.  Such methods include polynomials
> and piecewise linear functions.  No prior assumptions
> 'bout anything.  This, of course, rules out kriging
> for rubber-sheeting... well, not exactly, but that's a
> question for another day.
> 
> So, given that this is a matter of developing a
> deterministic model, would YOU account for degrees of
> freedom?  Two alternatives follow below for
> polynomials.
> 
> (1)
> RMS for X    = sqrt ( sum( (residual X)**2 ) / (N - K)
> )
> RMS for Y    = sqrt ( sum( (residual Y)**2 ) / (N - K)
> )
> RMS Distance = sqrt ( (RMS for X)**2 + (RMS for Y)**2
> )
> 
> (2)
> RMS for X    = sqrt ( sum( (residual X)**2 ) / N )
> RMS for Y    = sqrt ( sum( (residual Y)**2 ) / N) )
> RMS Distance = sqrt ( (RMS for X)**2 + (RMS for Y)**2
> )
> 
> where,
> 
> N is the number of GCPs and K is the number of terms
> for the specified order.
> 
> Regards.
> 
> -David
> fogel at geog.ucsb.edu
> 
> Hope this Helps
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> > Today's Topics:
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> >    1. Re: [BiO BB] delta RMS (Joseph Landman)
> >
> > --__--__--
> >
> > Message: 1
> > From: Joseph Landman
> > <landman at scalableinformatics.com>
> > To: bio_bulletin_board at bioinformatics.org
> > Cc: biodevelopers <biodevelopers at bioinformatics.org>
> > Date: 18 Aug 2002 22:08:34 -0400
> > Subject: [Biodevelopers] Re: [BiO BB] delta RMS
> > Reply-To: biodevelopers at bioinformatics.org
> >
> > Hi Pete:
> >
> >   RMS deviation (also known as standard deviation)
> > is generally well
> > defined.  Could what you have be
> >
> >       delta (RMS deviation)[i,j] = SD(i) - SD(j)
> >
> > basically using the delta as a difference operator
> > between two different
> > SD's?  This might be one of several possible
> > "signatures" that an
> > analysis would use to compare measurement
> > distributions, or set
> > thresholds for sub-sampling to help delineate
> > clusters.
> >
> >
> > On Sun, 2002-08-18 at 21:54, Peter oledzki wrote:
> > > Hello,
> > >
> > > I'm working on a project at the moment and I've
> > come
> > > across something called delta root mean squared
> > > deviation....does anybody know what this is?
> > >
> > > Could they possily try and explain it to me.....?
> > >
> > > Any help would be much appreciated.

-- 
J.W. Bizzaro                                jeff at bioinformatics.org
Director, Bioinformatics.Org        http://bioinformatics.org/~jeff
"As we enjoy great advantages from the inventions of others, we
should be glad of an opportunity to serve others by any invention
of ours; and this we should do freely and generously."
                   -- Benjamin Franklin
--