[R] Errors in Variables

John Fox jfox at mcmaster.ca
Sun May 29 23:56:10 CEST 2005 

Dear Spencer,

> -----Original Message-----
> From: Spencer Graves [mailto:spencer.graves at pdf.com] 
> Sent: Sunday, May 29, 2005 4:13 PM
> To: John Fox
> Cc: r-help at stat.math.ethz.ch; 'Jacob van Wyk'; 'Eric-Olivier Le Bigot'
> Subject: Re: [R] Errors in Variables
> 
> Hi, John:
> 
> 	  Thanks for the clarification.  I know that the 
> "errors in X problem" 
> requires additional information, most commonly one of the 
> variances or the correlation.  The question I saw (below) 
> indicated he had tried "model of the form y ~ x (with a given 
> covariance matrix ...)", which made me think of "sem".
> 
> 	  If he wants "the least (orthogonal) distance", could 
> he could get it indirectly from "sem" by calling "sem" 
> repeatedly giving, say, a variance for "x", then averaging 
> the variances of "x" and "y" and trying that in "sem"?
> 

I'm not sure how that would work, but seems similar to averaging the
regressions of y on x and x on y.

> 	  Also, what do you know about "ODRpack"?  It looks 
> like that might solve "the least (orthogonal) distance".
> 

I'm not familiar with ODRpack, but it seems to me that one could fairly
simply minimize the sum of squared least distances using, e.g., optim.

Regards,
 John

> 	  Thanks again for your note, John.
> 	  Best Wishes,
> 	  Spencer Graves	
> 
> John Fox wrote:
> 
> > Dear Spencer,
> > 
> > The reason that I didn't respond to the original posting (I'm the 
> > author of the sem package), that that without additional 
> information 
> > (such as the error variance of x), a model with error in 
> both x and y 
> > will be underidentified (unless there are multiple indicators of x, 
> > which didn't seem to be the case here). I figured that what 
> Jacob had 
> > in mind was something like minimizing the least 
> (orthogonal) distance 
> > of the points to the regression line (implying by the way 
> that x and y 
> > are on the same scale or somehow standardized), which isn't 
> doable with sem as far as I'm aware.
> > 
> > Regards,
> >  John
> > 
> > --------------------------------
> > John Fox
> > Department of Sociology
> > McMaster University
> > Hamilton, Ontario
> > Canada L8S 4M4
> > 905-525-9140x23604
> > http://socserv.mcmaster.ca/jfox
> > --------------------------------
> > 
> > 
> >>-----Original Message-----
> >>From: r-help-bounces at stat.math.ethz.ch 
> >>[mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of 
> Spencer Graves
> >>Sent: Saturday, May 28, 2005 4:47 PM
> >>To: Eric-Olivier Le Bigot
> >>Cc: r-help at stat.math.ethz.ch; Jacob van Wyk
> >>Subject: Re: [R] Errors in Variables
> >>
> >>	  I'm sorry, I have not followed this thread, but I 
> wonder if you 
> >>have considered library(sem), "structural equations modeling"?  
> >>"Errors in variables" problems are the canonical special case.
> >>
> >>	  Also, have you done a search of "www.r-project.org" 
> >>-> search -> "R site search" for terms like "errors in
> >>variables regression"?  This just led me to "ODRpack", 
> which is NOT a 
> >>CRAN package but is apparently available after a Google 
> search.  If it 
> >>were my problem, I'd first try to figure out "sem";  if that seemed 
> >>too difficult, I might then look at "ODRpack".
> >>
> >>	  Also, have you read the posting guide! 
> >>http://www.R-project.org/posting-guide.html?  This suggests, among 
> >>other things, that you provide a toy example that a potential 
> >>respondant could easily copy from your email, test a few 
> >>modifications, and prase a reply in a minute or so.
> >>This also helps clarify your question so any respondants are more 
> >>likely to suggest something that is actually useful to you. 
>  Moreover, 
> >>many people have reported that they were able to answer their own 
> >>question in the course of preparing a question for this 
> list using the 
> >>posting guide.
> >>
> >>	  hope this helps.  spencer graves
> >>
> >>Eric-Olivier Le Bigot wrote:
> >>
> >>
> >>>I'm interested in this "2D line fitting" too!  I've been looking, 
> >>>without success, in the list of R packages.
> >>>
> >>>It might be possible to implement quite easily some of the
> >>
> >>formalism
> >>
> >>>that you can find in Numerical Recipes (Fortran 77, 2nd ed.), 
> >>>paragraph 15.3.  As a matter of fact, I did this in R but
> >>
> >>only for a
> >>
> >>>model of the form y ~ x (with a given covariance matrix
> >>
> >>between x and
> >>
> >>>y).  I can send you the R code (preliminary version: I
> >>
> >>wrote it yesterday), if you want.
> >>
> >>>Another interesting reference might be Am. J. Phys. 60, p. 
> >>
> >>66 (1992).  
> >>
> >>>But, again, you would have to implement things by yourself.
> >>>
> >>>All the best,
> >>>
> >>>EOL
> >>>
> >>>-- 
> >>>Dr. Eric-Olivier LE BIGOT (EOL)                CNRS 
> >>
> >>Associate Researcher
> >>
> >>~~~o~o~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
> >>~~~~o~o~~~
> >>
> >>>Kastler Brossel Laboratory (LKB)                   
> >>
> >>http://www.lkb.ens.fr
> >>
> >>>Université P. & M. Curie and Ecole Normale Supérieure, Case 74
> >>>4 place Jussieu              75252 Paris CEDEX 05           
> >>
> >>      France
> >>
> >>~~~o~o~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
> >>~~~~o~o~~~
> >>
> >>>office  : 01 44 27 73 67                             fax: 
> >>
> >>01 44 27 38 45
> >>
> >>>ECR room: 01 44 27 47 12                      x-ray room: 
> >>
> >>01 44 27 63 00
> >>
> >>>home: 01 73 74 61 87      For int'l calls: 33 + number 
> >>
> >>without leading 0
> >>
> >>>
> >>>On Wed, 25 May 2005, Jacob van Wyk wrote:
> >>>
> >>>
> >>>>I hope somebody can help.
> >>>>A student of mine is doing a study on Measurement Error models 
> >>>>(errors-in-variables, total least squares, etc.). I have an old 
> >>>>reference to a "multi archive"  that contains
> >>>>leiv3: Programs for best line fitting with errors in both
> >>
> >>coordinates.
> >>
> >>>>(The date is October 1989, by B.D. Ripley et al.) I have done a 
> >>>>search for something similar in R withour success. Has this been 
> >>>>implemented in a R-package, possibly under some sort of
> >>
> >>assumptions
> >>
> >>>>about variances. I would lke my student to apply some regression 
> >>>>techniques to data that fit this profile.
> >>>>Any help is much appreciated.
> >>>>(If I have not done my search more carefully - my
> >>
> >>apologies.) Thanks
> >>
> >>>>Jacob
> >>>>
> >>>>
> >>>>Jacob L van Wyk
> >>>>Department of Mathematics and Statistics University of
> >>
> >>Johannesburg
> >>
> >>>>APK P O Box 524 Auckland Park 2006 South Africa
> >>>>Tel: +27-11-489-3080
> >>>>Fax: +27-11-489-2832
> >>>>
> >>>>______________________________________________
> >>>>R-help at stat.math.ethz.ch mailing list 
> >>>>https://stat.ethz.ch/mailman/listinfo/r-help
> >>>>PLEASE do read the posting guide! 
> >>>>http://www.R-project.org/posting-guide.html
> >>>>
> >>>
> >>>
> >>------------------------------------------------------------
> ----------
> >>
> >>>--
> >>>
> >>>______________________________________________
> >>>R-help at stat.math.ethz.ch mailing list 
> >>>https://stat.ethz.ch/mailman/listinfo/r-help
> >>>PLEASE do read the posting guide! 
> >>>http://www.R-project.org/posting-guide.html
> >>
> >>______________________________________________
> >>R-help at stat.math.ethz.ch mailing list
> >>https://stat.ethz.ch/mailman/listinfo/r-help
> >>PLEASE do read the posting guide! 
> >>http://www.R-project.org/posting-guide.html
> > 
> >

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