Errors in Variables

"John Fox" <jfox at mcmaster.ca> 5/29/2005 5:56:10 PM >>>

-----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"?

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

	  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! 

 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

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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

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