[R] How to do linear regression with errors in x and y?

[Marc R. Feldesman]

This is referred to in *my* trade as a Model II regression and is fit by 
finding either the major axis slope or the reduced major axis slope.  We 
find the RMA slope using principal components analysis of the covariance 
matrix - the ratio of eigenvectors of x & y variables form the major axis 
slopes; we get the reduced major axis slope by dividing the linear 
regression slope by the correlation coefficient for x & y.

The original approach to this type of regression traces to at least Haldane 
and Kermack in 1950.

At 12:32 PM 6/3/00 -0300, Dan E. Kelley wrote:
 >QUESTION: how should I do a linear regression in which there are
 >errors in x as well as y?
 >

Dr. Marc R. Feldesman
email:  feldesmanm at pdx.edu
email:  feldesman at ibm.net
fax:    503-725-3905

"Don't know where I'm going.
Don't like where I've been.
There may be no exit.
But hell, I'm going in."  Jimmy Buffett

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