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Dear r-help, I want to conduct nonlinear fitting to a data frame having x and y variables. Because both x and y have measurement error, I want to include error term of x variable in the model. I'm not sure but I think ordinary nls model only consider error term of y variable. How can I do this kind of nonlinear fitting in R. Is there any examples in nls package? Thanks in advance, --

I'm trying to fit a simple linear regression of just Y ~ X, but both X and Y are noisy. Thus instead of fitting a standard linear model minimizing vertical residuals, I would like to minimize orthogonal/perpendicular residuals. I have tried searching the R-packages, but have not found anything that seems suitable. I'm not sure what these types of residuals are typically called

QUESTION: how should I do a linear regression in which there are errors in x as well as y? SUPPLEMENT: I've seen folks approach this problem by computing eigenvectors of the covariance matrix, and that makes sense to me. But I'm wondering if this has a "pedigree" (i.e. if it makes sense to folks on this list, and if it's something that has been published, so I can refer to