[R] Maximum-likelihood fitting of a functional relationship(Ripley & Thompson) type analysis

[Turgut Durduran]

Hello all,

Based on "Ripley & Thompson, Analyst, 1987 ", I am trying to do a regression of my data which assumes a linear relationship between measurements by two modalities of the same physiological parameter. The complication is that my errors are heterogeneous, i.e. not only both X & Y variables have significant variances, their ratio and individual values differ greatly between subjects. I believe a simple linear regression (which ignores the variances) is underestimating the slope of the relationship while a method like deming regression is overestimating (or underestimating depending on what I give as the ratio) since it assumes a constant ratio of the variable. Therefore, I have concluded that I need to do the full MLFR type of analysis suggested in that paper.

Looking through archives and such, I could not find a direct implementation for R. I think a related method is that implemeted in "leiv" package which implements errors-in-variables methods.

Admittedly, I am bit lazy and I did not dig into "leiv" implementation to figure out the differences and whether giving the ratio of the standard errors of Y to those of X for each point actually is correct.


I am wondering if anyone has implemented this method in R and has an example that I can look that. 


While at it,  I am wondering what is the way to estimate the 95% confidence interval in the results both for "leiv" and "MLFR".


Thanks,

Turgut