R help - Sep 2012 - Maximum-likelihood fitting of a functional relationship(Ripley & Thompson) type analysis

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