Evidently my previous message met some filter due to subject line. I am re-sending my message. I apologize if this was sent out twice.
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.
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".