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 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
