Hello all,

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.

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".



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groupr-help @
postedSep 6, '12 at 1:16a
activeSep 7, '12 at 10:01a



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