This is the third time I have seen this message. I don't have an answer for you. Someone else might, but repeatedly posting won't make an answer magically appear, and if no one knows what you are talking about you won't see any response. In that case you may need to translate your references into R code yourself.

You may want to acquaint yourself with the RSiteSearch() function, the maintainer() function, and reading source code of libraries as first steps toward understanding how to implement these algorithms yourself.
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Sent from my phone. Please excuse my brevity.

Turgut Durduran wrote:

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
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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