? 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". Thanks, Turgut
Maximum likelihood fitting of a functional relationship (MLFR)
5 messages · Turgut Durduran, Jeff Newmiller, Jose Iparraguirre
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". Thanks, Turgut?
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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Turgut Durduran <durduran at yahoo.com> 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 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 at 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.
Him ?
T his 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.
Hi Jeff,
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.
As I noted in my original message, I had received a note saying my message was rejected due to the subject line that is why I altered the subject line and sent it since I thought it did not go through. I was not trying to receive an answer by repeat posts within few hours. I apologize once more. Turgut
Turgut, I'm afraid you'll have to write it by yourself. Dhanoa and Sanderson (Can J Zool, 66:821-823, 2010) mention the FREML Excel add-in written by Ripley and Thompson (available on the Royal Society of Chemistry's website at the moment of writing that paper) and that the simulation and extrapolation procedure has been implemented in R (Simex package) but they remark that "simple linear regression typically would not be a candidate for SIMEX analysis". Hope this helps, and good luck with your piece of coding! Jos? Jos? Iparraguirre Chief Economist Age UK T 020 303 31482 E Jose.Iparraguirre at ageuk.org.uk Twitter @jose.iparraguirre at ageuk Tavis House, 1- 6 Tavistock Square London, WC1H 9NB www.ageuk.org.uk?| ageukblog.org.uk | @ageukcampaigns For evidence and statistics on the older population, visit the Age UK Knowledge Hub http://www.ageuk.org.uk/professional-resources-home/knowledge-hub-evidence-statistics/ -----Original Message----- From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of Jeff Newmiller Sent: 06 September 2012 18:52 To: Turgut Durduran; Turgut Durduran; r-help at stat.math.ethz.ch Subject: Re: [R] Query on how to do maximum likelihood fitting of a functional (MLFR) 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. --------------------------------------------------------------------------- Jeff Newmiller The ..... ..... Go Live... DCN:<jdnewmil at dcn.davis.ca.us> Basics: ##.#. ##.#. Live Go... Live: OO#.. Dead: OO#.. Playing Research Engineer (Solar/Batteries O.O#. #.O#. with /Software/Embedded Controllers) .OO#. .OO#. rocks...1k --------------------------------------------------------------------------- Sent from my phone. Please excuse my brevity.
Turgut Durduran <durduran at yahoo.com> 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 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 at 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.
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