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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

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  • Turgut Durduran at Sep 6, 2012 at 4:55 pm
    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?
  • Jeff Newmiller at Sep 6, 2012 at 5:52 pm
    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@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 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.
  • Turgut Durduran at Sep 6, 2012 at 6:28 pm
    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
  • Jose Iparraguirre at Sep 7, 2012 at 10:01 am
    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
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    -----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@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 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.
    ______________________________________________
    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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