Re: Model II regression analysis

Posted by Mike on Apr 06, 2011; 7:02pm
URL: http://spssx-discussion.165.s1.nabble.com/Model-II-regression-analysis-tp4286435p4287034.html

If the question is directed to me, I would have to say that I don't
really know all that much about Model II regression (I'm more of
a Model I kind of guy).  However, there are some useful notes
by Jack Weiss at UNC for one of his course that clarify the different
types of models for error in variables in regression analysis; see:
http://www.unc.edu/courses/2007spring/biol/145/001/docs/lectures/Nov3.html#maxlikelihoodform
I assume that the OP's comment of reporting "ordinary least-products"
may have something to do with "Alternative 3" which minimizes the
product of the error in x times the error in y (also called geometric mean
or reduced major axis regression).

One of the key distinctions, the ratio of error in Y relative to error in X,
is explored in more detail in another lecture; see:
http://www.unc.edu/courses/2007spring/biol/145/001/docs/lectures/Nov10.html

I presume that SEM can be used to estimate the parameters in the
equation but it also seems likely to me that this type of analysis might
be done by specialized software.

I will now defer to someone who actually is familiar with this stuff.

-Mike Palij
New York University
[hidden email]


----- Original Message -----
From: "Gene Maguin" <[hidden email]>
To: <[hidden email]>
Sent: Wednesday, April 06, 2011 2:10 PM
Subject: Re: Model II regression analysis

> Let me see if in can learn something here from this. I looked at the page on
> the Monterey Bay website that you cited. From that description, it sounds as
> though Model II regression is concerned with predictor variables measured
> with error (which, I think, is referred to as the 'errors in variables'
> problem). Error that could, perhaps, be estimated by specific procedures and
> estimates of that error then used to correct a correlation/covariance matrix
> in an SEM analysis. Would that be a correct line of thinking?
>
> Gene
>
>
>
>
>
>
> -----Original Message-----
> From: Mike Palij [mailto:[hidden email]]
> Sent: Wednesday, April 06, 2011 12:30 PM
> To: Gene Maguin; [hidden email]
> Cc: Mike Palij
> Subject: Re: Model II regression analysis
>
> I'm not speaking for Hazim but "Model II" regression is a specialized
> type of regression analysis.  Sokal and Rohlf present and contrast
> Model I (ordinary regression for prediction) and Model II (major axis
> regression) in their Biometry textbook, sections of which are available
> at books.google.com; see:
> http://tinyurl.com/sokal-rohlf
> or
> http://books.google.com/books?id=N6KCNw5NHNkC&pg=PA543&dq=%22model+II%22+reg
> ression+%22major+axis%22&hl=en&ei=cpKcTYTxIsSdgQeltZiZBw&sa=X&oi=book_result
> &ct=result&resnum=1&ved=0CCkQ6AEwAA#v=onepage&q=%22model%20II%22%20regressio
> n%20%22major%20axis%22&f=false
>
> A brief historical presentation on the distinction is also available here:
> http://www.mbari.org/staff/etp3/regress/history.htm
>
> There are several programs and routines that use different methods
> for Model II regression (stand alone and programs for MatLab and R)
> but I haven't seen "ordinary least products" used.
>
> -Mike Palij
> New York University
> [hidden email]
>
>
> ----- Original Message -----
> From: "Gene Maguin" <[hidden email]>
> To: <[hidden email]>
> Sent: Wednesday, April 06, 2011 11:58 AM
> Subject: Re: Model II regression analysis
>
>
>> Hazim,
>>
>> I'm sure others on the list will know but what specifically is the
>> design/meaning of an "ordinary least products"/Model II regression?
>>
>> Gene Maguin
>>
>>
>>
>> -----Original Message-----
>> From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of
>> Hazim Markos
>> Sent: Wednesday, April 06, 2011 10:43 AM
>> To: [hidden email]
>> Subject: Model II regression analysis
>>
>> Hi,
>> I'm a physiologist so here comes a cry for help. I just got a paper back
>> from a journal editor who prefers I use "ordinary least products"
> regression
>> analysis, which I've since found out is also called Model II regression
>> analysis (right?). I dont want to argue with him, so I've agreed to do
> this.
>>
>> Long story short: how do I do this type of regression in SPSS v.17???
>>
>> Thanks
>> Hazim
>>
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