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Model II regression analysis

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Hazim Markos

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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Maguin, Eugene

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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Mike

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+regression+%22major+axis%22&hl=en&ei=cpKcTYTxIsSdgQeltZiZBw&sa=X&oi=book_result&ct=result&resnum=1&ved=0CCkQ6AEwAA#v=onepage&q=%22model%20II%22%20regression%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
>
> =====================
> To manage your subscription to SPSSX-L, send a message to
> [hidden email] (not to SPSSX-L), with no body text except the
> command. To leave the list, send the command
> SIGNOFF SPSSX-L
> For a list of commands to manage subscriptions, send the command
> INFO REFCARD
>
> =====================
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=====================
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Hazim Markos

Re: Model II regression analysis

In reply to this post by Hazim Markos

Thanks for the reply, it seems that another name for this is Reduced Major
Axis regression. Which as far as I understand means that both x and y values
can vary, unlike the straightforward least square regression (Model I) where
the x values are fixed and only the y values vary. The logic of this editor
is that in physiology & pharmacology both x & y values vary; so Model II
should be used.
Hazim

=====================
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Maguin, Eugene

Re: Model II regression analysis

In reply to this post by Mike

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

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
>
> =====================
> To manage your subscription to SPSSX-L, send a message to
> [hidden email] (not to SPSSX-L), with no body text except the
> command. To leave the list, send the command
> SIGNOFF SPSSX-L
> For a list of commands to manage subscriptions, send the command
> INFO REFCARD
>
> =====================
> To manage your subscription to SPSSX-L, send a message to
> [hidden email] (not to SPSSX-L), with no body text except the
> command. To leave the list, send the command
> SIGNOFF SPSSX-L
> For a list of commands to manage subscriptions, send the command
> INFO REFCARD

Mike

Re: Model II regression analysis

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
>>
>> =====================
>> To manage your subscription to SPSSX-L, send a message to
>> [hidden email] (not to SPSSX-L), with no body text except the
>> command. To leave the list, send the command
>> SIGNOFF SPSSX-L
>> For a list of commands to manage subscriptions, send the command
>> INFO REFCARD
>>
>> =====================
>> To manage your subscription to SPSSX-L, send a message to
>> [hidden email] (not to SPSSX-L), with no body text except the
>> command. To leave the list, send the command
>> SIGNOFF SPSSX-L
>> For a list of commands to manage subscriptions, send the command
>> INFO REFCARD
>
> =====================
> To manage your subscription to SPSSX-L, send a message to
> [hidden email] (not to SPSSX-L), with no body text except the
> command. To leave the list, send the command
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Kylie

Re: Model II regression analysis

Hi everyone,

The CNLR procedure can be used to fit major axis regression and reduced major
axis regression models in SPSS. Here are example syntax programs from the
knowledgebase (#12739, #12740).

=====================
There is not a built in procedure for reduced major axis regression in SPSS, but
this model can easily be fitted using the CNLR procedure. If Y is the dependent
variable and X is the predictor or independent variable, the command syntax
would be:

MODEL PROGRAM A=1 B=1.
COMPUTE PRED=A+B*X.
COMPUTE LOSS=((Y-PRED)**2)/ABS(B).
CNLR Y /LOSS=LOSS.

Bootstrapped estimates of standard errors are also available.
=====================
There is not a built in procedure for major axis regression
in SPSS, but this model can easily be fitted using the CNLR
procedure. If Y is the dependent variable and X is the
predictor or independent variable, the command syntax would
be:

MODEL PROGRAM A=1 B=1.
COMPUTE PRED=A+B*X.
COMPUTE LOSS=((Y-PRED)**2)/(1+B**2).
CNLR Y /LOSS=LOSS.

Bootstrapped estimates of standard errors are also available.
=====================

Hope this helps.

Cheers,
Kylie.

Quoting Mike Palij <[hidden email]>:

> 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
> >>
> >> =====================
> >> To manage your subscription to SPSSX-L, send a message to
> >> [hidden email] (not to SPSSX-L), with no body text except the
> >> command. To leave the list, send the command
> >> SIGNOFF SPSSX-L
> >> For a list of commands to manage subscriptions, send the command
> >> INFO REFCARD
> >>
> >> =====================
> >> To manage your subscription to SPSSX-L, send a message to
> >> [hidden email] (not to SPSSX-L), with no body text except the
> >> command. To leave the list, send the command
> >> SIGNOFF SPSSX-L
> >> For a list of commands to manage subscriptions, send the command
> >> INFO REFCARD
> >
> > =====================
> > To manage your subscription to SPSSX-L, send a message to
> > [hidden email] (not to SPSSX-L), with no body text except the
> > command. To leave the list, send the command
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> > For a list of commands to manage subscriptions, send the command
> > INFO REFCARD
>
> =====================
> To manage your subscription to SPSSX-L, send a message to
> [hidden email] (not to SPSSX-L), with no body text except the
> command. To leave the list, send the command
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>

--
Kylie Lange
Biostatistician
Centre of Clinical Research Excellence (CCRE) in
Nutritional Physiology, Interventions and Outcomes
Discipline of Medicine
The University of Adelaide

Phone (Mon-Wed, Fri @ UniAdel): (08) 8222 5973
Phone (Thurs @ CSIRO): (08) 8303 8860
Fax: (08) 8223 3870
Web: www.adelaide.edu.au/ccre-nutrition
E-mail: [hidden email]

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Allan Reese (Cefas)

Re: Model II regression analysis

In reply to this post by Hazim Markos

The following reference by Ludbrook may be helpful and may explain why
an editor in physiology & pharmacology has picked up on this.
Clinical and Experimental Pharmacology and Physiology (2010) 37, 692-699
http://onlinelibrary.wiley.com/doi/10.1111/j.1440-1681.2010.05376.x/pdf

-----Original Message-----
From: Hazim Markos [mailto:[hidden email]]
Sent: 06 April 2011 17:46
Subject: Re: Model II regression analysis

Thanks for the reply, it seems that another name for this is Reduced
Major
Axis regression. Which as far as I understand means that both x and y
values
can vary, unlike the straightforward least square regression (Model I)
where
the x values are fixed and only the y values vary. The logic of this
editor
is that in physiology & pharmacology both x & y values vary; so Model II
should be used.
Hazim

This email and any attachments are intended for the named recipient only. Its unauthorised use, distribution, disclosure, storage or copying is not permitted. If you have received it in error, please destroy all copies and notify the sender. In messages of a non-business nature, the views and opinions expressed are the author's own and do not necessarily reflect those of Cefas. Communications on Cefas’ computer systems may be monitored and/or recorded to secure the effective operation of the system and for other lawful purposes.

Mike

Re: Model II regression analysis

Allan,

Nice article that clarifies a number of issues. If my reading of it

is correct, it seems to suggest that Model II regression would be

useful in establishing reliability of measurement under certain

conditions.

-Mike Palij
New York University

[hidden email]

----- Original Message -----

From: [hidden email]

To: [hidden email]

Sent: Thursday, April 07, 2011 5:22 AM

Subject: Re: Model II regression analysis

The following reference by Ludbrook may be helpful and may explain why
an editor in physiology & pharmacology has picked up on this.
Clinical and Experimental Pharmacology and Physiology (2010) 37, 692-699
http://onlinelibrary.wiley.com/doi/10.1111/j.1440-1681.2010.05376.x/pdf

-----Original Message-----
From: Hazim Markos [mailto:[hidden email]]
Sent: 06 April 2011 17:46
Subject: Re: Model II regression analysis

Thanks for the reply, it seems that another name for this is Reduced
Major
Axis regression. Which as far as I understand means that both x and y
values
can vary, unlike the straightforward least square regression (Model I)
where
the x values are fixed and only the y values vary. The logic of this
editor
is that in physiology & pharmacology both x & y values vary; so Model II
should be used.
Hazim

This email and any attachments are intended for the named recipient only. Its unauthorised use, distribution, disclosure, storage or copying is not permitted. If you have received it in error, please destroy all copies and notify the sender. In messages of a non-business nature, the views and opinions expressed are the author's own and do not necessarily reflect those of Cefas. Communications on Cefas’ computer systems may be monitored and/or recorded to secure the effective operation of the system and for other lawful purposes.

rityxu

Re: Model II regression analysis

In reply to this post by Hazim Markos

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kalpanaceo

Re: Model II regression analysis

In reply to this post by Hazim Markos

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Bruce Weaver

Re: Model II regression analysis

Administrator

kalpanaceo wrote

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Oh, aye? I have a couple questions:

1. What is your connection to "Informatics Outsourcing"?

2. The world is a pretty big place. How exactly was it determined that IO provides the "best Regression Analysis service Worldwide"?

--
Bruce Weaver
bweaver@lakeheadu.ca
http://sites.google.com/a/lakeheadu.ca/bweaver/

"When all else fails, RTFM."

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