[R-sig-ME] mixed effects models and multiple explanatory variables that, are correlated
Highland Statistics Ltd
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Fri Nov 23 12:13:54 CET 2012
On 23/11/2012 11:00, r-sig-mixed-models-request at r-project.org wrote:
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> Today's Topics:
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> 1. mixed effects models and multiple explanatory variables that
> are correlated (Laurence O'Dwyer)
> 2. mixed effects models and multiple explanatory variables that
> are correlated (Laurence O'Dwyer)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Fri, 23 Nov 2012 10:11:12 +0100
> From: "Laurence O'Dwyer" <larodwyer at gmail.com>
> To: r-sig-mixed-models at r-project.org
> Subject: [R-sig-ME] mixed effects models and multiple explanatory
> variables that are correlated
> Message-ID:
> <CACp2W9Azk5iGSSvUs1zN+p3aDw46aqKNY=ACM6gMrosaBJCn4A at mail.gmail.com>
> Content-Type: text/plain
>
> Hello to mixed-effects model experts,
>
> I am currently
> trying to run an analysis on structural MRI data and would like to use
> glmer or MCMCglmm to model my data. I have basic statistical knowledge and
> would appreciate any guidance in the use of these R-tools from experts in
> mixed effects models.
>
> In a crude way, I am interested in a model that might look
> something like the following:
>
>
>
> moo = MCMCglmm(autism_spectrum_scores ~ Diagnosis + Striatum + Thalamus +
> Amygdala + Hippocampus,
>
> random = ~ADHD_symptom_scores
>
> + age
>
> + scanner_type
>
> + gender
>
> + total_brain_volume,
>
> data=dat)
>
>
Are you using gender as random effect?
> It is a study of ADHD and autism. I have data for ~170 children with ADHD,
> ~70 unaffected siblings, and ~80 controls - this is the fixed factor
> "Diagnosis".
>
> I have the volumes of particular structures in the brain. These are the
> fixed factors Striatum, Thalamus, etc. I am interested to know their
> relationship with a scale of autistic traits (NOT ADHD traits) within all
> experimental groups. For example, smaller volumes in the Striatum may be
> associated with increased autistic traits.
>
> For the random effects, I want to control for differences in ADHD symptoms,
> age, scanner type (two different scanners were used to collect the
> volumetric data), gender and total brain volume.
Yes you do. That is not a good idea. You may want to read a little bit
on mixed modelling before doing this. Your model is overly complicated
for 170 observations. I actually wonder whether this is mixed effects
modelling; do you have multiple observations per child? If not...then it
seems ordinary linear regression?
> A key point of the analysis would be to establish the relationship between
> structural volumes and autistic scores, when levels of ADHD have been
> controlled for.
>
> One problem is that all the structural volumes are closely correlated.
> Previously, when working with two structural volumes that were correlated,
> I used the regression residuals of one structural volume relative to the
> other to isolate the unique contribution of each explanatory variable,
> independent from what was shared between them. But, I don't think I can use
> this approach with four structures that are highly correlated.
>
> There are probably many other statistical flies in the
> ointment relating to the above. If anyone has any pointers as to how to
> deal with the situation when multiple explanatory variables are correlated,
dump some of them...after making scatterplots, and calculate VIF values.
Or use them, and accept that SEs will be blown up.
Kind regards,
Alain
> in a mixed-effects models framework, they would be appreciated.
>
> Thanks; Larry
>
> [[alternative HTML version deleted]]
>
>
>
> ------------------------------
>
> Message: 2
> Date: Fri, 23 Nov 2012 10:31:27 +0100
> From: "Laurence O'Dwyer" <larodwyer at gmail.com>
> To: r-sig-mixed-models at r-project.org
> Subject: [R-sig-ME] mixed effects models and multiple explanatory
> variables that are correlated
> Message-ID:
> <CACp2W9DxZMG5frP3gRcXJ4svsMWSiYP=Ox2EM3kpAWc0t61XVA at mail.gmail.com>
> Content-Type: text/plain; charset=ISO-8859-1
>
> Hello to mixed-effects model experts,
>
> I am currently
> trying to run an analysis on structural MRI data and would like to use
> glmer or MCMCglmm to model my data. I have basic statistical knowledge and
> would appreciate any guidance in the use of these R-tools from experts in
> mixed effects models.
>
> In a crude way, I am interested in a model that might look
> something like the following:
>
>
>
> moo = MCMCglmm(autism_spectrum_scores ~ Diagnosis + Striatum + Thalamus +
> Amygdala + Hippocampus,
>
> random = ~ADHD_symptom_scores
>
> + age
>
> + scanner_type
>
> + gender
>
> + total_brain_volume,
>
> data=dat)
>
>
>
> It is a study of ADHD and autism. I have data for ~170 children with ADHD,
> ~70 unaffected siblings, and ~80 controls - this is the fixed factor
> "Diagnosis".
>
> I have the volumes of particular structures in the brain. These are the
> fixed factors Striatum, Thalamus, etc. I am interested to know their
> relationship with a scale of autistic traits (NOT ADHD traits) within all
> experimental groups. For example, smaller volumes in the Striatum may be
> associated with increased autistic traits.
>
> For the random effects, I want to control for differences in ADHD symptoms,
> age, scanner type (two different scanners were used to collect the
> volumetric data), gender and total brain volume.
>
> A key point of the analysis would be to establish the relationship between
> structural volumes and autistic scores, when levels of ADHD have been
> controlled for.
>
> One problem is that all the structural volumes are closely correlated.
> Previously, when working with two structural volumes that were correlated,
> I used the regression residuals of one structural volume relative to the
> other to isolate the unique contribution of each explanatory variable,
> independent from what was shared between them. But, I don't think I can use
> this approach with four structures that are highly correlated.
>
> There are probably many other statistical flies in the
> ointment relating to the above. If anyone has any pointers as to how to
> deal with the situation when multiple explanatory variables are correlated,
> in a mixed-effects models framework, they would be appreciated.
>
> Thanks; Larry
>
>
>
> ------------------------------
>
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> End of R-sig-mixed-models Digest, Vol 71, Issue 33
> **************************************************
>
--
Dr. Alain F. Zuur
First author of:
1. Analysing Ecological Data (2007).
Zuur, AF, Ieno, EN and Smith, GM. Springer. 680 p.
URL: www.springer.com/0-387-45967-7
2. Mixed effects models and extensions in ecology with R. (2009).
Zuur, AF, Ieno, EN, Walker, N, Saveliev, AA, and Smith, GM. Springer.
http://www.springer.com/life+sci/ecology/book/978-0-387-87457-9
3. A Beginner's Guide to R (2009).
Zuur, AF, Ieno, EN, Meesters, EHWG. Springer
http://www.springer.com/statistics/computational/book/978-0-387-93836-3
4. Zero Inflated Models and Generalized Linear Mixed Models with R. (2012) Zuur, Saveliev, Ieno.
http://www.highstat.com/book4.htm
Other books: http://www.highstat.com/books.htm
Statistical consultancy, courses, data analysis and software
Highland Statistics Ltd.
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Tel: 0044 1358 788177
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