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List: r-sig-mixed-models
Subject: Re: [R-sig-ME] About computing covariances between two fixed effects
From: Julian Gaviria Lopez <Julian.GaviriaLopez () unige ! ch>
Date: 2019-10-28 13:22:42
Message-ID: e363de2b3e074735aff032462b058240 () unige ! ch
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Dear Thierry,
Thank you so much for your comment. In fact, it was the first step of the analysis:
Mol<- glmmTMB(Observations ~ CAP * Condition + (1|ID), data=mDATA, ziformula=~ 1, \
family=nbinom1)
I aimed to investigate the differences between the CAPs (CAP: c1, c2, c3, c4), \
across 5 conditions (Condition: base, neu, pneu, aff, paff). For this purpose, \
implemented the Anova function (glmmTMB), and the emmeans package for assessing the \
interactions.
Another question to answer is how related (i.e., Pearson coefficients for normally \
distributed and independent data) are those CAPs to each other, across conditions \
(Condition: neu, pneu, aff, paff), being from the same individuals (N=20). In the \
previous email, you were right when pointing my confusion between fixed and random \
effects. So, I went on solving my problem, and I found two alternative solutions:
1) Taking the previous model:
Mol<- glmmTMB(Observations ~ CAP * Condition + (1|ID), data=mDATA, ziformula=~ 1, \
family=nbinom1)
We can compute the corr/cov
(vcov(Mol)$cond)
I assume that the output covariance structure is autoregresive (AR1).(Question aside: \
Is there any way to change the structure when using his function?)
2) Following the previously cited vignette:
https://cran.r-project.org/web/packages/glmmTMB/vignettes/covstruct.html
I found the following alternative:
fit.us <- glmmTMB(Observations ~ us(CAP * Condition + 0 | group), data=mDATA)
Where the"group "variable" is just a 1 in every row of the data, and "us" corresponds \
to the"heterogeneous unstructured" covariance structure.
Regardless of the method, I obtain a correlation values (-1 to 1), of the covariances \
of the random effect (ID). Therefore, my questions to solve are: 1) Would it be right \
to interpret the output "correlation" values as the evaluation of the relationship \
between the factors "CAP" and "Condition" (fixed effects), based on the number of \
counts reported in "Observations" (random effect)?
2) I do not manage to obtain the cov/corr values from the intercept. For instance, if \
the intercept values corresponds to"CAP:c1, Condition: base" how can I obtain the \
corr/cov values corresponding to the regressors from itself? e.g.:
CAP:c1, Condition: base" - CAP:c1, Condition: neu"
CAP:c1, Condition: base" - CAP:c1, Condition: pneu"
CAP:c1, Condition: base" - CAP:c1, Condition: aff"
CAP:c1, Condition: base" - CAP:c1, Condition: paff"
Thank you so much in advance for any comment.
Julian Gaviria
Neurology and Imaging of cognition lab (Labnic)
University of Geneva. Campus Biotech.
9 Chemin des Mines, 1202 Geneva, CH
Tel: +41 22 379 0380
Email: Julian.GaviriaLopez@unige.ch
________________________________
From: Thierry Onkelinx <thierry.onkelinx@inbo.be>
Sent: Friday, October 25, 2019 10:30:47 AM
To: Julian Gaviria Lopez
Cc: r-sig-mixed-models@r-project.org
Subject: Re: [R-sig-ME] About computing covariances between two fixed effects with 4 \
and 5 levels respectively.
Dear Julian,
The described covariance structures relate to a _random_ effect. You are looking for \
_fixed_ effect covariances.
You are probably looking for a model like glmmTMB(Observations ~ CAP * Condition + \
(1|ID), data=sdf, ziformula=~1)
I'd also recommend to contact a local statistician about your problem.
Best regards,
ir. Thierry Onkelinx
Statisticus / Statistician
Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx@inbo.be<mailto:thierry.onkelinx@inbo.be>
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be<http://www.inbo.be>
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died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger \
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Op do 24 okt. 2019 om 15:05 schreef Julian Gaviria Lopez \
<Julian.GaviriaLopez@unige.ch<mailto:Julian.GaviriaLopez@unige.ch>>: Hello,
I want to assess the correlation of 4 kinds of brain activation patterns (CAP: c1, \
c2, c3, c4) from 20 subjects, across 5 different conditions (Condition: base, neu, \
pneu, aff, paff). In total, the count data contains 380 observations, and has the \
next structure:
ID Observations CAP Condition
1 6 c1 base
... ... ... ...
20 0 c1 base
... ... ... ...
1 3 c4 base
... ... ... ...
20 0 c4 base
1 4 c1 neu
... ... ... ...
20 2 c1 neu
... ... ... ...
1 0 c4 neu
... ... ... ...
20 5 c4 neu
... ... ... ...
20 0 c4 paff
I am trying to compute the covariance structures proposed by Kasper Kristensen:
https://cran.r-project.org/web/packages/glmmTMB/vignettes/covstruct.html
When I compute the unstructured covariance:
> fit.us<http://fit.us> <- glmmTMB(Observations ~ us(CAP + 0 | Condition), data=sdf, \
> ziformula=~1)
I obtain the following result:
> VarCorr(fit.us<http://fit.us>)
Conditional model:
Groups Name Std.Dev. Corr
Condition c1 0.86527
c2 0.34487 0.116
c3 0.16450 -0.951 0.164
c4 0.36269 0.414 -0.719 -0.545
Residual 1.98011
As you might appreciate, the results are either wrong or uncompleted, since the right \
output would yield a 5x4 cov matrix, expressing the correlation of the CAPs (c1, c2, \
c3, c4) across all the conditions (base, neu, pneu, aff, paff). One rapid solution is \
to compute the cov matrix per condition. However, apart of being penalized by model \
deficiency (I guess), the problem is still present, since the question to answer is \
how the brain activation patterns (CAP) are correlated across all conditions (e.g. \
correlation between "CAP c1 - Condition aff", and "CAP c4 - Condition paff").
Thanks in advance for any comment on this regard.
Best,
Julian Gaviria
Neurology and Imaging of cognition lab (Labnic)
University of Geneva. Campus Biotech.
9 Chemin des Mines, 1202 Geneva, CH
Tel: +41 22 379 0380
Email: Julian.GaviriaLopez@unige.ch<mailto:Julian.GaviriaLopez@unige.ch>
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