[R-sig-ME] About computing covariances between two fixed effects with 4 and 5 levels respectively.

[Thierry Onkelinx]

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 using inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be

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Op do 24 okt. 2019 om 15:05 schreef Julian Gaviria Lopez <
Julian.GaviriaLopez using 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 <- glmmTMB(Observations ~ us(CAP + 0 | Condition), data=sdf,
> ziformula=~1)
>
> I obtain the following result:
> > VarCorr(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 using unige.ch
>
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>
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