[R-sig-ME] pairwise combinations of subjects

Stevens, Hank h@nk@@teven@ @end|ng |rom m|@m|oh@edu
Tue Jul 16 14:36:23 CEST 2019

Thanks David! That looks like a great solution, and a great SEM package. I
plan on spreading the word about umx.

On Tue, Jul 16, 2019 at 12:27 AM David Duffy <
David.Duffy using qimrberghofer.edu.au> wrote:

> > I sometimes encounter data that are derived from interactions between all
> > pairwise interactions of subjects (e.g., subject a vs. subject b,
> subject a
> > vs. subject c, subject b vs. subject c). The response is the result of
> the
> > interaction between subjects, and observations are likely to show
> > correlations within subject. We are interested in the relation between a
> > fixed effect predictor and the response, and not the effects of subject
> per se.
> [...]
> > This seems like a design that might be common in breeding....
> Yes, we fit this flavour of model as SEMs - for example, not exactly the
> same but just as mechanistically plausible, you have a reciprocal causative
> pairwise relationship
>     ----->
> X1      X2
>    <-----
> |           |
> v         v
> Y1      Y2
> detectable by its effects on total variance (correlated with values of X -
> so 'ware those variance stabilising transformations ;)), and distribution
> of the Y's.  The coefficients can be negative, so members of each pair rub
> each other the wrong way _OR_ if the measurement is say a rating by an
> external observer, then the ratings may be biased away from each other
> ("contrast effect"). I don't know how to do this in lme4, but on page 9 of
> https://peerj.com/preprints/3354.pdf
> you can see them fitting such a model using the R umx package.
> Cheers, David Duffy


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*Dr. Hank Stevens*
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