pairwise combinations of subjects
Thanks David! That looks like a great solution, and a great SEM package. I plan on spreading the word about umx. Best, Hank On Tue, Jul 16, 2019 at 12:27 AM David Duffy <
David.Duffy at 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
?Life is a garden, not a road. We enter and exit through the same gate. Wandering, where we go matters less than what we notice.? *Dr. Hank Stevens* Lab website <http://blogs.miamioh.edu/stevens-lab/> PhD Program in Ecology, Evolution, and Environmental Biology <http://www.cas.muohio.edu/eeeb/index.html> My schedule is available by adding stevenmh at miamioh.edu to your Google Calendar 433 Hughes Hall, Miami University, tel: 513-529-4206 [[alternative HTML version deleted]]